-
-## Step 7. Add 3 nodes on "cloud 2"
-
-At this point, you're running three nodes on cloud 1. But what if you'd like to start experimenting with resources provided by another cloud vendor? Let's try that by adding three more nodes to a new cloud platform. Again, the flag to note is [`--locality`]({% link {{ page.version.version }}/configure-replication-zones.md %}#descriptive-attributes-assigned-to-nodes), which you're using to specify that these next 3 nodes are running on cloud 2.
-
-In a new terminal, start node 4 on cloud 2:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ cockroach start \
---insecure \
---locality=cloud=2 \
---store=cloud2node4 \
---listen-addr=localhost:26260 \
---http-addr=localhost:8083 \
---cache=100MB \
---join=localhost:26257,localhost:26258,localhost:26259
-~~~
-
-In a new terminal, start node 5 on cloud 2:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ cockroach start \
---insecure \
---locality=cloud=2 \
---store=cloud2node5 \
---advertise-addr=localhost:26261 \
---http-addr=localhost:8084 \
---cache=100MB \
---join=localhost:26257,localhost:26258,localhost:26259
-~~~
-
-In a new terminal, start node 6 on cloud 2:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ cockroach start \
---insecure \
---locality=cloud=2 \
---store=cloud2node6 \
---advertise-addr=localhost:26262 \
---http-addr=localhost:8085 \
---cache=100MB \
---join=localhost:26257,localhost:26258,localhost:26259
-~~~
-
-## Step 8. Watch data balance across all 6 nodes
-
-Back on the **Overview** dashboard in DB Console, hover over the **Replicas per Node** graph again. Because you used [`--locality`]({% link {{ page.version.version }}/configure-replication-zones.md %}#descriptive-attributes-assigned-to-nodes) to specify that nodes are running on 2 clouds, you'll see an approximately even number of replicas on each node, indicating that CockroachDB has automatically rebalanced replicas across both simulated clouds:
-
-
-
-Note that it takes a few minutes for the DB Console to show accurate per-node replica counts on hover. This is why the new nodes in the screenshot above show 0 replicas. However, the graph lines are accurate, and you can click **View node list** in the **Summary** area for accurate per-node replica counts as well.
-
-## Step 9. Migrate all data to "cloud 2"
-
-So your cluster is replicating across two simulated clouds. But let's say that after experimentation, you're happy with cloud vendor 2, and you decide that you'd like to move everything there. Can you do that without interruption to your live client traffic? Yes, and it's as simple as running a single command to add a [hard constraint]({% link {{ page.version.version }}/configure-replication-zones.md %}#replication-constraints) that all replicas must be on nodes with `--locality=cloud=2`.
-
-In a new terminal, [edit the default replication zone]({% link {{ page.version.version }}/alter-range.md %}#configure-zone):
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ cockroach sql --execute="ALTER RANGE default CONFIGURE ZONE USING constraints='[+cloud=2]';" --insecure --host=localhost:26257
-~~~
-
-## Step 10. Verify the data migration
-
-Back on the **Overview** dashboard in the DB Console, hover over the **Replicas per Node** graph again. Very soon, you'll see the replica count double on nodes 4, 5, and 6 and drop to 0 on nodes 1, 2, and 3:
-
-
-
-This indicates that all data has been migrated from cloud 1 to cloud 2. In a real cloud migration scenario, at this point you would update the load balancer to point to the nodes on cloud 2 and then stop the nodes on cloud 1. But for the purpose of this local simulation, there's no need to do that.
-
-## Step 11. Stop the cluster
-
-Once you're done with your cluster, stop YCSB by switching into its terminal and pressing **CTRL-C**. Then do the same for HAProxy and each CockroachDB node.
-
-{{site.data.alerts.callout_success}}For the last node, the shutdown process will take longer (about a minute) and will eventually force stop the node. This is because, with only 1 node still online, a majority of replicas are no longer available (2 of 3), and so the cluster is not operational. To speed up the process, press CTRL-C a second time.{{site.data.alerts.end}}
-
-If you do not plan to restart the cluster, you may want to remove the nodes' data stores and the HAProxy config file:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ rm -rf cloud1node1 cloud1node2 cloud1node3 cloud2node4 cloud2node5 cloud2node6 haproxy.cfg
-~~~
-
-## What's next?
-
-Explore other CockroachDB benefits and features:
-
-{% include {{ page.version.version }}/misc/explore-benefits-see-also.md %}
-
-You may also want to learn other ways to control the location and number of replicas in a cluster:
-
-- [Even Replication Across Datacenters]({% link {{ page.version.version }}/configure-replication-zones.md %}#even-replication-across-availability-zones)
-- [Multiple Applications Writing to Different Databases]({% link {{ page.version.version }}/configure-replication-zones.md %}#multiple-applications-writing-to-different-databases)
-- [Stricter Replication for a Table and Its Indexes]({% link {{ page.version.version }}/configure-replication-zones.md %}#stricter-replication-for-a-table-and-its-secondary-indexes)
-- [Tweaking the Replication of System Ranges]({% link {{ page.version.version }}/configure-replication-zones.md %}#tweaking-the-replication-of-system-ranges)
diff --git a/src/current/v26.1/demo-json-support.md b/src/current/v26.1/demo-json-support.md
deleted file mode 100644
index 9678a98a162..00000000000
--- a/src/current/v26.1/demo-json-support.md
+++ /dev/null
@@ -1,291 +0,0 @@
----
-title: JSON Support
-summary: Use a local cluster to explore how CockroachDB can store and query unstructured JSONB data.
-toc: true
-keywords: gin, gin index, gin indexes, inverted index, inverted indexes, accelerated index, accelerated indexes
-docs_area: deploy
----
-
-This page guides you through a simple demonstration of how CockroachDB can store and query unstructured [`JSONB`]({% link {{ page.version.version }}/jsonb.md %}) data from a third-party API, as well as how a [GIN index]({% link {{ page.version.version }}/inverted-indexes.md %}) can optimize your queries.
-
-
-
-## See also
-
-- [Install CockroachDB]({% link {{ page.version.version }}/install-cockroachdb.md %})
-- [Spatial Data Overview]({% link {{ page.version.version }}/spatial-data-overview.md %})
-- [Spatial Indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
-- [Spatial & GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %})
-- [Migrate from GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %})
-- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
-- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
-- [Spatial Functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
-- [POINT]({% link {{ page.version.version }}/point.md %})
-- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
-- [POLYGON]({% link {{ page.version.version }}/polygon.md %})
-- [MULTIPOINT]({% link {{ page.version.version }}/multipoint.md %})
-- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
-- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
-- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Well Known Text]({% link {{ page.version.version }}/well-known-text.md %})
-- [Well Known Binary]({% link {{ page.version.version }}/well-known-binary.md %})
-- [GeoJSON]({% link {{ page.version.version }}/geojson.md %})
-- [SRID 4326 - Longitude and Latitude]({% link {{ page.version.version }}/srid-4326.md %})
-- [`ST_Contains`]({% link {{ page.version.version }}/st_contains.md %})
-- [`ST_ConvexHull`]({% link {{ page.version.version }}/st_convexhull.md %})
-- [`ST_CoveredBy`]({% link {{ page.version.version }}/st_coveredby.md %})
-- [`ST_Covers`]({% link {{ page.version.version }}/st_covers.md %})
-- [`ST_Disjoint`]({% link {{ page.version.version }}/st_disjoint.md %})
-- [`ST_Equals`]({% link {{ page.version.version }}/st_equals.md %})
-- [`ST_Intersects`]({% link {{ page.version.version }}/st_intersects.md %})
-- [`ST_Overlaps`]({% link {{ page.version.version }}/st_overlaps.md %})
-- [`ST_Touches`]({% link {{ page.version.version }}/st_touches.md %})
-- [`ST_Union`]({% link {{ page.version.version }}/st_union.md %})
-- [`ST_Within`]({% link {{ page.version.version }}/st_within.md %})
-- [Troubleshooting Overview]({% link {{ page.version.version }}/troubleshooting-overview.md %})
-- [Support Resources]({% link {{ page.version.version }}/support-resources.md %})
diff --git a/src/current/v26.1/indexes.md b/src/current/v26.1/indexes.md
index 2b6a892aa9b..05591f8586f 100644
--- a/src/current/v26.1/indexes.md
+++ b/src/current/v26.1/indexes.md
@@ -18,7 +18,7 @@ For example, if you index an `INT` column and then filter it `WHERE JSONB column, see the JSON tutorial.{{site.data.alerts.end}}
-
## How do GIN indexes work?
Standard [indexes]({% link {{ page.version.version }}/indexes.md %}) work well for searches based on prefixes of sorted data. However, data types like [`JSONB`]({% link {{ page.version.version }}/jsonb.md %}) or [arrays]({% link {{ page.version.version }}/array.md %}) cannot be queried without a full table scan, since they do not adhere to ordinary value prefix comparison operators. `JSONB` in particular needs to be indexed in a more detailed way than what a standard index provides. This is where GIN indexes prove useful.
diff --git a/src/current/v26.1/jsonb.md b/src/current/v26.1/jsonb.md
index fccf81617e3..3ac6b214271 100644
--- a/src/current/v26.1/jsonb.md
+++ b/src/current/v26.1/jsonb.md
@@ -8,8 +8,6 @@ docs_area: reference.sql
The `JSONB` [data type]({% link {{ page.version.version }}/data-types.md %}) stores JSON (JavaScript Object Notation) data as a binary representation of the `JSONB` value, which eliminates whitespace, duplicate keys, and key ordering. `JSONB` supports [GIN indexes]({% link {{ page.version.version }}/inverted-indexes.md %}).
-{{site.data.alerts.callout_success}}For a hands-on demonstration of storing and querying JSON data from a third-party API, see the JSON tutorial.{{site.data.alerts.end}}
-
## Alias
In CockroachDB, `JSON` is an alias for `JSONB`.
@@ -489,7 +487,6 @@ INSERT 1
## See also
-- [JSON tutorial]({% link {{ page.version.version }}/demo-json-support.md %})
- [GIN Indexes]({% link {{ page.version.version }}/inverted-indexes.md %})
- [Use an expression to index a field in a JSONB column]({% link {{ page.version.version }}/expression-indexes.md %}#use-an-expression-to-index-a-field-in-a-jsonb-column)
- [Computed Columns]({% link {{ page.version.version }}/computed-columns.md %})
diff --git a/src/current/v26.1/linestring.md b/src/current/v26.1/linestring.md
index 06d228af25e..1da1ffc4dd7 100644
--- a/src/current/v26.1/linestring.md
+++ b/src/current/v26.1/linestring.md
@@ -31,7 +31,6 @@ You can also make a LineString using the [aggregate function form]({% link {{ pa
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [POINT]({% link {{ page.version.version }}/point.md %})
- [POLYGON]({% link {{ page.version.version }}/polygon.md %})
@@ -39,4 +38,3 @@ You can also make a LineString using the [aggregate function form]({% link {{ pa
- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/migrate-from-geojson.md b/src/current/v26.1/migrate-from-geojson.md
index e0d804f0be7..95ebe6b8e4e 100644
--- a/src/current/v26.1/migrate-from-geojson.md
+++ b/src/current/v26.1/migrate-from-geojson.md
@@ -5,7 +5,7 @@ toc: true
docs_area: migrate
---
-CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/export-spatial-data.md %}).
+CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/query-spatial-data.md %}).
This page has instructions for migrating data from the [GeoJSON](https://wikipedia.org/wiki/GeoJSON) format into CockroachDB using [`ogr2ogr`](https://gdal.org/programs/ogr2ogr.html) and [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %}).
@@ -116,8 +116,6 @@ IMPORT INTO underground_storage_tank CSV DATA ('http://localhost:3000/tanks.csv'
## See also
- [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %})
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
@@ -128,7 +126,6 @@ IMPORT INTO underground_storage_tank CSV DATA ('http://localhost:3000/tanks.csv'
- [Back Up and Restore Data]({% link {{ page.version.version }}/take-full-and-incremental-backups.md %})
- [Use the Built-in SQL Client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [`cockroach` Commands Overview]({% link {{ page.version.version }}/cockroach-commands.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
{% comment %} Reference Links {% endcomment %}
diff --git a/src/current/v26.1/migrate-from-geopackage.md b/src/current/v26.1/migrate-from-geopackage.md
index c3fabeb57ef..949da54d47a 100644
--- a/src/current/v26.1/migrate-from-geopackage.md
+++ b/src/current/v26.1/migrate-from-geopackage.md
@@ -5,7 +5,7 @@ toc: true
docs_area: migrate
---
- CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/export-spatial-data.md %}).
+ CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/query-spatial-data.md %}).
This page has instructions for migrating data from the [GeoPackage](https://www.geopackage.org/) format into CockroachDB using [`ogr2ogr`](https://gdal.org/programs/ogr2ogr.html) and [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %}).
@@ -109,8 +109,6 @@ IMPORT INTO busstops CSV DATA ('http://localhost:3000/busstops.csv') WITH skip =
## See also
- [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %})
@@ -120,7 +118,6 @@ IMPORT INTO busstops CSV DATA ('http://localhost:3000/busstops.csv') WITH skip =
- [Back Up and Restore Data]({% link {{ page.version.version }}/take-full-and-incremental-backups.md %})
- [Use the Built-in SQL Client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [`cockroach` Commands Overview]({% link {{ page.version.version }}/cockroach-commands.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
{% comment %} Reference Links {% endcomment %}
diff --git a/src/current/v26.1/migrate-from-openstreetmap.md b/src/current/v26.1/migrate-from-openstreetmap.md
index a4bcdc56eef..cccb9aad434 100644
--- a/src/current/v26.1/migrate-from-openstreetmap.md
+++ b/src/current/v26.1/migrate-from-openstreetmap.md
@@ -5,7 +5,7 @@ toc: true
docs_area: migrate
---
- CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/export-spatial-data.md %}).
+ CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/query-spatial-data.md %}).
This page has instructions for migrating data from [OpenStreetMap](https://www.openstreetmap.org) `.pbf` data files into CockroachDB using [`osm2pgsql`](https://github.com/openstreetmap/osm2pgsql/).
@@ -122,8 +122,6 @@ Osm2pgsql took 2879s overall
## See also
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Migrate from GeoPackages]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %})
@@ -134,7 +132,6 @@ Osm2pgsql took 2879s overall
- [Back Up and Restore Data]({% link {{ page.version.version }}/take-full-and-incremental-backups.md %})
- [Use the Built-in SQL Client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [`cockroach` Commands Overview]({% link {{ page.version.version }}/cockroach-commands.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
{% comment %} Reference Links {% endcomment %}
diff --git a/src/current/v26.1/migrate-from-shapefiles.md b/src/current/v26.1/migrate-from-shapefiles.md
index 44f366d5a69..1737f3209b8 100644
--- a/src/current/v26.1/migrate-from-shapefiles.md
+++ b/src/current/v26.1/migrate-from-shapefiles.md
@@ -5,7 +5,7 @@ toc: true
docs_area: migrate
---
-CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/export-spatial-data.md %}).
+CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/query-spatial-data.md %}).
This page has instructions for migrating data from ESRI [Shapefiles]({% link {{ page.version.version }}/architecture/glossary.md %}#shapefile) into CockroachDB using [`ogr2ogr`](https://gdal.org/programs/ogr2ogr.html) and [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %}).
@@ -133,10 +133,7 @@ IMPORT INTO tornadoes CSV DATA ('http://localhost:3000/tornadoes.csv') WITH skip
## See also
- [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Migrate from GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %})
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
diff --git a/src/current/v26.1/multilinestring.md b/src/current/v26.1/multilinestring.md
index 7918f325711..42bb2de319c 100644
--- a/src/current/v26.1/multilinestring.md
+++ b/src/current/v26.1/multilinestring.md
@@ -67,7 +67,6 @@ A MultiLineString can be created from SQL by calling an aggregate function such
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [POINT]({% link {{ page.version.version }}/point.md %})
- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
@@ -75,4 +74,3 @@ A MultiLineString can be created from SQL by calling an aggregate function such
- [MULTIPOINT]({% link {{ page.version.version }}/multipoint.md %})
- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/multipoint.md b/src/current/v26.1/multipoint.md
index 3b9fd6b296e..6a67acd7d83 100644
--- a/src/current/v26.1/multipoint.md
+++ b/src/current/v26.1/multipoint.md
@@ -69,7 +69,6 @@ SELECT ST_GeomFromText('MULTIPOINT (-87.738258999999999 42.010930999999999, -87.
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [POINT]({% link {{ page.version.version }}/point.md %})
- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
@@ -77,4 +76,3 @@ SELECT ST_GeomFromText('MULTIPOINT (-87.738258999999999 42.010930999999999, -87.
- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/multipolygon.md b/src/current/v26.1/multipolygon.md
index d2ceaed2cdb..02eb315c354 100644
--- a/src/current/v26.1/multipolygon.md
+++ b/src/current/v26.1/multipolygon.md
@@ -27,7 +27,6 @@ SELECT ST_GeomFromText('SRID=4326;MULTIPOLYGON(((-87.906471 43.038902, -95.99277
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [POINT]({% link {{ page.version.version }}/point.md %})
- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
@@ -35,4 +34,3 @@ SELECT ST_GeomFromText('SRID=4326;MULTIPOLYGON(((-87.906471 43.038902, -95.99277
- [MULTIPOINT]({% link {{ page.version.version }}/multipoint.md %})
- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/performance-best-practices-overview.md b/src/current/v26.1/performance-best-practices-overview.md
index 2da6890047a..538635dcab1 100644
--- a/src/current/v26.1/performance-best-practices-overview.md
+++ b/src/current/v26.1/performance-best-practices-overview.md
@@ -15,9 +15,9 @@ For `INSERT`, `UPSERT`, and `DELETE` statements, a single multi-row statement is
For more information, see:
-- [Insert Data]({% link {{ page.version.version }}/insert-data.md %})
-- [Update Data]({% link {{ page.version.version }}/update-data.md %})
-- [Delete Data]({% link {{ page.version.version }}/delete-data.md %})
+- [`INSERT`]({% link {{ page.version.version }}/insert.md %})
+- [`UPDATE`]({% link {{ page.version.version }}/update.md %})
+- [`DELETE`]({% link {{ page.version.version }}/delete.md %})
- [How to improve IoT application performance with multi-row DML](https://www.cockroachlabs.com/blog/multi-row-dml/)
### Use `UPSERT` instead of `INSERT ON CONFLICT` on tables with no secondary indexes
diff --git a/src/current/v26.1/point.md b/src/current/v26.1/point.md
index ce6eba7b9e0..35d0cf44418 100644
--- a/src/current/v26.1/point.md
+++ b/src/current/v26.1/point.md
@@ -47,7 +47,6 @@ SELECT ST_GeomFromText('POINT(0 0)');
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
- [POLYGON]({% link {{ page.version.version }}/polygon.md %})
@@ -55,4 +54,3 @@ SELECT ST_GeomFromText('POINT(0 0)');
- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/polygon.md b/src/current/v26.1/polygon.md
index 1a61704faf3..1c9983a2542 100644
--- a/src/current/v26.1/polygon.md
+++ b/src/current/v26.1/polygon.md
@@ -62,7 +62,6 @@ SELECT ST_MakePolygon('LINESTRING(0 0, 0 1024, 1024 1024, 1024 0, 0 0)');
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [POINT]({% link {{ page.version.version }}/point.md %})
- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
@@ -70,4 +69,3 @@ SELECT ST_MakePolygon('LINESTRING(0 0, 0 1024, 1024 1024, 1024 0, 0 0)');
- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/row-level-security.md b/src/current/v26.1/row-level-security.md
index 4f8c8075c3c..d4dc779962a 100644
--- a/src/current/v26.1/row-level-security.md
+++ b/src/current/v26.1/row-level-security.md
@@ -37,7 +37,7 @@ For an example, refer to [RLS for Multi-Tenant Isolation](#rls-for-multi-tenant-
At a high level, the steps for using row-level security (RLS) are as follows:
-1. Choose which [schema objects]({% link {{ page.version.version }}/schema-design-overview.md %}) need row-level security. You can re-use existing schemas, or create new ones and [insert data]({% link {{ page.version.version }}/insert-data.md %}). ([`CREATE TABLE`]({% link {{ page.version.version }}/create-table.md %}), [`INSERT`]({% link {{ page.version.version }}/insert.md %}))
+1. Choose which [schema objects]({% link {{ page.version.version }}/schema-design-overview.md %}) need row-level security. You can re-use existing schemas, or create new ones and [insert data]({% link {{ page.version.version }}/insert.md %}). ([`CREATE TABLE`]({% link {{ page.version.version }}/create-table.md %}), [`INSERT`]({% link {{ page.version.version }}/insert.md %}))
2. [Create roles]({% link {{ page.version.version }}/create-role.md %}) & [grant access]({% link {{ page.version.version }}/grant.md %}) to schema objects by those roles. ([`CREATE ROLE`]({% link {{ page.version.version }}/create-role.md %}), [`GRANT`]({% link {{ page.version.version }}/grant.md %}))
3. Enable row-level security on the schema objects. ([`ALTER TABLE ... ENABLE ROW LEVEL SECURITY`]({% link {{ page.version.version }}/alter-table.md %}#enable-row-level-security))
4. Define row-level security policies on the schema objects which are assigned to specific roles. ([`CREATE POLICY`]({% link {{ page.version.version }}/create-policy.md %}))
diff --git a/src/current/v26.1/row-level-ttl.md b/src/current/v26.1/row-level-ttl.md
index 0a02b9c82dd..9613cf4dd8f 100644
--- a/src/current/v26.1/row-level-ttl.md
+++ b/src/current/v26.1/row-level-ttl.md
@@ -586,7 +586,6 @@ If you are migrating your TTL usage from an earlier version of CockroachDB, the
## See also
- [Bulk-delete Data]({% link {{ page.version.version }}/bulk-delete-data.md %})
-- [Delete data]({% link {{ page.version.version }}/delete-data.md %})
- [`DELETE`]({% link {{ page.version.version }}/delete.md %})
- [`SELECT` clause]({% link {{ page.version.version }}/select-clause.md %})
- [`AS OF SYSTEM TIME`]({% link {{ page.version.version }}/as-of-system-time.md %})
diff --git a/src/current/v26.1/run-multi-statement-transactions.md b/src/current/v26.1/run-multi-statement-transactions.md
index d26f45a6bd0..ab7e286f969 100644
--- a/src/current/v26.1/run-multi-statement-transactions.md
+++ b/src/current/v26.1/run-multi-statement-transactions.md
@@ -14,7 +14,7 @@ Make sure you have already:
- Set up a [local cluster]({% link {{ page.version.version }}/secure-a-cluster.md %}).
- [Installed a PostgreSQL client]({% link {{ page.version.version }}/install-client-drivers.md %}).
- [Connected to the database]({% link {{ page.version.version }}/connect-to-the-database.md %}).
-- [Inserted data]({% link {{ page.version.version }}/insert-data.md %}) that you now want to run queries against.
+- [Inserted data]({% link {{ page.version.version }}/insert.md %}) that you now want to run queries against.
{% include {{page.version.version}}/app/retry-errors.md %}
@@ -84,9 +84,9 @@ Reference information related to this task:
Other common tasks:
- [Connect to the Database]({% link {{ page.version.version }}/connect-to-the-database.md %})
-- [Insert Data]({% link {{ page.version.version }}/insert-data.md %})
-- [Update Data]({% link {{ page.version.version }}/update-data.md %})
-- [Delete Data]({% link {{ page.version.version }}/delete-data.md %})
+- [Insert Data]({% link {{ page.version.version }}/insert.md %})
+- [Update Data]({% link {{ page.version.version }}/update.md %})
+- [Delete Data]({% link {{ page.version.version }}/delete.md %})
- [Optimize Statement Performance][fast]
- [Troubleshoot SQL Statements]({% link {{ page.version.version }}/query-behavior-troubleshooting.md %})
diff --git a/src/current/v26.1/schema-design-indexes.md b/src/current/v26.1/schema-design-indexes.md
index 60a904b357e..1afecdb768b 100644
--- a/src/current/v26.1/schema-design-indexes.md
+++ b/src/current/v26.1/schema-design-indexes.md
@@ -274,14 +274,14 @@ To see an index definition, use a [`SHOW CREATE`]({% link {{ page.version.versio
(1 row)
~~~
-After creating a database, a user-defined schema, some tables, and secondary indexes, the database schema should be ready for your application to [write]({% link {{ page.version.version }}/insert-data.md %}) and read data.
+After creating a database, a user-defined schema, some tables, and secondary indexes, the database schema should be ready for your application to [write]({% link {{ page.version.version }}/insert.md %}) and read data.
It's likely that you will need to update your database schema at some point. We also recommend reading about [how online schema changes work in CockroachDB]({% link {{ page.version.version }}/online-schema-changes.md %}).
## What's next?
- [Online Schema Changes]({% link {{ page.version.version }}/online-schema-changes.md %})
-- [Insert Data]({% link {{ page.version.version }}/insert-data.md %})
+- [`INSERT`]({% link {{ page.version.version }}/insert.md %})
You might also be interested in the following pages:
diff --git a/src/current/v26.1/secure-a-cluster.md b/src/current/v26.1/secure-a-cluster.md
index 7f8f9d74ebe..b4fc4e477b4 100644
--- a/src/current/v26.1/secure-a-cluster.md
+++ b/src/current/v26.1/secure-a-cluster.md
@@ -460,7 +460,7 @@ Adding capacity is as simple as starting more nodes with `cockroach start`.
- [Install the client driver]({% link {{ page.version.version }}/install-client-drivers.md %}) for your preferred language
- Learn more about [CockroachDB SQL]({% link {{ page.version.version }}/learn-cockroachdb-sql.md %}) and the [built-in SQL client]({% link {{ page.version.version }}/cockroach-sql.md %})
-- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
+- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and JSON support({% link {{ page.version.version }}/jsonb.md %})
You might also be interested in the following pages:
diff --git a/src/current/v26.1/simulate-a-multi-region-cluster-on-localhost.md b/src/current/v26.1/simulate-a-multi-region-cluster-on-localhost.md
index c539bbc0a0d..f77db92ae17 100644
--- a/src/current/v26.1/simulate-a-multi-region-cluster-on-localhost.md
+++ b/src/current/v26.1/simulate-a-multi-region-cluster-on-localhost.md
@@ -98,4 +98,3 @@ When you're done with your demo cluster, you can wipe the cluster by typing the
- [Low Latency Reads and Writes in a Multi-Region Cluster]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %})
- [Fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %})
- [Serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %})
- - [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
diff --git a/src/current/v26.1/spatial-data-overview.md b/src/current/v26.1/spatial-data-overview.md
index ec183bdaad9..f837e390f0a 100644
--- a/src/current/v26.1/spatial-data-overview.md
+++ b/src/current/v26.1/spatial-data-overview.md
@@ -13,7 +13,6 @@ See the links below for more information about how to use CockroachDB for spatia
## Getting Started
- [Install CockroachDB]({% link {{ page.version.version }}/install-cockroachdb.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
## Migrating spatial data into and out of CockroachDB
@@ -21,7 +20,6 @@ See the links below for more information about how to use CockroachDB for spatia
- [Migrate from GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %})
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
## Reference
@@ -71,4 +69,3 @@ In addition to the [generated reference documentation for spatial functions]({%
## See also
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/spatial-indexes.md b/src/current/v26.1/spatial-indexes.md
index 9dbac1f399d..63b7b9a8700 100644
--- a/src/current/v26.1/spatial-indexes.md
+++ b/src/current/v26.1/spatial-indexes.md
@@ -6,7 +6,7 @@ keywords: gin, gin index, gin indexes, inverted index, inverted indexes, acceler
docs_area: reference.sql
---
-This page describes CockroachDB's support for indexing [spatial data]({% link {{ page.version.version }}/export-spatial-data.md %}), including:
+This page describes CockroachDB's support for indexing [spatial data]({% link {{ page.version.version }}/query-spatial-data.md %}), including:
- What spatial indexes are
- How they work
@@ -227,8 +227,6 @@ CREATE INDEX geog_idx_3 ON geo_table USING GIST(geog);
- [S2 Geometry Library](https://s2geometry.io/)
- [Indexes]({% link {{ page.version.version }}/indexes.md %})
- [Spatial Data Overview]({% link {{ page.version.version }}/spatial-data-overview.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
- [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %})
@@ -236,4 +234,3 @@ CREATE INDEX geog_idx_3 ON geo_table USING GIST(geog);
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/spatial-tutorial.md b/src/current/v26.1/spatial-tutorial.md
deleted file mode 100644
index f668a0644f9..00000000000
--- a/src/current/v26.1/spatial-tutorial.md
+++ /dev/null
@@ -1,1861 +0,0 @@
----
-title: Spatial Data
-summary: Tutorial for working with spatial data in CockroachDB.
-toc: true
-toc_not_nested: true
-docs_area: deploy
----
-
-In this tutorial, you will plan a vacation from New York City to the [Adirondack Mountains](https://visitadirondacks.com/about) in northern New York State to do some birdwatching while visiting local independent bookstores. In the process, you will explore several of CockroachDB's [spatial capabilities]({% link {{ page.version.version }}/spatial-data-overview.md %}):
-
-+ Importing spatial data from SQL files (including how to build spatial geometries from data in CSV files).
-+ Putting together separate spatial data sets to ask and answer potentially interesting questions.
-+ Using various [built-in functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions) for operating on spatial data.
-+ Creating [indexes]({% link {{ page.version.version }}/spatial-indexes.md %}) on spatial data.
-+ Performing [joins]({% link {{ page.version.version }}/joins.md %}) on spatial data, and using [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}) to make sure indexes are effective.
-+ Visualizing the output of your queries using free tools like 
-
-### (2) What is the total area of Loon sightings?
-
-Now that you have some sense of how loon sightings are distributed across the state, you may wonder: What is the area of the loon's approximate habitat (per these sightings) in New York State?
-
-To find the answer:
-
-1. Collect the geometries of all loon sightings together in a CTE as one geometry.
-1. Get the area of the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of the resulting geometry.
-1. Because the `birds.routes` data uses [SRID 4326]({% link {{ page.version.version }}/srid-4326.md %}), the resulting area is measured in degrees, which can be converted to square miles by casting the data to a `GEOGRAPHY` type and dividing by 1609 (the number of meters in a mile) squared.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_sightings
- AS (
- SELECT
- st_collect(routes.geom) AS the_geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- st_area(st_convexhull(the_geom)::GEOGRAPHY) / 1609 ^ 2
-FROM
- loon_sightings;
-~~~
-
-The result is an area of about 10,000 square miles, which can be visualized by a box with sides that are a little over 100 miles long. This answer can be verified by looking at the map output from [#1][q_01] and noting that most of the observation locations are well within 100 miles of each other.
-
-~~~
- ?column?
-----------------------
- 10430.691899422598
-(1 row)
-~~~
-
-### (3) How many Loon sightings have there been in NY state in the years 2000-2019?
-
-In addition to the [loon observation point locations][q_01] and the [area of those observations][q_02] (which you can take as a proxy for the size of the loon's local habitat), you may want to know exactly how many times a loon has been sighted in the area.
-
-To find the answer:
-
-1. Join `birds.birds` and `birds.observations` on the bird ID where the bird name is "Common Loon".
-1. Sum all of the sightings; the `GROUP BY` on bird names is necessary due to the use of the `sum` aggregate function.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-SELECT
- birds.name, sum(observations.count) AS sightings
-FROM
- birds.birds, birds.observations
-WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
-GROUP BY
- birds.name;
-~~~
-
-~~~
- name | sightings
---------------+------------
- Common Loon | 269
-(1 row)
-~~~
-
-### (4) How many Loon sightings were there in NY state in just the year 2019?
-
-You might like to get a sense of how many of the loon sightings were more recent. For example, if there have been fewer sightings of loons in recent years, you might wonder if the population were declining in NY State.
-
-To determine how many sightings there were in NY state in 2019, re-use the query from [#3][q_03], with the additional constraint that the observation year is 2019.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-SELECT
- birds.name, sum(observations.count) AS sightings
-FROM
- birds.birds, birds.observations
-WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.year = 2019
-GROUP BY
- birds.name;
-~~~
-
-If the sightings are evenly distributed, you might expect this to yield about 13 sightings. In fact, it's pretty close. This suggests that the loon population may have remained fairly stable over the years 2000-2019.
-
-~~~
- name | sightings
---------------+------------
- Common Loon | 12
-(1 row)
-~~~
-
-### (5) What is the density of Loon sightings per square mile in its range as defined by this data set in 2019?
-
-Since you are planning to do some hiking back into the woods to find the actual loons, you may get curious as to how densely the loon population is scattered over its habitat. From a practical perspective, the distribution is not actually even, since loons are most frequently located on lakes and ponds. Even so, it may serve as a useful proxy for the general areas in which to focus your birdwatching travels.
-
-To answer this question:
-
-1. Build a CTE that returns both the convex hull of loon habitat, as well as the sum of all loon observations in NY.
-1. Query the result table of the CTE from step 1 to divide the number of sightings by the area of the loon's habitat (the convex hull). As in [#2][q_02], do some arithmetic to convert from the unit of degrees returned by [SRID 4326]({% link {{ page.version.version }}/srid-4326.md %}) to square miles.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- sum(observations.count) AS sightings,
- st_convexhull(st_collect(routes.geom))
- AS the_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- AND observations.year = 2019
- )
-SELECT
- loon_habitat.sightings::FLOAT8
- / (
- SELECT
- st_area(loon_habitat.the_hull::GEOGRAPHY)
- / 1609 ^ 2
- )
-FROM
- loon_habitat;
-~~~
-
-It turns out that this is a pretty small number. There are not very many loon sightings per square mile within its overall habitat area, since lakes and ponds make up a small portion of the physical space inside the convex hull of observations.
-
-~~~
- ?column?
-------------------------
- 0.005078531189694916
-(1 row)
-~~~
-
-## Step 5. Find bookstores to visit
-
-### (6) What are the bookstores that lie within the Loon's habitat range in NY state?
-
-Now that you have asked (and answered) some exploratory questions that may inform your birdwatching activities while on vacation, it would be good to start thinking about what bookstores to visit as part of your travels.
-
-A natural question that arises is: given that you are looking for loon habitat, what are the bookstores located within that habitat?
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above CTE with a query against [the `bookstores` table](#the-bookstores-and-bookstore_routes-tables) that checks whether a bookstore's location is [contained]({% link {{ page.version.version }}/st_contains.md %}) by the loon habitat. Note that the query below [orders by]({% link {{ page.version.version }}/order-by.md %}) the store geometries so that stores in the list are clustered by location. This ordering may be useful if you want to travel between nearby stores. For more information about how this ordering is calculated, see [How CockroachDB's spatial indexing works]({% link {{ page.version.version }}/spatial-indexes.md %}#how-cockroachdbs-spatial-indexing-works).
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_sightings
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS loon_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- name, street, city, state, url, phone
-FROM
- tutorial.bookstores, loon_sightings
-WHERE
- st_contains(loon_hull, geom)
-ORDER BY
- geom;
-~~~
-
-~~~
- name | street | city | state | url | phone
----------------------------------------------------+-----------------------+----------------+-------+-------------------------------------------------+-----------------
- Blacktree Books | 5006 State Highway 23 | Oneonta | NY | https://blacktreeoneonta.square.site/ | 6074321200
- The Green Toad Bookstore | 198 Main St | Oneonta | NY | http://www.greentoadbookstore.com | (607) 433-8898
- The Treehouse Reading and Arts Center | 587 Main St Ste 304 | New York Mills | NY | http://treehousebookshop.com | 315-765-6262
- Gansevoort House Books at Gems Along the Mohawk | 800 Mohawk St | Herkimer | NY | |
- Gansevoort House Books at The Shoppes at 25 West | 25 W Mill Street | Little Falls | NY | http://www.gansevoorthouse.com/bookstore/ |
- Mysteries On Main Street | 144 W Main St | Johnstown | NY | https://www.facebook.com/MysteriesOnMainStreet/ | (518) 736-2665
- The Bookstore Plus Music & Art | 2491 Main St | Lake Placid | NY | http://www.thebookstoreplus.com | (518) 523-2950
- The Book Nook (Saranac Lake, NY) | 7 Broadway | Saranac Lake | NY | https://www.facebook.com/slbooknook/ | 6315999511
-(8 rows)
-~~~
-
-### (7) How many different species of bird habitats contain the location of the Book Nook in Saranac Lake, NY?
-
-As a birdwatcher, you probably do not want to only see the Common Loon. As long as you are spending time outside, it would be nice to see what other species of bird are in the area. This may provide even more birdwatching fun.
-
-Since you know you will want to hit up [The Book Nook in Saranac Lake, NY](https://www.facebook.com/slbooknook/) at least once on your trip, you decide to see what bird species' habitats contain the store's location. That way you can get a sense of the diversity of bird species in the area.
-
-To answer this question:
-
-1. Build a CTE that returns some information about the bookstore you want to visit.
-1. Build another CTE that returns information about the habitats of birds observed in NY state, and collects the habitat geometries together into one geometry.
-1. Join the results of the above CTEs and query the count of birds whose habitats contain the location of the bookstore.
-
-{{site.data.alerts.callout_info}}
-The final [`SELECT`]({% link {{ page.version.version }}/selection-queries.md %}) in the query below is doing a join that will not benefit from [spatial indexing]({% link {{ page.version.version }}/spatial-indexes.md %}), since both sides of the join are the results of [CTEs]({% link {{ page.version.version }}/common-table-expressions.md %}), and are therefore not indexed.
-{{site.data.alerts.end}}
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- the_book_nook
- AS (
- SELECT
- bookstores.name, street, city, state, geom
- FROM
- tutorial.bookstores
- WHERE
- state = 'NY' AND city = 'Saranac Lake'
- ),
- local_birds
- AS (
- SELECT
- birds.name,
- st_convexhull(st_collect(routes.geom))
- AS the_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.id = observations.bird_id
- AND observations.route_id = routes.id
- GROUP BY
- birds.name
- )
-SELECT
- count(local_birds.name)
-FROM
- local_birds, the_book_nook
-WHERE
- st_contains(local_birds.the_hull, the_book_nook.geom);
-~~~
-
-This is encouraging; there are over 120 species of birds for you to choose from whose habitats include the bookstore you want to visit!
-
-~~~
- count
----------
- 124
-(1 row)
-~~~
-
-You can verify the results by checking how many bird species have been sighted by the bird survey overall; you should expect that this number will be much larger than 124, and indeed it is:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-SELECT COUNT(name) FROM birds.birds;
-~~~
-
-~~~
- count
----------
- 756
-(1 row)
-~~~
-
-### (8) Which 25 birds were most often sighted within 10 miles of the Book Nook in Saranac Lake, NY during the 2000-2019 observation period?
-
-It's great that you [know how many bird species may be near a given bookstore][q_07]; however, that query didn't tell you which birds you have the best chance of seeing. Therefore, you'd like to figure out what birds are the most commonly seen in the area near a bookstore you want to visit: The Book Nook in Saranac Lake, NY.
-
-To answer this question:
-
-1. Build a CTE that returns some information about the bookstore you want to visit.
-1. Join the results of the above CTE and a query against the `birds` database that lists the names and observation totals (sums) of birds whose habitats are within 10 miles of the location of the bookstore.
-
-{{site.data.alerts.callout_info}}
-The query below can also be written using an explicit `ST_DWithin`, which is an [index-accelerated function]({% link {{ page.version.version }}/query-spatial-data.md %}#performance). CockroachDB optimizes `ST_Distance(...) < $some_value` to use `ST_DWithin` (see this query's [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}) output for details).
-{{site.data.alerts.end}}
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- the_book_nook
- AS (
- SELECT
- bookstores.name, street, city, state, geom
- FROM
- tutorial.bookstores
- WHERE
- state = 'NY' AND city = 'Saranac Lake'
- )
-SELECT
- birds.name, sum(observations.count) AS sightings
-FROM
- birds.birds,
- birds.observations,
- birds.routes,
- the_book_nook
-WHERE
- birds.id = observations.bird_id
- AND observations.route_id = routes.id
- AND st_distance(
- the_book_nook.geom::GEOGRAPHY,
- routes.geom::GEOGRAPHY
- )
- < (1609 * 10)
-GROUP BY
- birds.name
-ORDER BY
- sightings DESC
-LIMIT
- 25;
-~~~
-
-Perhaps surprisingly, the Red-eyed Vireo is the "winner", followed by a number of other fairly common birds. If you want a birdwatching challenge, you can reverse the sort order of the above query to find the rarest birds.
-
-~~~
- name | sightings
------------------------------------------+------------
- Red-eyed Vireo | 2557
- White-throated Sparrow | 928
- Hermit Thrush | 924
- American Robin | 691
- Ovenbird | 650
- American Crow | 528
- (Myrtle Warbler) Yellow-rumped Warbler | 506
- Chipping Sparrow | 465
- Black-capped Chickadee | 390
- Yellow-bellied Sapsucker | 359
- Blue-headed Vireo | 357
- Blue Jay | 345
- Winter Wren | 344
- Cedar Waxwing | 272
- Blackburnian Warbler | 248
- Magnolia Warbler | 236
- Black-throated Green Warbler | 226
- Common Grackle | 218
- Red-breasted Nuthatch | 209
- Common Yellowthroat | 184
- Northern Parula | 175
- Nashville Warbler | 164
- Red-winged Blackbird | 152
- Least Flycatcher | 128
- American Redstart | 123
-(25 rows)
-~~~
-
-### (9) What does the shape of all bookstore locations that lie within the Loon's habitat look like?
-
-You [already discovered which bookstores are located within loon habitat][q_06]. However, you cannot really tell where these stores are located based on reading that query's output. You need to see them on the map. Therefore, for trip planning purposes, you decide you would like to look at the shape of the convex hull of the store locations.
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above CTE with a query against [the `bookstores` table](#the-bookstores-and-bookstore_routes-tables) that checks whether a bookstore's location is [contained]({% link {{ page.version.version }}/st_contains.md %}) by the loon habitat.
-1. Collect the geometries that result from the step above into a single geometry, calculate its convex hull, and return the results as [GeoJSON]({% link {{ page.version.version }}/geojson.md %}).
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS the_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- st_asgeojson(st_convexhull(st_collect(geom)))
-FROM
- tutorial.bookstores, loon_habitat
-WHERE
- st_contains(the_hull, geom);
-~~~
-
-~~~
- st_asgeojson
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- {"type":"Polygon","coordinates":[[[-75.036815,42.448793],[-75.062138,42.454003],[-75.296575,43.098322],[-74.131673,44.326892],[-73.980251,44.279377],[-74.374175,43.006495],[-75.036815,42.448793]]]}
-(1 row)
-~~~
-
-Paste the result above into 
-
-### (10) What is the area of the shape of all bookstore locations that are in the Loon's habitat range within NY state?
-
-You have already [visualized the convex hull][q_09], but now you would like to calculate its area in square miles.
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above CTE with a query against [the `bookstores` table](#the-bookstores-and-bookstore_routes-tables) that checks whether a bookstore's location is [contained]({% link {{ page.version.version }}/st_contains.md %}) by the loon habitat.
-1. Get the area of the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of the resulting geometry.
-1. Collect the geometries that result from the step above into a single geometry, calculate its convex hull, and calculate the area of the hull. As in previous examples, note that because the `birds.routes` data uses [SRID 4326]({% link {{ page.version.version }}/srid-4326.md %}), the resulting area is measured in degrees, which is converted to square miles by casting the data to a `GEOGRAPHY` type and dividing by 1609 (the number of meters in a mile) squared.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS the_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- st_area(st_convexhull(st_collect(geom))::GEOGRAPHY)
- / 1609 ^ 2
-FROM
- tutorial.bookstores, loon_habitat
-WHERE
- st_contains(the_hull, geom);
-~~~
-
-The result is an area of about 3,500 square miles, which can be visualized by a box with sides that are approximately 60 miles long. This answer can be verified by looking at the map output from [#9][q_09] and noting that most of the points on the hull are within about 60 miles of each other.
-
-~~~
- ?column?
-----------------------
- 3564.1722404508437
-(1 row)
-~~~
-
-## Step 6. Plan your route
-
-### (11) How long is the route from Mysteries on Main Street in Johnstown, NY to The Book Nook in Saranac Lake, NY?
-
-So far, you have written queries that ask questions about bird habitats, bookstore locations, or some combination of the two. Because you are planning a trip, you want to think about how to travel between the bookstores in the loon habitat area. Depending on how far apart the bookstores are, you may want to visit more than one of them in a day, perhaps doing some birdwatching along the way.
-
-In order to accomplish this, you start looking at [the `bookstore_routes` table](#the-bookstores-and-bookstore_routes-tables) to see how far the distance is in miles between two of the bookstores you'd like to visit.
-
-To answer this question:
-
-1. Issue subqueries that find the IDs of two of the bookstores you'd like to travel between, as start and end points.
-1. Measure the length of the geometry that corresponds to those start and end IDs. Note that because the `bookstore_routes.geom` column has a SRID of 0 (which it inherited from the `roads` database from which it was created), you can convert to miles by casting the data to a `GEOGRAPHY` type, which uses meters, and then dividing by 1609 (the number of meters in a mile).
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-SELECT
- st_length(geom::GEOGRAPHY) / 1609
-FROM
- bookstore_routes
-WHERE
- end_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Johnstown' AND state = 'NY'
- )
- AND start_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Saranac Lake' AND state = 'NY'
- );
-~~~
-
-The answer is: these two stores are about 132 miles apart, measured in road miles.
-
-~~~
- ?column?
----------------------
- 132.6837073049756
-(1 row)
-~~~
-
-### (12) What does the route from Mysteries on Main Street in Johnstown, NY to The Book Nook in Saranac Lake, NY look like?
-
-You have [determined how long the drive between these two stores will be][q_11], but you would like to see what it looks like on the map.
-
-To find this out, you can re-use the query from the previous question, using `ST_AsGeoJSON` instead of `ST_Length`, and skipping the distance math to generate output in miles.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-SELECT
- st_asgeojson(geom)
-FROM
- bookstore_routes
-WHERE
- end_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Johnstown' AND state = 'NY'
- )
- AND start_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Saranac Lake' AND state = 'NY'
- );
-~~~
-
-The result is a very large chunk of JSON:
-
-~~~
- st_asgeojson
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- 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-(1 row)
-~~~
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-Paste the result above into 
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-### (13) What were the 25 most-commonly-sighted birds in 2019 within 10 miles of the route between Mysteries on Main Street in Johnstown, NY and The Bookstore Plus in Lake Placid, NY?
-
-A natural question that arises once you start traveling between stores is: if I decide to visit two of these stores in a day, and do some birdwatching in between, what are some of the bird species I may expect to find? In this case you are looking at what species are near the route between [Mysteries on Main Street in Johnstown, NY](https://www.facebook.com/MysteriesOnMainStreet), and [The Bookstore Plus in Lake Placid, NY](http://www.thebookstoreplus.com).
-
-To answer this question:
-
-1. Build a CTE that returns the route between the two stores of interest (a geometry).
-1. Join the results of the above CTE with a query against [the `birds` database](#the-birds-database) that checks whether the distance between the route geometry and the location of the bird observation (`birds.routes.geom`) bookstore's location is less than the desired length of 10 miles. Note that because the call to `ST_Distance` is operating on shapes cast to `GEOGRAPHY` data type, the results are in meters, which then have to be converted to miles by dividing the result by 1609 (the number of meters in a mile).
-
-{{site.data.alerts.callout_info}}
-The query below can also be written using an explicit `ST_DWithin`, which is an [index-accelerated function]({% link {{ page.version.version }}/query-spatial-data.md %}#performance). CockroachDB optimizes `ST_Distance(...) < $some_value` to use `ST_DWithin` (see this query's [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}) output for details).
-{{site.data.alerts.end}}
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- bookstore_trip
- AS (
- SELECT
- geom
- FROM
- bookstore_routes
- WHERE
- start_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Johnstown'
- AND state = 'NY'
- )
- AND end_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Lake Placid'
- AND state = 'NY'
- )
- )
-SELECT
- birds.birds.name,
- sum(birds.observations.count) AS sightings
-FROM
- birds.birds,
- birds.observations,
- birds.routes,
- bookstore_trip
-WHERE
- birds.birds.id = birds.observations.bird_id
- AND observations.route_id = routes.id
- AND observations.year = 2019
- AND st_distance(
- bookstore_trip.geom::GEOGRAPHY,
- birds.routes.geom::GEOGRAPHY
- )
- < (1609 * 10)
-GROUP BY
- birds.name
-ORDER BY
- sightings DESC
-LIMIT
- 25;
-~~~
-
-~~~
- name | sightings
------------------------------------------+------------
- Red-eyed Vireo | 124
- Ovenbird | 98
- Blue Jay | 56
- Black-capped Chickadee | 44
- American Robin | 43
- American Crow | 31
- White-throated Sparrow | 26
- Common Yellowthroat | 25
- Red-breasted Nuthatch | 19
- Blue-headed Vireo | 18
- Red-winged Blackbird | 16
- Veery | 16
- Winter Wren | 16
- American Redstart | 15
- Blackburnian Warbler | 15
- Chipping Sparrow | 14
- Least Flycatcher | 13
- Northern Parula | 13
- American Goldfinch | 13
- Magnolia Warbler | 12
- Cedar Waxwing | 12
- Song Sparrow | 11
- Hermit Thrush | 11
- Common Raven | 10
- (Myrtle Warbler) Yellow-rumped Warbler | 10
-(25 rows)
-~~~
-
-### (14) What are the 25 least often sighted other species of birds in the loon's sightings range?
-
-Since you are already looking for loons, you would like to know if there are other, more challenging birdwatching opportunities available within the loon habitat you are already visiting. Specifically, you would like to know: What are the least-often-observed bird species in the loon's habitat?
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above CTE and a query against [the `birds` database](#the-birds-database) that lists the names and observation totals (sums) of birds whose habitats are contained within the convex hull of loon habitat.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS loon_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- birds.name, sum(birds.observations.count) AS sightings
-FROM
- birds.birds,
- birds.observations,
- birds.routes,
- loon_habitat
-WHERE
- birds.id = observations.bird_id
- AND observations.route_id = routes.id
- AND st_contains(loon_hull, routes.geom)
-GROUP BY
- birds.name
-ORDER BY
- sightings ASC
-LIMIT
- 25;
-~~~
-
-~~~
- name | sightings
----------------------------------------------------+------------
- Vesper Sparrow | 1
- Northern Saw-whet Owl | 1
- Sora | 1
- Least Bittern | 1
- Palm Warbler | 1
- Ring-necked Duck | 1
- American Three-toed Woodpecker | 1
- unid. Yellow-billed Cuckoo / Black-billed Cuckoo | 1
- unid. Accipiter hawk | 1
- Eastern Whip-poor-will | 1
- Golden-winged Warbler | 1
- Eastern Screech-Owl | 1
- Northern Goshawk | 1
- Upland Sandpiper | 1
- Hooded Warbler | 2
- Grasshopper Sparrow | 2
- Gadwall | 2
- Brewster Warbler (Golden-winged x Blue-winged) | 2
- Double-crested Cormorant | 3
- White-winged Crossbill | 3
- Marsh Wren | 3
- Pine Siskin | 3
- Great Horned Owl | 3
- Northern Mockingbird | 3
- Red-shouldered Hawk | 4
-(25 rows)
-~~~
-
-## Step 7. Get road and travel info
-
-### (15) What are the top 10 roads nearest to a Loon sighting location in NY?
-
-So far, you have learned [where the birds are](#step-4-scout-loon-locations), [where the overlaps are between birds and bookstores](#step-5-find-bookstores-to-visit), and [how to travel between bookstores while finding birds](#step-6-plan-your-route).
-
-Now you will start asking questions that involve looking at [the `roads` table](#the-roads-table) as well.
-
-Specifically, you would like to know: What are the 10 roads that are closest to a loon observation site? The answer to this question may help you figure out exactly which roads you should travel down to start the search for loons. The closer a road is to the sighting location, the better: it means there is probably less work to do to see the birds you are looking for.
-
-To answer this question:
-
-1. Build a CTE called `loon_habitat` that collects all of the Common Loon sightings into a single geometry.
-1. Build another CTE called `nearby_roads` that joins the results of the subquery above with [the `roads` table](#the-roads-table) and pulls out the roads in NY state that are within a degree (about 60 nautical miles). Order the roads returned by their distance from a loon sighting. This will return some duplicate roads (since loons can be sighted multiple times along a single road), which is why you need to `LIMIT` to 20 here so you can get the list down to 10 later. Because the data in the `roads` table has an SRID of 0, you need to use `ST_SetSRID` to set its SRID to [4326]({% link {{ page.version.version }}/srid-4326.md %}). This step is necessary because `ST_Distance` cannot operate on geometries with differing SRIDs.
-1. Finally, query the results of the `nearby_roads` subquery to get a list of 10 distinct road names that you can plan to visit.
-
-{{site.data.alerts.callout_info}}
-The query below can also be written using an explicit `ST_DWithin`, which is an [index-accelerated function]({% link {{ page.version.version }}/query-spatial-data.md %}#performance). CockroachDB optimizes `ST_Distance(...) < $some_value` to use `ST_DWithin` (see this query's [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}) output for details).
-{{site.data.alerts.end}}
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_collect(routes.geom) AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- ),
- nearby_roads
- AS (
- SELECT
- roads.prime_name AS road_name
- FROM
- roads, loon_habitat
- WHERE
- st_distance(
- loon_habitat.geom,
- st_setsrid(roads.geom, 4326)
- )
- < 1
- ORDER BY
- st_distance(
- loon_habitat.geom,
- st_setsrid(roads.geom, 4326)
- )
- ASC
- LIMIT
- 20
- )
-SELECT
- DISTINCT road_name
-FROM
- nearby_roads
-LIMIT
- 10;
-~~~
-
-~~~
- prime_name
------------------------
- US Route 9
- State Route 30
- State Route 22 Spur
- State Route 22
- State Route 28
- State Route 86
- State Route 28N
- Interstate 87
- US Route 11
- State Route 421
-(10 rows)
-
-Time: 1.447s total (execution 1.446s / network 0.000s)
-~~~
-
-Unfortunately, this query is a bit slower than you would like: about 1.5 seconds on a single-node [`cockroach demo`]({% link {{ page.version.version }}/cockroach-demo.md %}) cluster on a laptop. There are several reasons for this:
-
-1. You haven't created any indexes at all yet. The query is likely to be doing full table scans, which you will need to hunt down with [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}).
-1. CockroachDB does not yet have built-in support for index-based nearest neighbor queries. If this feature is important to you, please comment with some information about your use case on [cockroachdb/cockroach#55227](https://github.com/cockroachdb/cockroach/issues/55227).
-
-Let's look at the `EXPLAIN` output to see if there is something that can be done to improve this query's performance:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-EXPLAIN WITH loon_habitat AS (SELECT st_collect(routes.geom) AS geom FROM birds.birds, birds.observations, birds.routes WHERE birds.name = 'Common Loon' AND birds.id = observations.bird_id AND observations.route_id = routes.id), nearby_roads AS (SELECT roads.prime_name AS road_name FROM roads, loon_habitat WHERE st_distance(loon_habitat.geom, st_setsrid(roads.geom, 4326)) < 1 ORDER BY st_distance(loon_habitat.geom, st_setsrid(roads.geom, 4326)) ASC LIMIT 20) SELECT DISTINCT road_name FROM nearby_roads LIMIT 10;
-~~~
-
-{% include_cached copy-clipboard.html %}
-~~~
- tree | field | description
--------------------------------------------------------------------+---------------------+---------------------------------------------------------
- | distribution | full
- | vectorized | true
- limit | |
- │ | count | 10
- └── distinct | |
- │ | distinct on | road_name
- └── render | |
- └── limit | |
- │ | count | 20
- └── sort | |
- │ | order | +column48
- └── render | |
- └── cross join | |
- │ | pred | st_dwithinexclusive(geom, st_setsrid(geom, 4326), 1.0)
- ├── scan | |
- │ | estimated row count | 225838
- │ | table | roads@primary
- │ | spans | FULL SCAN
- └── render | |
- └── group (scalar) | |
- └── hash join | |
- │ | equality | (route_id) = (id)
- │ | right cols are key |
- ├── hash join | |
- │ │ | equality | (bird_id) = (id)
- │ │ | right cols are key |
- │ ├── scan | |
- │ │ | Estimated row count | 85731
- │ │ | table | observations@primary
- │ │ | spans | FULL SCAN
- │ └── filter | |
- │ │ | filter | name = 'Common Loon'
- │ └── scan | |
- │ | estimated row count | 756
- │ | table | birds@primary
- │ | spans | FULL SCAN
- └── scan | |
- | estimated row count | 129
- | table | routes@primary
- | spans | FULL SCAN
-(40 rows)
-~~~
-
-As you can see above, there are several full table scans being performed (see `FULL SCAN`). For each table, you have a hypothesis on what is causing the scan; usually the hypothesis is that the table is missing an index.
-
-- For [the `roads` table](#the-roads-table), there is no [spatial index]({% link {{ page.version.version }}/spatial-indexes.md %}) on the `road.geom` column. This is probably the worst performance offender of the bunch, since this table has almost a quarter of a million rows. It makes sense that it's quite large, since it represents a road map of the entire United States.
- - In addition to adding an index to `roads.geom`, you will need to update the SRID of the `roads.geom` column to use [SRID 4326]({% link {{ page.version.version }}/srid-4326.md %}) so it matches the SRID of the loon habitat geometry. Updating the SRID to match will allow you to stop using `ST_SetSRID` on `roads.geom` as shown above, which will keep CockroachDB from taking advantage of the spatial index on that column.
-- On [the `birds.observations` table](#the-birds-database), there is no index on the `birds.observations.route_id` or `birds.observations.bird_id` columns, which are used for all bird information lookups.
-- On the [`birds.birds` table](#the-birds-database), there is no index on the `birds.birds.name` column which you're filtering on (this is also used by almost all of your other bird-related queries).
-
-Based on these hypotheses, you take the following steps:
-
-1. Add an index to the `roads.geom` column. Note that creating the index on `roads.geom` takes about a minute on a single-node [`cockroach demo`]({% link {{ page.version.version }}/cockroach-demo.md %}) cluster, since the table is relatively large.
-
- {% include_cached copy-clipboard.html %}
- ~~~ sql
- CREATE INDEX ON roads USING GIST(geom);
- ~~~
-
-1. Update the SRID of the `roads.geom` column from 0 to [4326]({% link {{ page.version.version }}/srid-4326.md %}). This will take a few seconds.
-
- {% include_cached copy-clipboard.html %}
- ~~~ sql
- UPDATE roads SET geom = st_transform(st_setsrid(geom, 4326), 4326) WHERE gid IS NOT NULL RETURNING NOTHING;
- ~~~
-
-1. Add indexes on the `birds.observations.route_id` and `birds.observations.bird_id` columns:
-
- {% include_cached copy-clipboard.html %}
- ~~~ sql
- CREATE INDEX ON birds.observations(bird_id);
- ~~~
-
- {% include_cached copy-clipboard.html %}
- ~~~ sql
- CREATE INDEX ON birds.observations(route_id);
- ~~~
-
-1. Add an index on the `birds.birds.name` column:
-
- {% include_cached copy-clipboard.html %}
- ~~~ sql
- CREATE INDEX ON birds.birds(name);
- ~~~
-
-Did adding all of these indexes and updating the road geometry SRIDs make this query any faster? Let's check. Note that the query below has been modified to no longer use the `ST_SetSRID` function.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_collect(routes.geom) AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- ),
- nearby_roads
- AS (
- SELECT
- roads.prime_name AS road_name
- FROM
- roads, loon_habitat
- WHERE
- st_distance(loon_habitat.geom, roads.geom)
- < 1
- ORDER BY
- st_distance(loon_habitat.geom, roads.geom)
- ASC
- LIMIT
- 20
- )
-SELECT
- DISTINCT road_name
-FROM
- nearby_roads
-LIMIT
- 10;
-~~~
-
-It looks like the answer is yes; you have sped up this query by about 30%:
-
-~~~
- prime_name
------------------------
- US Route 9
- State Route 30
- State Route 22 Spur
- State Route 22
- State Route 28
- State Route 86
- State Route 28N
- Interstate 87
- US Route 11
- State Route 421
-(10 rows)
-
-Time: 998ms total (execution 998ms / network 0ms)
-~~~
-
-To see why, look at the [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}) output:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-EXPLAIN WITH loon_habitat AS (SELECT st_collect(routes.geom) AS geom FROM birds.birds, birds.observations, birds.routes WHERE birds.name = 'Common Loon' AND birds.id = observations.bird_id AND observations.route_id = routes.ID), nearby_roads AS (SELECT roads.prime_name AS road_name FROM roads, loon_habitat WHERE st_distance(loon_habitat.geom, roads.geom) < 1 ORDER BY st_distance(loon_habitat.geom, roads.geom) ASC LIMIT 20) SELECT DISTINCT road_name FROM nearby_roads LIMIT 10;
-~~~
-
-~~~
- tree | field | description
---------------------------------------------------------------------------+-----------------------+----------------------------------------
- | distribution | full
- | vectorized | false
- limit | |
- │ | count | 10
- └── distinct | |
- │ | distinct on | road_name
- └── render | |
- └── limit | |
- │ | count | 20
- └── sort | |
- │ | order | +column49
- └── render | |
- └── lookup join | |
- │ | table | roads@primary
- │ | equality | (gid) = (gid)
- │ | equality cols are key |
- │ | pred | st_dwithinexclusive(geom, geom, 1.0)
- └── inverted join | |
- │ | table | roads@roads_geom_idx
- └── render | |
- └── group (scalar) | |
- └── hash join | |
- │ | equality | (route_id) = (id)
- │ | right cols are key |
- ├── lookup join | |
- │ │ | table | observations@primary
- │ │ | equality | (id) = (id)
- │ │ | equality cols are key |
- │ └── lookup join | |
- │ │ | table | observations@observations_bird_id_idx
- │ │ | equality | (id) = (bird_id)
- │ └── scan | |
- │ | estimated row count | 1
- │ | table | birds@birds_name_idx
- │ | spans | [/'Common Loon' - /'Common Loon']
- └── scan | |
- | estimated row count | 129
- | table | routes@primary
- | spans | FULL SCAN
-(39 rows)
-~~~
-
-Based on the output above, you notice the following improvements:
-
-- The [spatial index]({% link {{ page.version.version }}/spatial-indexes.md %}) on `roads.geom` is being used by the join between that column and the loon habitat geometry. This is the biggest reason for improved performance, since [the `roads` table](#the-roads-table) has ~225,000 rows.
-- You no longer appear to be scanning all ~85,000 rows of [the `birds.observations` table](#the-birds-database) thanks to the index on the `birds.observations.bird_id` column.
-- You are probably getting a small speedup from the index on [`birds.birds.name`](#the-birds-database) (although that table is not very large, only about 750 rows).
-
-### (16) How many miles of roads are contained within the portion of the Loon habitat that lies within NY state?
-
-It may not be immediately relevant to your birdwatching or book-buying travels, but a question of general interest that could arise is: How many miles of roads are contained within loon habitat?
-
-To answer this:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above CTE and a query against [the `roads` table](#the-roads-table) that sums the mileage of all roads that are contained within the convex hull of loon habitat.
-
-{{site.data.alerts.callout_info}}
-Because you are using `ST_Contains`, the query below only sums the road mileages of roads whose geometries lie entirely within the loon habitat.
-{{site.data.alerts.end}}
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- sum(roads.miles)
-FROM
- roads, loon_habitat
-WHERE
- roads.state = 'NY'
- AND st_contains(
- loon_habitat.hull,
- roads.geom
- );
-~~~
-
-The answer to your question is that there are about 1,700 miles of roads within the Common Loon's habitat. Further, you can change the `WHERE` clause of the above query slightly to say `AND (NOT (ST_Contains(...)))` to find out how many miles of roads in NY state are _not_ within loon habitat (it's about 14,600).
-
-~~~
- sum
------------------------
- 1704.65731711040306
-(1 row)
-~~~
-
-### (17) Which bookstore in the Loon's region in NY has the fewest miles of roads within a 10 mile radius of the store?
-
-As you are driving around the Adirondacks, searching for loons as well as your next great read, the question occurs to you: Which bookstore is the most remotely located? This is important since one of your goals for this vacation is to "get away from it all." You do not have population density data, but you do have road data. Therefore you decide to use "miles of roads near the store" as a rough proxy for which store location is the most remote: the fewer miles of roads near the store, the more remote you can assume it is.
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Build a CTE that joins [the `bookstores` table](#the-bookstores-and-bookstore_routes-tables) and the results of the above subquery to generate a set of bookstores inside the loon's habitat area.
-1. Finally, generate a query that joins the results of the above subquery against [the `roads` table](#the-roads-table) based on which roads are within a 10 mile radius. This generates a list of bookstores and the number of miles of nearby roads, sorted in order of which store has the fewest miles of road nearby.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- ),
- loon_bookstores
- AS (
- SELECT
- bookstores.name,
- address,
- bookstores.geom AS geom
- FROM
- bookstores, loon_habitat
- WHERE
- st_contains(
- loon_habitat.geom,
- bookstores.geom
- )
- )
-SELECT
- loon_bookstores.name,
- address,
- sum(roads.miles)::INT AS nearby_road_miles
-FROM
- roads, loon_bookstores
-WHERE
- st_distance(
- loon_bookstores.geom,
- roads.geom
- )
- < (10 / 69)
-GROUP BY
- loon_bookstores.name, address
-ORDER BY
- nearby_road_miles ASC;
-~~~
-
-You get an answer, but unfortunately it's rather slow: about 6 seconds!
-
-~~~
- name | address | nearby_road_miles
----------------------------------------------------+------------------------------------------------+--------------------
- The Bookstore Plus Music & Art | 2491 Main St, Lake Placid, NY, 12946 | 40
- The Book Nook (Saranac Lake, NY) | 7 Broadway, Saranac Lake, NY, 12983 | 81
- Blacktree Books | 5006 State Highway 23, Oneonta, NY, 13820 | 106
- The Green Toad Bookstore | 198 Main St, Oneonta, NY, 13820 | 107
- Gansevoort House Books at Gems Along the Mohawk | 800 Mohawk St, Herkimer, NY, 13350 | 136
- Gansevoort House Books at The Shoppes at 25 West | 25 W Mill Street, Little Falls, NY, 13365 | 155
- The Treehouse Reading and Arts Center | 587 Main St Ste 304, New York Mills, NY, 13417 | 156
- Mysteries On Main Street | 144 W Main St, Johnstown, NY, 12095 | 172
-(8 rows)
-
-Time: 6.214s total (execution 6.214s / network 0.000s)
-~~~
-
-Let's look at the `EXPLAIN` output to see if there is something that can be done to improve this query's performance:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-EXPLAIN WITH loon_habitat AS (SELECT st_convexhull(st_collect(routes.geom)) AS geom FROM birds.birds, birds.observations, birds.routes WHERE birds.name = 'Common Loon' AND birds.id = observations.bird_id AND observations.route_id = routes.id), loon_bookstores AS (SELECT bookstores.name, address, bookstores.geom AS geom FROM bookstores, loon_habitat WHERE st_contains(loon_habitat.geom, bookstores.geom)) SELECT loon_bookstores.name, address, sum(roads.miles)::INT AS nearby_road_miles FROM roads, loon_bookstores WHERE st_distance(loon_bookstores.geom, roads.geom) < (10 / 69) GROUP BY loon_bookstores.name, address ORDER BY nearby_road_miles ASC;
-~~~
-
-~~~
- tree | field | description
-----------------------------------------------------------------+-----------------------+-------------------------------------------------------
- | distribution | full
- | vectorized | true
- sort | |
- │ | order | +nearby_road_miles
- └── render | |
- └── group | |
- │ | group by | name, address
- └── cross join | |
- │ | pred | st_dwithinexclusive(geom, geom, 0.14492753623188406)
- ├── scan | |
- │ | estimated row count | 225838
- │ | table | roads@primary
- │ | spans | FULL SCAN
- └── render | |
- └── cross join | |
- │ | pred | st_contains(geom, geom)
- ├── scan | |
- │ | estimated row count | 2913
- │ | table | bookstores@primary
- │ | spans | FULL SCAN
- └── render | |
- └── group (scalar) | |
- └── hash join | |
- │ | equality | (route_id) = (id)
- │ | right cols are key |
- ├── lookup join | |
- │ │ | table | observations@primary
- │ │ | equality | (id) = (id)
- │ │ | equality cols are key |
- │ └── lookup join | |
- │ │ | table | observations@observations_bird_id_idx
- │ │ | equality | (id) = (bird_id)
- │ └── scan | |
- │ | estimated row count | 1
- │ | table | birds@birds_name_idx
- │ | spans | [/'Common Loon' - /'Common Loon']
- └── scan | |
- | estimated row count | 129
- | table | routes@primary
- | spans | FULL SCAN
-(40 rows)
-~~~
-
-Looking at these results from the bottom up, you can see that:
-
-1. There is a full table scan happening on the `birds.birds.routes` table. Since it's so small (129 rows), let's move on for now.
-1. There is a full table scan on the `bookstores` table. Since the predicate is [`ST_Contains`]({% link {{ page.version.version }}/st_contains.md %}), you probably need to add a [spatial index]({% link {{ page.version.version }}/spatial-indexes.md %}) on `bookstores.geom`.
-1. There is a full table scan happening on the `roads` table. This is a serious problem due to the size of that table: ~225,000 rows. You already have an index on the `roads.geom` column for the `ST_DWithin` predicate to use, so you need to find another way to reduce the number of rows scanned. You can do what you did in [query 16][q_16] and add a check that the `roads.state = 'NY'`, since you are only looking at roads inside New York State.
-
-Based on these observations, you add an index to the `bookstores.geom` column:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-CREATE INDEX ON bookstores USING GIST(geom);
-~~~
-
-After adding the index, you modify your query as shown below to filter on `roads.state`. This looks only at roads in New York (as in [query 16][q_16]):
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- ),
- loon_bookstores
- AS (
- SELECT
- bookstores.name,
- address,
- bookstores.geom AS geom
- FROM
- bookstores, loon_habitat
- WHERE
- st_contains(
- loon_habitat.geom,
- bookstores.geom
- )
- )
-SELECT
- loon_bookstores.name,
- address,
- sum(roads.miles)::INT8 AS nearby_road_miles
-FROM
- roads, loon_bookstores
-WHERE
- roads.state = 'NY'
- AND st_distance(
- roads.geom,
- loon_bookstores.geom
- )
- < (10 / 69)
-GROUP BY
- loon_bookstores.name, address
-ORDER BY
- nearby_road_miles ASC;
-~~~
-
-The steps above brings your query execution down to about 400ms!
-
-~~~
- name | address | nearby_road_miles
----------------------------------------------------+------------------------------------------------+--------------------
- The Bookstore Plus Music & Art | 2491 Main St, Lake Placid, NY, 12946 | 40
- The Book Nook (Saranac Lake, NY) | 7 Broadway, Saranac Lake, NY, 12983 | 81
- Blacktree Books | 5006 State Highway 23, Oneonta, NY, 13820 | 106
- The Green Toad Bookstore | 198 Main St, Oneonta, NY, 13820 | 107
- Gansevoort House Books at Gems Along the Mohawk | 800 Mohawk St, Herkimer, NY, 13350 | 136
- Gansevoort House Books at The Shoppes at 25 West | 25 W Mill Street, Little Falls, NY, 13365 | 155
- The Treehouse Reading and Arts Center | 587 Main St Ste 304, New York Mills, NY, 13417 | 156
- Mysteries On Main Street | 144 W Main St, Johnstown, NY, 12095 | 172
-(8 rows)
-
-Time: 376ms total (execution 376ms / network 0ms)
-~~~
-
-When you look at `EXPLAIN` for the modified query, you see why: the filter on `roads.state='NY'` means you are scanning far fewer columns of the `roads` table (~8,900), and the index on `bookstores.geom` means you are using that index now as well:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-EXPLAIN WITH loon_habitat AS (SELECT st_convexhull(st_collect(routes.geom)) AS geom FROM birds.birds, birds.observations, birds.routes WHERE birds.name = 'Common Loon' AND birds.id = observations.bird_id AND observations.route_id = routes.id), loon_bookstores AS (SELECT bookstores.name, address, bookstores.geom AS geom FROM bookstores, loon_habitat WHERE st_contains(loon_habitat.geom, bookstores.geom)) SELECT loon_bookstores.name, address, sum(roads.miles)::INT8 AS nearby_road_miles FROM roads, loon_bookstores WHERE roads.state = 'NY' AND st_distance(roads.geom, loon_bookstores.geom) < (10 / 69) GROUP BY loon_bookstores.name, address ORDER BY nearby_road_miles ASC;
-~~~
-
-~~~
- tree | field | description
----------------------------------------------------------------------+-----------------------+-------------------------------------------------------------------------
- | distribution | full
- | vectorized | true
- sort | |
- │ | order | +nearby_road_miles
- └── render | |
- └── group | |
- │ | group by | name, address
- └── cross join | |
- │ | pred | st_dwithinexclusive(geom, geom, 0.14492753623188406)
- ├── index join | |
- │ │ | table | roads@primary
- │ └── scan | |
- │ | estimated row count | 8899
- │ | table | roads@roads_state_idx
- │ | spans | [/'NY' - /'NY']
- └── render | |
- └── lookup join | |
- │ | table | bookstores@primary
- │ | equality | (id) = (id)
- │ | equality cols are key |
- │ | pred | st_contains(geom, geom)
- └── inverted join | |
- │ | table | bookstores@bookstores_geom_idx
- └── render | |
- └── group (scalar) | |
- └── hash join | |
- │ | equality | (route_id) = (id)
- │ | right cols are key |
- ├── lookup join | |
- │ │ | table | observations@primary
- │ │ | equality | (id) = (id)
- │ │ | equality cols are key |
- │ └── lookup join | |
- │ │ | table | observations@observations_bird_id_idx
- │ │ | equality | (id) = (bird_id)
- │ └── scan | |
- │ | estimated row count | 1
- │ | table | birds@birds_name_idx
- │ | spans | [/'Common Loon' - /'Common Loon']
- └── scan | |
- | estimated row count | 129
- | table | routes@primary
- | spans | FULL SCAN
-~~~
-
-## Step 8. Find other birds
-
-### (18) What are the hawks sighted in the same region as the Common Loon?
-
-If you get tired of looking for Common Loons (and it's understandable that you might), you may want to widen your search to other types of birds. You have already looked at [different bird species based on frequency of observation][q_13]. Now you turn your attention to a specific type of bird: hawks.
-
-Due to your long birdwatching experience, you recall that hawks are in the family "Accipitridae". You can use this knowledge to answer the question: What hawks live in the same region as the Common Loon in New York State?
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above subquery with [the `birds` database](#the-birds-database) to find the names and observation counts of the birds that:
- 1. Are in the family "Accipitridae".
- 1. Have sighting locations whose geometry is contained by the hull describing the Common Loon's habitat.
-1. Order the birds in the list by how frequently they are sighted, since you may want to look for the most common hawks first.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_collect(routes.geom) AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- birds.name, sum(birds.observations.count) AS sightings
-FROM
- birds.birds,
- birds.observations,
- birds.routes,
- loon_habitat
-WHERE
- birds.family = 'Accipitridae'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- AND st_contains(loon_habitat.geom, routes.geom)
-GROUP BY
- birds.name
-ORDER BY
- sightings DESC;
-~~~
-
-Based on this data, it looks like the most commonly sighted hawk by far in the area over the years 2000-2019 is the Broad-winged Hawk:
-
-~~~
- name | sightings
------------------------+------------
- Broad-winged Hawk | 115
- Red-tailed Hawk | 33
- Northern Harrier | 12
- Red-shouldered Hawk | 11
- Sharp-shinned Hawk | 9
- Bald Eagle | 9
- Cooper Hawk | 7
- Northern Goshawk | 5
- unid. Accipiter hawk | 1
-(9 rows)
-~~~
-
-### (19) What if you also want to look for owls as well as hawks?
-
-If you are a fan of owls as well as hawks, you can make several updates to [the previous query][q_18], which just looked for hawks. In particular, you want to group the hawks together and the owls together.
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above subquery with [the `birds` database](#the-birds-database) to find the names and observation counts of the birds that:
- 1. Are in the family "Accipitridae" (hawks) _or_ the family "Strigidae" (owls).
- 1. Have sighting locations whose geometry is contained by the hull describing the Common Loon's habitat.
-1. Group the birds by name and family, and within each grouping order the birds by how frequently they are sighted, since you may want to look for the most common hawks or owls first.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_collect(routes.geom) AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- birds.name,
- birds.family,
- sum(birds.observations.count) AS sightings
-FROM
- birds.birds,
- birds.observations,
- birds.routes,
- loon_habitat
-WHERE
- (
- birds.family = 'Accipitridae'
- OR birds.family = 'Strigidae'
- )
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- AND st_contains(loon_habitat.geom, routes.geom)
-GROUP BY
- birds.name, birds.family
-ORDER BY
- birds.family, sightings DESC;
-~~~
-
-~~~
- name | family | sightings
-------------------------+--------------+------------
- Broad-winged Hawk | Accipitridae | 115
- Red-tailed Hawk | Accipitridae | 33
- Northern Harrier | Accipitridae | 12
- Red-shouldered Hawk | Accipitridae | 11
- Sharp-shinned Hawk | Accipitridae | 9
- Bald Eagle | Accipitridae | 9
- Cooper Hawk | Accipitridae | 7
- Northern Goshawk | Accipitridae | 5
- unid. Accipiter hawk | Accipitridae | 1
- Barred Owl | Strigidae | 44
- Eastern Screech-Owl | Strigidae | 3
- Great Horned Owl | Strigidae | 1
- Northern Saw-whet Owl | Strigidae | 1
-(13 rows)
-~~~
-
-## Next steps
-
-Now that you are familiar with writing and tuning spatial queries, you are ready for the following steps:
-
-- Learn more details about the [spatial features]({% link {{ page.version.version }}/spatial-data-overview.md %}) supported by CockroachDB.
-
-- Learn how to migrate spatial data from [shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %}), [GeoPackages]({% link {{ page.version.version }}/migrate-from-geopackage.md %}), [GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %}), or [OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %}) into CockroachDB.
-
-- Learn how CockroachDB's [spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %}) work.
-
-## Appendix
-
-### Spatial features and corresponding queries
-
-The queries are presented above in a "narrative order" that corresponds roughly to the order in which you might ask questions about bookstores and loon habitat as you plan for your vacation.
-
-However, you may just want to see what queries are exercising a specific [spatial feature]({% link {{ page.version.version }}/spatial-data-overview.md %}) supported by CockroachDB. The table below provides a mapping from a feature (such as 'spatial indexing' or 'spatial joins') to the queries that use that feature.
-
-| Feature | Queries | Description(s) |
-|------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Spatial data set preparation | N/A | For information about how this data set was prepared, see the relevant portions of the [Data set description](#data-set-description). |
-| Loading spatial data | N/A | For loading instructions on loading the data set, see [Loading the data set](#step-3-load-the-data-set). |
-| Spatial joins | [#6][q_06], [#7][q_07], [#8][q_08], [#9][q_09], [#10][q_10], [#13][q_13], [#14][q_14], [#15][q_15], [#16][q_16], [#17][q_17], [#18][q_18], [#19][q_19] | Joins that involve comparing spatial features from two or more tables, usually with a predicate such as [`ST_Contains`]({% link {{ page.version.version }}/st_contains.md %}). |
-| Spatial indexing | [#15][q_15], [#17][q_17] | These queries show explicit spatial index creation; many of the spatial join queries listed above could use these indexes, once created. |
-| Spatial relationships | [#6][q_06], [#7][q_07], [#8][q_08], [#9][q_09], [#10][q_10], [#13][q_13], [#14][q_14], [#15][q_15], [#16][q_16], [#17][q_17], [#18][q_18], [#19][q_19] | Comparing spatial relationships with predicates such as [`ST_Contains`]({% link {{ page.version.version }}/st_contains.md %}) and `ST_DWithin`. |
-| Nearest-neighbor searching | [#15][q_15] | This query shows how to execute a nearest-neighbor search using CockroachDB. |
-
-### Data set description
-
-This section contains an overview of the data sets and how they were constructed and put together. Because this data set is a "compound data set" that is built from several other data sets, there is some complexity to the schema that you will have to navigate.
-
-The data set contains two databases:
-
-- [The `tutorial` database](#the-tutorial-database)
-- [The `birds` database](#the-birds-database)
-
-#### The `tutorial` database
-
-The `tutorial` database contains the following tables:
-
-- [The `bookstores` table](#the-bookstores-and-bookstore_routes-tables) stores the independent bookstore information such as name, address, geo-location, etc.
-- [The `bookstore_routes` table](#the-bookstores-and-bookstore_routes-tables) is a small table containing just the routes between the local bookstores that lie within the loon's Adirondack habitat (this will be explained in more detail below).
-- [The `roads` table](#the-roads-table) is where the U.S. National Atlas road data is kept.
-
-##### The `bookstores` and `bookstore_routes` tables
-
-Below is an entity-relationship diagram showing the `bookstores` and `bookstore_routes` tables (generated using [DBeaver]({% link {{ page.version.version }}/dbeaver.md %})):
-
-
-
-As mentioned above, the `bookstores` table was created by scraping web data from the [American Booksellers Association website's member directory](https://bookweb.org/member_directory/search/ABAmember). In addition, the `geom` column was constructed by doing some [address geocoding](https://wikipedia.org/wiki/Address_geocoding) that converted each bookstore's address to a lon/lat pair and converted to a spatial object using `ST_MakePoint`. For each bookstore, the script did a bit of parsing and geocoding and ran essentially the following query:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
--- sqlchecker: ignore
-INSERT
-INTO
- bookstores
- (
- name,
- url,
- phone,
- address,
- street,
- city,
- state,
- zip,
- description,
- geom
- )
-VALUES
- (
- '$name',
- '$url',
- '$phone',
- '$address',
- '$street',
- '$city',
- '$state',
- '$zip',
- '$description',
- (
- SELECT
- st_setsrid(
- st_makepoint('$lon', '$lat'),
- 4326
- )
- )
- );
-~~~
-
-Similarly, the paths (specifically [MultiLinestrings]({% link {{ page.version.version }}/multilinestring.md %})) between bookstores in the `bookstore_routes` table were also calculated using a script that made use of a simple greedy algorithm to find paths between the bookstores using the `roads` table (described below). The algorithm always looks for the next road that will bring you closer to the destination. It is fine for this small use case, but it is not robust enough for any kind of production use, since it does not perform any backtracking when it gets stuck in a local optimum. The algorithm works as follows, and assumes a starting bookstore _A_ and an ending bookstore _B_:
-
-1. (Start) Find the road nearest _A_; call this road _R_A.
-1. Find the road nearest the destination _B_; call this road _R_B.
-1. Do _R_A and _R_B intersect? If so, the algorithm is done. You have arrived at your destination. (This is the recursive base case.)
-1. Otherwise, find the roads that intersect with _R_A. Of these roads, choose the one that passes closest to _R_B. Call it _R_A'.
-1. Do _R_A' and _R_B intersect? If so, the algorithm is done. You have arrived at your destination.
-1. If the algorithm has arrived at this step without finishing, it goes back to step 4 and applies that step to the current "closest road to destination" _R_A', and keeps operating steps 4-6 recursively until it has found a complete path or it hits some kind of heuristic timeout.
-
-{{site.data.alerts.callout_info}}
-For more information about production quality map routing software that uses OpenStreetMap, see [the OpenStreetMap wiki page on Routing](https://wiki.openstreetmap.org/wiki/Routing).
-{{site.data.alerts.end}}
-
-{{site.data.alerts.callout_info}}
-There are multiple ways to do geocoding. You can use REST API-based services or do the geocoding yourself by processing [TIGER/LINE address data](https://www2.census.gov/geo/tiger/tigerua/) using a library for that purpose. For more information about some of the options available to you, see [this OpenStreetMap wiki page on geocoders](https://wiki.openstreetmap.org/wiki/Search_engines) or [this list of geocoding software](http://www.tsusiatsoftware.net/geocode.html).
-{{site.data.alerts.end}}
-
-##### The `roads` table
-
-Meanwhile, the `roads` table has many columns; the most important ones used in this tutorial are `state`, `geom`, `miles`, and `prime_name` (the human-readable name of the road).
-
-
-
-For more information about what the other columns in `roads` mean, see the [full data set description](https://www.sciencebase.gov/catalog/file/get/581d052be4b08da350d524ce?f=__disk__60%2F6b%2F4e%2F606b4e564884da8cca57ffeb229cd817006616e0&transform=1&allowOpen=true).
-
-{{site.data.alerts.callout_info}}
-The `roads` table was imported from a [shapefile](https://prd-tnm.s3.amazonaws.com/StagedProducts/Small-scale/data/Transportation/roadtrl010g.shp_nt00920.tar.gz) using the method described in [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %}).
-{{site.data.alerts.end}}
-
-#### The `birds` database
-
-The `birds` database contains several tables that you will have to [join]({% link {{ page.version.version }}/joins.md %}) together in most of the queries shown elsewhere on this page. The multi-table design reflects [the schema of the actual data set](https://www.sciencebase.gov/catalog/file/get/5ea04e9a82cefae35a129d65?f=__disk__b4%2F2f%2Fcf%2Fb42fcfe28a799db6e8c97200829ea1ebaccbf8ea&transform=1&allowOpen=true), which is split across files that map to these tables.
-
-The tables in the `birds` database are diagrammed below:
-
-- `birds` is a list of ~750 bird species. Most queries will use the `name` column.
-- `routes` is a list of ~130 prescribed locations that the birdwatchers helping with the survey visit each year. The `geom` associated with each route is a [Point]({% link {{ page.version.version }}/point.md %}) marking the latitude and longitude of the route's starting point. For details, see the [schema](https://www.sciencebase.gov/catalog/file/get/5ea04e9a82cefae35a129d65?f=__disk__b4%2F2f%2Fcf%2Fb42fcfe28a799db6e8c97200829ea1ebaccbf8ea&transform=1&allowOpen=true) (search for the text "routes.csv").
-- `observations` describes the ~85,000 times and places in which birds of various species were actually seen. The `bird_id` is a [foreign key]({% link {{ page.version.version }}/foreign-key.md %}) to the ID in the `birds` table, and the `route_id` points to the ID of the `routes` table.
-
-
-
-Each of these tables were populated using a script that parsed [the CSV files available for download](https://www.sciencebase.gov/catalog/item/52b1dfa8e4b0d9b325230cd9) and added the data using [`INSERT`]({% link {{ page.version.version }}/insert.md %}) statements. For the `routes` table, once again the `ST_MakePoint` function was used to create a geometry from the lon/lat values in the CSV as follows:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
--- sqlchecker: ignore
-INSERT
-INTO
- routes (id, name, country, state, geom)
-VALUES
- (
- '$route_id',
- '$route_name',
- '$country',
- '$state',
- (
- SELECT
- st_setsrid(
- st_makepoint('$lon', '$lat'),
- 4326
- )
- )
- );
-~~~
-
-{{site.data.alerts.callout_info}}
-This data is stored in a separate `birds` database due to the fact that it is split into several tables. It could also have been added to the `tutorial` database by naming the tables something like `bird_species`, `bird_routes`, and `bird_observations`.
-{{site.data.alerts.end}}
-
-## See also
-
-- [Install CockroachDB]({% link {{ page.version.version }}/install-cockroachdb.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial Data Overview]({% link {{ page.version.version }}/spatial-data-overview.md %})
-- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
-- [Spatial & GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
-- [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %})
-- [Migrate from GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %})
-- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
-- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
-- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
-- [POINT]({% link {{ page.version.version }}/point.md %})
-- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
-- [POLYGON]({% link {{ page.version.version }}/polygon.md %})
-- [MULTIPOINT]({% link {{ page.version.version }}/multipoint.md %})
-- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
-- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
-- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Well known text]({% link {{ page.version.version }}/well-known-text.md %})
-- [Well known binary]({% link {{ page.version.version }}/well-known-binary.md %})
-- [GeoJSON]({% link {{ page.version.version }}/geojson.md %})
-- [SRID 4326 - longitude and latitude]({% link {{ page.version.version }}/srid-4326.md %})
-- [`ST_Contains`]({% link {{ page.version.version }}/st_contains.md %})
-- [`ST_ConvexHull`]({% link {{ page.version.version }}/st_convexhull.md %})
-- [`ST_CoveredBy`]({% link {{ page.version.version }}/st_coveredby.md %})
-- [`ST_Covers`]({% link {{ page.version.version }}/st_covers.md %})
-- [`ST_Disjoint`]({% link {{ page.version.version }}/st_disjoint.md %})
-- [`ST_Equals`]({% link {{ page.version.version }}/st_equals.md %})
-- [`ST_Intersects`]({% link {{ page.version.version }}/st_intersects.md %})
-- [`ST_Overlaps`]({% link {{ page.version.version }}/st_overlaps.md %})
-- [`ST_Touches`]({% link {{ page.version.version }}/st_touches.md %})
-- [`ST_Union`]({% link {{ page.version.version }}/st_union.md %})
-- [`ST_Within`]({% link {{ page.version.version }}/st_within.md %})
-- [Troubleshooting overview]({% link {{ page.version.version }}/troubleshooting-overview.md %})
-- [Support resources]({% link {{ page.version.version }}/support-resources.md %})
-
-{% comment %} Reference links {% endcomment %}
-
-[q_01]: #1-where-has-the-common-loon-been-sighted-by-the-nabbs-in-the-years-2000-2019-in-ny-state
-[q_02]: #2-what-is-the-total-area-of-loon-sightings
-[q_03]: #3-how-many-loon-sightings-have-there-been-in-ny-state-in-the-years-2000-2019
-[q_04]: #4-how-many-loon-sightings-were-there-in-ny-state-in-just-the-year-2019
-[q_05]: #5-what-is-the-density-of-loon-sightings-per-square-mile-in-its-range-as-defined-by-this-data-set-in-2019
-[q_06]: #6-what-are-the-bookstores-that-lie-within-the-loons-habitat-range-in-ny-state
-[q_07]: #7-how-many-different-species-of-bird-habitats-contain-the-location-of-the-book-nook-in-saranac-lake-ny
-[q_08]: #8-which-25-birds-were-most-often-sighted-within-10-miles-of-the-book-nook-in-saranac-lake-ny-during-the-2000-2019-observation-period
-[q_09]: #9-what-does-the-shape-of-all-bookstore-locations-that-lie-within-the-loons-habitat-look-like
-[q_10]: #10-what-is-the-area-of-the-shape-of-all-bookstore-locations-that-are-in-the-loons-habitat-range-within-ny-state
-[q_11]: #11-how-long-is-the-route-from-mysteries-on-main-street-in-johnstown-ny-to-the-book-nook-in-saranac-lake-ny
-[q_12]: #12-what-does-the-route-from-mysteries-on-main-street-in-johnstown-ny-to-the-book-nook-in-saranac-lake-ny-look-like
-[q_13]: #13-what-were-the-25-most-commonly-sighted-birds-in-2019-within-10-miles-of-the-route-between-mysteries-on-main-street-in-johnstown-ny-and-the-bookstore-plus-in-lake-placid-ny
-[q_14]: #14-what-are-the-25-least-often-sighted-other-species-of-birds-in-the-loons-sightings-range
-[q_15]: #15-what-are-the-top-10-roads-nearest-to-a-loon-sighting-location-in-ny
-[q_16]: #16-how-many-miles-of-roads-are-contained-within-the-portion-of-the-loon-habitat-that-lies-within-ny-state
-[q_17]: #17-which-bookstore-in-the-loons-region-in-ny-has-the-fewest-miles-of-roads-within-a-10-mile-radius-of-the-store
-[q_18]: #18-what-are-the-hawks-sighted-in-the-same-region-as-the-common-loon
-[q_19]: #19-what-if-you-also-want-to-look-for-owls-as-well-as-hawks
-
-{% comment %} EOF {% endcomment %}
diff --git a/src/current/v26.1/srid-4326.md b/src/current/v26.1/srid-4326.md
index 8cad5bd5680..fac33a2eabc 100644
--- a/src/current/v26.1/srid-4326.md
+++ b/src/current/v26.1/srid-4326.md
@@ -118,11 +118,9 @@ ERROR: st_contains(): operation on mixed SRIDs forbidden: (Point, 0) != (Point,
## See also
- [Spatial Data Overview]({% link {{ page.version.version }}/spatial-data-overview.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Well known text]({% link {{ page.version.version }}/well-known-text.md %})
- [Well known binary]({% link {{ page.version.version }}/well-known-binary.md %})
- [GeoJSON]({% link {{ page.version.version }}/geojson.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/st_contains.md b/src/current/v26.1/st_contains.md
index 441159a90a3..fdd0b9aa455 100644
--- a/src/current/v26.1/st_contains.md
+++ b/src/current/v26.1/st_contains.md
@@ -79,8 +79,6 @@ SELECT st_contains(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95
## See also
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -101,4 +99,3 @@ SELECT st_contains(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/st_convexhull.md b/src/current/v26.1/st_convexhull.md
index 53bf0ce5a7c..f93309a4c49 100644
--- a/src/current/v26.1/st_convexhull.md
+++ b/src/current/v26.1/st_convexhull.md
@@ -246,8 +246,6 @@ In this example, we will generate the convex hull of a single geometry. The geo
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -268,4 +266,3 @@ In this example, we will generate the convex hull of a single geometry. The geo
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/st_coveredby.md b/src/current/v26.1/st_coveredby.md
index d5f49a62860..b657a86f46d 100644
--- a/src/current/v26.1/st_coveredby.md
+++ b/src/current/v26.1/st_coveredby.md
@@ -72,8 +72,6 @@ SELECT ST_CoveredBy(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -9
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -94,4 +92,3 @@ SELECT ST_CoveredBy(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -9
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/st_covers.md b/src/current/v26.1/st_covers.md
index bc066436e4c..7e45a3ef821 100644
--- a/src/current/v26.1/st_covers.md
+++ b/src/current/v26.1/st_covers.md
@@ -74,8 +74,6 @@ SELECT ST_Covers(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -95,4 +93,3 @@ SELECT ST_Covers(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/st_disjoint.md b/src/current/v26.1/st_disjoint.md
index 7bb0a5bb550..a19d50792e7 100644
--- a/src/current/v26.1/st_disjoint.md
+++ b/src/current/v26.1/st_disjoint.md
@@ -73,8 +73,6 @@ SELECT st_disjoint(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -95,4 +93,3 @@ SELECT st_disjoint(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/st_equals.md b/src/current/v26.1/st_equals.md
index c5cdaeccdb8..f417f71b9c3 100644
--- a/src/current/v26.1/st_equals.md
+++ b/src/current/v26.1/st_equals.md
@@ -69,8 +69,6 @@ SELECT st_equals(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -91,4 +89,3 @@ SELECT st_equals(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/st_intersects.md b/src/current/v26.1/st_intersects.md
index 783f996fbbd..1e7ef725b30 100644
--- a/src/current/v26.1/st_intersects.md
+++ b/src/current/v26.1/st_intersects.md
@@ -70,8 +70,6 @@ SELECT st_intersects(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -92,4 +90,3 @@ SELECT st_intersects(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/st_overlaps.md b/src/current/v26.1/st_overlaps.md
index 6cb5faccbbd..e1f93a35306 100644
--- a/src/current/v26.1/st_overlaps.md
+++ b/src/current/v26.1/st_overlaps.md
@@ -71,8 +71,6 @@ SELECT st_overlaps(st_geomfromtext('SRID=4326;POLYGON((-79.995888 40.440624,-74.
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -93,4 +91,3 @@ SELECT st_overlaps(st_geomfromtext('SRID=4326;POLYGON((-79.995888 40.440624,-74.
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/st_touches.md b/src/current/v26.1/st_touches.md
index 54c5e17012c..9eda982164a 100644
--- a/src/current/v26.1/st_touches.md
+++ b/src/current/v26.1/st_touches.md
@@ -71,8 +71,6 @@ SELECT st_touches(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -93,4 +91,3 @@ SELECT st_touches(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/st_union.md b/src/current/v26.1/st_union.md
index 6324b094065..c49acc60e30 100644
--- a/src/current/v26.1/st_union.md
+++ b/src/current/v26.1/st_union.md
@@ -247,8 +247,6 @@ In this example, we will generate a single geometry from many individual points
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -269,4 +267,3 @@ In this example, we will generate a single geometry from many individual points
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/st_within.md b/src/current/v26.1/st_within.md
index ac1f5c71b39..c28288fb503 100644
--- a/src/current/v26.1/st_within.md
+++ b/src/current/v26.1/st_within.md
@@ -77,10 +77,7 @@ SELECT ST_Within(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
-- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
- [`ST_Covers`]({% link {{ page.version.version }}/st_covers.md %})
- [`ST_CoveredBy`]({% link {{ page.version.version }}/st_coveredby.md %})
@@ -99,4 +96,3 @@ SELECT ST_Within(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.1/start-a-local-cluster-in-docker-linux.md b/src/current/v26.1/start-a-local-cluster-in-docker-linux.md
index 5cc91fc7ab0..fe34cbaa255 100644
--- a/src/current/v26.1/start-a-local-cluster-in-docker-linux.md
+++ b/src/current/v26.1/start-a-local-cluster-in-docker-linux.md
@@ -37,4 +37,4 @@ Once you've [installed the official CockroachDB Docker image]({% link {{ page.ve
- [Create a CockroachDB Cloud account](https://cockroachlabs.cloud/signup?experience=enterprise) where you can [generate and manage licenses]({% link {{ page.version.version }}/licensing-faqs.md %}) for CockroachDB installations
- Learn more about [CockroachDB SQL]({% link {{ page.version.version }}/learn-cockroachdb-sql.md %}) and the [built-in SQL client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [Install the client driver]({% link {{ page.version.version }}/install-client-drivers.md %}) for your preferred language
-- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
+- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/jsonb.md %})
diff --git a/src/current/v26.1/start-a-local-cluster-in-docker-mac.md b/src/current/v26.1/start-a-local-cluster-in-docker-mac.md
index e1d43d8140c..8dabc54bc70 100644
--- a/src/current/v26.1/start-a-local-cluster-in-docker-mac.md
+++ b/src/current/v26.1/start-a-local-cluster-in-docker-mac.md
@@ -34,4 +34,4 @@ Once you've [installed the official CockroachDB Docker image]({% link {{ page.ve
- [Create a CockroachDB Cloud account](https://cockroachlabs.cloud/signup?experience=enterprise) where you can [generate and manage licenses]({% link {{ page.version.version }}/licensing-faqs.md %}) for CockroachDB installations
- Learn more about [CockroachDB SQL]({% link {{ page.version.version }}/learn-cockroachdb-sql.md %}) and the [built-in SQL client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [Install the client driver]({% link {{ page.version.version }}/install-client-drivers.md %}) for your preferred language
-- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
+- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/jsonb.md %})
diff --git a/src/current/v26.1/start-a-local-cluster-in-docker-windows.md b/src/current/v26.1/start-a-local-cluster-in-docker-windows.md
index 83877ae9735..97816cee25f 100644
--- a/src/current/v26.1/start-a-local-cluster-in-docker-windows.md
+++ b/src/current/v26.1/start-a-local-cluster-in-docker-windows.md
@@ -366,4 +366,4 @@ The CockroachDB [DB Console]({% link {{ page.version.version }}/ui-overview.md %
- [Create a CockroachDB Cloud account](https://cockroachlabs.cloud/signup?experience=enterprise) where you can [generate and manage licenses]({% link {{ page.version.version }}/licensing-faqs.md %}) for CockroachDB installations
- Learn more about [CockroachDB SQL]({% link {{ page.version.version }}/learn-cockroachdb-sql.md %}) and the [built-in SQL client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [Install the client driver]({% link {{ page.version.version }}/install-client-drivers.md %}) for your preferred language
-- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
+- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/jsonb.md %})
diff --git a/src/current/v26.1/start-a-local-cluster.md b/src/current/v26.1/start-a-local-cluster.md
index 9e780a9a1da..86ab9aa356e 100644
--- a/src/current/v26.1/start-a-local-cluster.md
+++ b/src/current/v26.1/start-a-local-cluster.md
@@ -389,4 +389,4 @@ Adding capacity is as simple as starting more nodes with `cockroach start`.
- [Create a CockroachDB Cloud account](https://cockroachlabs.cloud/signup?experience=enterprise) where you can [generate and manage licenses]({% link {{ page.version.version }}/licensing-faqs.md %}) for CockroachDB installations
- [Install the client driver]({% link {{ page.version.version }}/install-client-drivers.md %}) for your preferred language
- Learn more about [CockroachDB SQL]({% link {{ page.version.version }}/learn-cockroachdb-sql.md %}) and the [built-in SQL client]({% link {{ page.version.version }}/cockroach-sql.md %})
-- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
+- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/jsonb.md %})
diff --git a/src/current/v26.1/update-data.md b/src/current/v26.1/update-data.md
deleted file mode 100644
index f78c16677d0..00000000000
--- a/src/current/v26.1/update-data.md
+++ /dev/null
@@ -1,436 +0,0 @@
----
-title: Update Data
-summary: How to update the data in CockroachDB from various programming languages
-toc: true
-docs_area: develop
----
-
-This page has instructions for updating existing rows of data in CockroachDB, using the following [SQL statements]({% link {{ page.version.version }}/sql-statements.md %}):
-
-- [`UPDATE`](#use-update), which updates existing rows in a table.
-- [`UPSERT`](#use-upsert), which inserts new rows in a table, updating existing rows that conflict on a primary key.
-- [`INSERT ... ON CONFLICT ... DO UPDATE`](#use-insert-on-conflict), which inserts new rows in a table, updating existing rows in the event of a conflict on specified, `UNIQUE`-constrained columns.
-
-## Before you begin
-
-Before reading this page, do the following:
-
-- [Create a CockroachDB {{ site.data.products.standard }} cluster]({% link cockroachcloud/quickstart.md %}) or [start a local cluster]({% link cockroachcloud/quickstart.md %}?filters=local).
-- [Install a Driver or ORM Framework]({% link {{ page.version.version }}/install-client-drivers.md %}).
-- [Connect to the database]({% link {{ page.version.version }}/connect-to-the-database.md %}).
-- [Create a database schema]({% link {{ page.version.version }}/schema-design-overview.md %}).
-- [Insert data]({% link {{ page.version.version }}/insert-data.md %}) that you now want to update.
-
- In the examples on this page, we use sample [`movr`]({% link {{ page.version.version }}/movr.md %}) data imported with the [`cockroach workload` command]({% link {{ page.version.version }}/cockroach-workload.md %}).
-
-## Use `UPDATE`
-
-To update existing rows in a table, use an [`UPDATE` statement]({% link {{ page.version.version }}/update.md %}) with a `WHERE` clause that filters on the columns that identify the rows that you want to update.
-
-{{site.data.alerts.callout_info}}
-To update a large number of rows (i.e., tens of thousands of rows or more), we recommend iteratively updating subsets of the rows that you want to update, until all of the rows have been updated. You can write a script to do this, or you can write a loop into your application.
-
-For guidance and an example, see [Bulk-update Data]({% link {{ page.version.version }}/bulk-update-data.md %}).
-{{site.data.alerts.end}}
-
-### `UPDATE` SQL syntax
-
-In SQL, `UPDATE` statements generally take the following form:
-
-~~~
-UPDATE {table} SET {update_column} = {update_value} WHERE {filter_column} = {filter_value}
-~~~
-
-Where:
-
-- `{table}` is the table to update.
-- `{update_column}` is the column to update.
-- `{update_value}` is the new value of the column in the matching row.
-- `{filter_column}` is the column to filter on.
-- `{filter_value}` is the matching value for the filter.
-
-{{site.data.alerts.callout_success}}
-For detailed reference documentation on the `UPDATE` statement, including additional examples, see the [`UPDATE` syntax page]({% link {{ page.version.version }}/update.md %}).
-{{site.data.alerts.end}}
-
-### `UPDATE` best practices
-
-Here are some best practices to follow when updating rows:
-
-- Always specify a `WHERE` clause in `UPDATE` queries. If no `WHERE` clause is specified, CockroachDB will update all of the rows in the specified table.
-- To update a large number of rows (i.e., tens of thousands of rows or more), use a [batch-update loop]({% link {{ page.version.version }}/bulk-update-data.md %}).
-- When executing `UPDATE` statements from an application, make sure that you wrap the SQL-executing functions in [a retry loop that handles transaction errors]({% link {{ page.version.version }}/query-behavior-troubleshooting.md %}#transaction-retry-errors) that can occur under [contention]({% link {{ page.version.version }}/performance-best-practices-overview.md %}#transaction-contention).
-
-### `UPDATE` example
-
-Suppose you want to change the status of all of the vehicles owned by a particular MovR user. To update all rows in the `vehicles` table with the `owner_id` equal to `bd70a3d7-0a3d-4000-8000-000000000025`:
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-## Step 7. Add 3 nodes on "cloud 2"
-
-At this point, you're running three nodes on cloud 1. But what if you'd like to start experimenting with resources provided by another cloud vendor? Let's try that by adding three more nodes to a new cloud platform. Again, the flag to note is [`--locality`]({% link {{ page.version.version }}/configure-replication-zones.md %}#descriptive-attributes-assigned-to-nodes), which you're using to specify that these next 3 nodes are running on cloud 2.
-
-In a new terminal, start node 4 on cloud 2:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ cockroach start \
---insecure \
---locality=cloud=2 \
---store=cloud2node4 \
---listen-addr=localhost:26260 \
---http-addr=localhost:8083 \
---cache=100MB \
---join=localhost:26257,localhost:26258,localhost:26259
-~~~
-
-In a new terminal, start node 5 on cloud 2:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ cockroach start \
---insecure \
---locality=cloud=2 \
---store=cloud2node5 \
---advertise-addr=localhost:26261 \
---http-addr=localhost:8084 \
---cache=100MB \
---join=localhost:26257,localhost:26258,localhost:26259
-~~~
-
-In a new terminal, start node 6 on cloud 2:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ cockroach start \
---insecure \
---locality=cloud=2 \
---store=cloud2node6 \
---advertise-addr=localhost:26262 \
---http-addr=localhost:8085 \
---cache=100MB \
---join=localhost:26257,localhost:26258,localhost:26259
-~~~
-
-## Step 8. Watch data balance across all 6 nodes
-
-Back on the **Overview** dashboard in DB Console, hover over the **Replicas per Node** graph again. Because you used [`--locality`]({% link {{ page.version.version }}/configure-replication-zones.md %}#descriptive-attributes-assigned-to-nodes) to specify that nodes are running on 2 clouds, you'll see an approximately even number of replicas on each node, indicating that CockroachDB has automatically rebalanced replicas across both simulated clouds:
-
-
-
-Note that it takes a few minutes for the DB Console to show accurate per-node replica counts on hover. This is why the new nodes in the screenshot above show 0 replicas. However, the graph lines are accurate, and you can click **View node list** in the **Summary** area for accurate per-node replica counts as well.
-
-## Step 9. Migrate all data to "cloud 2"
-
-So your cluster is replicating across two simulated clouds. But let's say that after experimentation, you're happy with cloud vendor 2, and you decide that you'd like to move everything there. Can you do that without interruption to your live client traffic? Yes, and it's as simple as running a single command to add a [hard constraint]({% link {{ page.version.version }}/configure-replication-zones.md %}#replication-constraints) that all replicas must be on nodes with `--locality=cloud=2`.
-
-In a new terminal, [edit the default replication zone]({% link {{ page.version.version }}/alter-range.md %}#configure-zone):
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ cockroach sql --execute="ALTER RANGE default CONFIGURE ZONE USING constraints='[+cloud=2]';" --insecure --host=localhost:26257
-~~~
-
-## Step 10. Verify the data migration
-
-Back on the **Overview** dashboard in the DB Console, hover over the **Replicas per Node** graph again. Very soon, you'll see the replica count double on nodes 4, 5, and 6 and drop to 0 on nodes 1, 2, and 3:
-
-
-
-This indicates that all data has been migrated from cloud 1 to cloud 2. In a real cloud migration scenario, at this point you would update the load balancer to point to the nodes on cloud 2 and then stop the nodes on cloud 1. But for the purpose of this local simulation, there's no need to do that.
-
-## Step 11. Stop the cluster
-
-Once you're done with your cluster, stop YCSB by switching into its terminal and pressing **CTRL-C**. Then do the same for HAProxy and each CockroachDB node.
-
-{{site.data.alerts.callout_success}}For the last node, the shutdown process will take longer (about a minute) and will eventually force stop the node. This is because, with only 1 node still online, a majority of replicas are no longer available (2 of 3), and so the cluster is not operational. To speed up the process, press CTRL-C a second time.{{site.data.alerts.end}}
-
-If you do not plan to restart the cluster, you may want to remove the nodes' data stores and the HAProxy config file:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ rm -rf cloud1node1 cloud1node2 cloud1node3 cloud2node4 cloud2node5 cloud2node6 haproxy.cfg
-~~~
-
-## What's next?
-
-Explore other CockroachDB benefits and features:
-
-{% include {{ page.version.version }}/misc/explore-benefits-see-also.md %}
-
-You may also want to learn other ways to control the location and number of replicas in a cluster:
-
-- [Even Replication Across Datacenters]({% link {{ page.version.version }}/configure-replication-zones.md %}#even-replication-across-availability-zones)
-- [Multiple Applications Writing to Different Databases]({% link {{ page.version.version }}/configure-replication-zones.md %}#multiple-applications-writing-to-different-databases)
-- [Stricter Replication for a Table and Its Indexes]({% link {{ page.version.version }}/configure-replication-zones.md %}#stricter-replication-for-a-table-and-its-secondary-indexes)
-- [Tweaking the Replication of System Ranges]({% link {{ page.version.version }}/configure-replication-zones.md %}#tweaking-the-replication-of-system-ranges)
diff --git a/src/current/v26.2/demo-json-support.md b/src/current/v26.2/demo-json-support.md
deleted file mode 100644
index 9678a98a162..00000000000
--- a/src/current/v26.2/demo-json-support.md
+++ /dev/null
@@ -1,291 +0,0 @@
----
-title: JSON Support
-summary: Use a local cluster to explore how CockroachDB can store and query unstructured JSONB data.
-toc: true
-keywords: gin, gin index, gin indexes, inverted index, inverted indexes, accelerated index, accelerated indexes
-docs_area: deploy
----
-
-This page guides you through a simple demonstration of how CockroachDB can store and query unstructured [`JSONB`]({% link {{ page.version.version }}/jsonb.md %}) data from a third-party API, as well as how a [GIN index]({% link {{ page.version.version }}/inverted-indexes.md %}) can optimize your queries.
-
-
-
-## Step 1. Install prerequisites
-
-
-
-
-
-
-
-- Install the latest version of [CockroachDB]({% link {{ page.version.version }}/install-cockroachdb.md %}).
-- Install the latest version of [Go](https://golang.org/dl/): `brew install go`
-- Install the [PostgreSQL driver](https://github.com/lib/pq): `go get github.com/lib/pq`
-
-
-
-- Install the latest version of [CockroachDB]({% link {{ page.version.version }}/install-cockroachdb.md %}).
-- Install the [Python psycopg2 driver](http://initd.org/psycopg/docs/install.html): `pip install psycopg2`
-- Install the [Python Requests library](https://requests.readthedocs.io/): `pip install requests`
-
-
-## Step 2. Start a single-node cluster
-
-For the purpose of this tutorial, you need only one CockroachDB node running in insecure mode, so use the [`cockroach start-single-node`]({% link {{ page.version.version }}/cockroach-start-single-node.md %}) command in the foreground:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ cockroach start-single-node \
---insecure \
---store=json-test \
---listen-addr=localhost:26257 \
---http-addr=localhost:8080
-~~~
-
-## Step 3. Create a user
-
-In a new terminal window, open the [built-in SQL shell]({% link {{ page.version.version }}/cockroach-sql.md %}) and create a new SQL user, `maxroach`:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ cockroach sql --insecure --host=localhost:26257
-~~~
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-> CREATE USER maxroach;
-~~~
-
-## Step 4. Create a database and grant privileges
-
-Next, create a database called `jsonb_test`:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-> CREATE DATABASE jsonb_test;
-~~~
-
-Set the database as the default:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-> SET DATABASE = jsonb_test;
-~~~
-
-Then [grant privileges]({% link {{ page.version.version }}/grant.md %}) to the `maxroach` user:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-> GRANT ALL ON DATABASE jsonb_test TO maxroach;
-~~~
-
-## Step 5. Create a table
-
-Still in the SQL shell, create a table called `programming`:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-> CREATE TABLE programming (
- id UUID DEFAULT uuid_v4()::UUID PRIMARY KEY,
- posts JSONB
- );
-~~~
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-> SHOW CREATE TABLE programming;
-~~~
-
-~~~
- table_name | create_statement
-+-------------+------------------------------------------------+
- programming | CREATE TABLE programming (
- | id UUID NOT NULL DEFAULT uuid_v4()::UUID,
- | posts JSONB NULL,
- | CONSTRAINT "primary" PRIMARY KEY (id ASC),
- | FAMILY "primary" (id, posts)
- | )
-(1 row)
-~~~
-
-## Step 6. Run the code
-
-Now that you have a database, a SQL user, and a table, let's run code to insert rows into the table.
-
-
-
-
-
-
-json-sample.go file, or create the file yourself and copy the code into it:
-
-{% include_cached copy-clipboard.html %}
-~~~ go
-{% include {{ page.version.version }}/json/json-sample.go %}
-~~~
-
-In a new terminal window, navigate to your sample code file and run it:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ go run json-sample.go
-~~~
-
-The code queries the [Reddit API](https://www.reddit.com/dev/api/) for posts in [/r/programming](https://www.reddit.com/r/programming/). The Reddit API only returns 25 results per page; however, each page returns an `"after"` string that tells you how to get the next page. Therefore, the program does the following in a loop:
-
-1. Makes a request to the API.
-1. Inserts the results into the table and grabs the `"after"` string.
-1. Uses the new `"after"` string as the basis for the next request.
-json-sample.py file, or create the file yourself and copy the code into it:
-
-{% include_cached copy-clipboard.html %}
-~~~ python
-{% include {{ page.version.version }}/json/json-sample.py %}
-~~~
-
-In a new terminal window, navigate to your sample code file and run it:
-
-{% include_cached copy-clipboard.html %}
-~~~ shell
-$ python json-sample.py
-~~~
-
-The code queries the [Reddit API](https://www.reddit.com/dev/api/) for posts in [/r/programming](https://www.reddit.com/r/programming/). The Reddit API only returns 25 results per page; however, each page returns an `"after"` string that tells you how to get the next page. Therefore, the program does the following in a loop:
-
-1. Makes a request to the API.
-1. Grabs the `"after"` string.
-1. Inserts the results into the table.
-1. Uses the new `"after"` string as the basis for the next request.
-
-
-## See also
-
-- [Install CockroachDB]({% link {{ page.version.version }}/install-cockroachdb.md %})
-- [Spatial Data Overview]({% link {{ page.version.version }}/spatial-data-overview.md %})
-- [Spatial Indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
-- [Spatial & GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %})
-- [Migrate from GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %})
-- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
-- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
-- [Spatial Functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
-- [POINT]({% link {{ page.version.version }}/point.md %})
-- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
-- [POLYGON]({% link {{ page.version.version }}/polygon.md %})
-- [MULTIPOINT]({% link {{ page.version.version }}/multipoint.md %})
-- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
-- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
-- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Well Known Text]({% link {{ page.version.version }}/well-known-text.md %})
-- [Well Known Binary]({% link {{ page.version.version }}/well-known-binary.md %})
-- [GeoJSON]({% link {{ page.version.version }}/geojson.md %})
-- [SRID 4326 - Longitude and Latitude]({% link {{ page.version.version }}/srid-4326.md %})
-- [`ST_Contains`]({% link {{ page.version.version }}/st_contains.md %})
-- [`ST_ConvexHull`]({% link {{ page.version.version }}/st_convexhull.md %})
-- [`ST_CoveredBy`]({% link {{ page.version.version }}/st_coveredby.md %})
-- [`ST_Covers`]({% link {{ page.version.version }}/st_covers.md %})
-- [`ST_Disjoint`]({% link {{ page.version.version }}/st_disjoint.md %})
-- [`ST_Equals`]({% link {{ page.version.version }}/st_equals.md %})
-- [`ST_Intersects`]({% link {{ page.version.version }}/st_intersects.md %})
-- [`ST_Overlaps`]({% link {{ page.version.version }}/st_overlaps.md %})
-- [`ST_Touches`]({% link {{ page.version.version }}/st_touches.md %})
-- [`ST_Union`]({% link {{ page.version.version }}/st_union.md %})
-- [`ST_Within`]({% link {{ page.version.version }}/st_within.md %})
-- [Troubleshooting Overview]({% link {{ page.version.version }}/troubleshooting-overview.md %})
-- [Support Resources]({% link {{ page.version.version }}/support-resources.md %})
diff --git a/src/current/v26.2/indexes.md b/src/current/v26.2/indexes.md
index 2b6a892aa9b..05591f8586f 100644
--- a/src/current/v26.2/indexes.md
+++ b/src/current/v26.2/indexes.md
@@ -18,7 +18,7 @@ For example, if you index an `INT` column and then filter it `WHERE JSONB column, see the JSON tutorial.{{site.data.alerts.end}}
-
## How do GIN indexes work?
Standard [indexes]({% link {{ page.version.version }}/indexes.md %}) work well for searches based on prefixes of sorted data. However, data types like [`JSONB`]({% link {{ page.version.version }}/jsonb.md %}) or [arrays]({% link {{ page.version.version }}/array.md %}) cannot be queried without a full table scan, since they do not adhere to ordinary value prefix comparison operators. `JSONB` in particular needs to be indexed in a more detailed way than what a standard index provides. This is where GIN indexes prove useful.
diff --git a/src/current/v26.2/jsonb.md b/src/current/v26.2/jsonb.md
index fccf81617e3..3ac6b214271 100644
--- a/src/current/v26.2/jsonb.md
+++ b/src/current/v26.2/jsonb.md
@@ -8,8 +8,6 @@ docs_area: reference.sql
The `JSONB` [data type]({% link {{ page.version.version }}/data-types.md %}) stores JSON (JavaScript Object Notation) data as a binary representation of the `JSONB` value, which eliminates whitespace, duplicate keys, and key ordering. `JSONB` supports [GIN indexes]({% link {{ page.version.version }}/inverted-indexes.md %}).
-{{site.data.alerts.callout_success}}For a hands-on demonstration of storing and querying JSON data from a third-party API, see the JSON tutorial.{{site.data.alerts.end}}
-
## Alias
In CockroachDB, `JSON` is an alias for `JSONB`.
@@ -489,7 +487,6 @@ INSERT 1
## See also
-- [JSON tutorial]({% link {{ page.version.version }}/demo-json-support.md %})
- [GIN Indexes]({% link {{ page.version.version }}/inverted-indexes.md %})
- [Use an expression to index a field in a JSONB column]({% link {{ page.version.version }}/expression-indexes.md %}#use-an-expression-to-index-a-field-in-a-jsonb-column)
- [Computed Columns]({% link {{ page.version.version }}/computed-columns.md %})
diff --git a/src/current/v26.2/linestring.md b/src/current/v26.2/linestring.md
index 06d228af25e..1da1ffc4dd7 100644
--- a/src/current/v26.2/linestring.md
+++ b/src/current/v26.2/linestring.md
@@ -31,7 +31,6 @@ You can also make a LineString using the [aggregate function form]({% link {{ pa
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [POINT]({% link {{ page.version.version }}/point.md %})
- [POLYGON]({% link {{ page.version.version }}/polygon.md %})
@@ -39,4 +38,3 @@ You can also make a LineString using the [aggregate function form]({% link {{ pa
- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/migrate-from-geojson.md b/src/current/v26.2/migrate-from-geojson.md
index e0d804f0be7..95ebe6b8e4e 100644
--- a/src/current/v26.2/migrate-from-geojson.md
+++ b/src/current/v26.2/migrate-from-geojson.md
@@ -5,7 +5,7 @@ toc: true
docs_area: migrate
---
-CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/export-spatial-data.md %}).
+CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/query-spatial-data.md %}).
This page has instructions for migrating data from the [GeoJSON](https://wikipedia.org/wiki/GeoJSON) format into CockroachDB using [`ogr2ogr`](https://gdal.org/programs/ogr2ogr.html) and [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %}).
@@ -116,8 +116,6 @@ IMPORT INTO underground_storage_tank CSV DATA ('http://localhost:3000/tanks.csv'
## See also
- [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %})
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
@@ -128,7 +126,6 @@ IMPORT INTO underground_storage_tank CSV DATA ('http://localhost:3000/tanks.csv'
- [Back Up and Restore Data]({% link {{ page.version.version }}/take-full-and-incremental-backups.md %})
- [Use the Built-in SQL Client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [`cockroach` Commands Overview]({% link {{ page.version.version }}/cockroach-commands.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
{% comment %} Reference Links {% endcomment %}
diff --git a/src/current/v26.2/migrate-from-geopackage.md b/src/current/v26.2/migrate-from-geopackage.md
index c3fabeb57ef..949da54d47a 100644
--- a/src/current/v26.2/migrate-from-geopackage.md
+++ b/src/current/v26.2/migrate-from-geopackage.md
@@ -5,7 +5,7 @@ toc: true
docs_area: migrate
---
- CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/export-spatial-data.md %}).
+ CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/query-spatial-data.md %}).
This page has instructions for migrating data from the [GeoPackage](https://www.geopackage.org/) format into CockroachDB using [`ogr2ogr`](https://gdal.org/programs/ogr2ogr.html) and [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %}).
@@ -109,8 +109,6 @@ IMPORT INTO busstops CSV DATA ('http://localhost:3000/busstops.csv') WITH skip =
## See also
- [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %})
@@ -120,7 +118,6 @@ IMPORT INTO busstops CSV DATA ('http://localhost:3000/busstops.csv') WITH skip =
- [Back Up and Restore Data]({% link {{ page.version.version }}/take-full-and-incremental-backups.md %})
- [Use the Built-in SQL Client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [`cockroach` Commands Overview]({% link {{ page.version.version }}/cockroach-commands.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
{% comment %} Reference Links {% endcomment %}
diff --git a/src/current/v26.2/migrate-from-openstreetmap.md b/src/current/v26.2/migrate-from-openstreetmap.md
index a4bcdc56eef..cccb9aad434 100644
--- a/src/current/v26.2/migrate-from-openstreetmap.md
+++ b/src/current/v26.2/migrate-from-openstreetmap.md
@@ -5,7 +5,7 @@ toc: true
docs_area: migrate
---
- CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/export-spatial-data.md %}).
+ CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/query-spatial-data.md %}).
This page has instructions for migrating data from [OpenStreetMap](https://www.openstreetmap.org) `.pbf` data files into CockroachDB using [`osm2pgsql`](https://github.com/openstreetmap/osm2pgsql/).
@@ -122,8 +122,6 @@ Osm2pgsql took 2879s overall
## See also
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Migrate from GeoPackages]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %})
@@ -134,7 +132,6 @@ Osm2pgsql took 2879s overall
- [Back Up and Restore Data]({% link {{ page.version.version }}/take-full-and-incremental-backups.md %})
- [Use the Built-in SQL Client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [`cockroach` Commands Overview]({% link {{ page.version.version }}/cockroach-commands.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
{% comment %} Reference Links {% endcomment %}
diff --git a/src/current/v26.2/migrate-from-shapefiles.md b/src/current/v26.2/migrate-from-shapefiles.md
index 44f366d5a69..1737f3209b8 100644
--- a/src/current/v26.2/migrate-from-shapefiles.md
+++ b/src/current/v26.2/migrate-from-shapefiles.md
@@ -5,7 +5,7 @@ toc: true
docs_area: migrate
---
-CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/export-spatial-data.md %}).
+CockroachDB supports efficiently storing and querying [spatial data]({% link {{ page.version.version }}/query-spatial-data.md %}).
This page has instructions for migrating data from ESRI [Shapefiles]({% link {{ page.version.version }}/architecture/glossary.md %}#shapefile) into CockroachDB using [`ogr2ogr`](https://gdal.org/programs/ogr2ogr.html) and [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %}).
@@ -133,10 +133,7 @@ IMPORT INTO tornadoes CSV DATA ('http://localhost:3000/tornadoes.csv') WITH skip
## See also
- [`IMPORT INTO`]({% link {{ page.version.version }}/import-into.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Migrate from GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %})
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
diff --git a/src/current/v26.2/multilinestring.md b/src/current/v26.2/multilinestring.md
index 7918f325711..42bb2de319c 100644
--- a/src/current/v26.2/multilinestring.md
+++ b/src/current/v26.2/multilinestring.md
@@ -67,7 +67,6 @@ A MultiLineString can be created from SQL by calling an aggregate function such
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [POINT]({% link {{ page.version.version }}/point.md %})
- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
@@ -75,4 +74,3 @@ A MultiLineString can be created from SQL by calling an aggregate function such
- [MULTIPOINT]({% link {{ page.version.version }}/multipoint.md %})
- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/multipoint.md b/src/current/v26.2/multipoint.md
index 3b9fd6b296e..6a67acd7d83 100644
--- a/src/current/v26.2/multipoint.md
+++ b/src/current/v26.2/multipoint.md
@@ -69,7 +69,6 @@ SELECT ST_GeomFromText('MULTIPOINT (-87.738258999999999 42.010930999999999, -87.
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [POINT]({% link {{ page.version.version }}/point.md %})
- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
@@ -77,4 +76,3 @@ SELECT ST_GeomFromText('MULTIPOINT (-87.738258999999999 42.010930999999999, -87.
- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/multipolygon.md b/src/current/v26.2/multipolygon.md
index d2ceaed2cdb..02eb315c354 100644
--- a/src/current/v26.2/multipolygon.md
+++ b/src/current/v26.2/multipolygon.md
@@ -27,7 +27,6 @@ SELECT ST_GeomFromText('SRID=4326;MULTIPOLYGON(((-87.906471 43.038902, -95.99277
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [POINT]({% link {{ page.version.version }}/point.md %})
- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
@@ -35,4 +34,3 @@ SELECT ST_GeomFromText('SRID=4326;MULTIPOLYGON(((-87.906471 43.038902, -95.99277
- [MULTIPOINT]({% link {{ page.version.version }}/multipoint.md %})
- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/performance-best-practices-overview.md b/src/current/v26.2/performance-best-practices-overview.md
index 2da6890047a..538635dcab1 100644
--- a/src/current/v26.2/performance-best-practices-overview.md
+++ b/src/current/v26.2/performance-best-practices-overview.md
@@ -15,9 +15,9 @@ For `INSERT`, `UPSERT`, and `DELETE` statements, a single multi-row statement is
For more information, see:
-- [Insert Data]({% link {{ page.version.version }}/insert-data.md %})
-- [Update Data]({% link {{ page.version.version }}/update-data.md %})
-- [Delete Data]({% link {{ page.version.version }}/delete-data.md %})
+- [`INSERT`]({% link {{ page.version.version }}/insert.md %})
+- [`UPDATE`]({% link {{ page.version.version }}/update.md %})
+- [`DELETE`]({% link {{ page.version.version }}/delete.md %})
- [How to improve IoT application performance with multi-row DML](https://www.cockroachlabs.com/blog/multi-row-dml/)
### Use `UPSERT` instead of `INSERT ON CONFLICT` on tables with no secondary indexes
diff --git a/src/current/v26.2/point.md b/src/current/v26.2/point.md
index ce6eba7b9e0..35d0cf44418 100644
--- a/src/current/v26.2/point.md
+++ b/src/current/v26.2/point.md
@@ -47,7 +47,6 @@ SELECT ST_GeomFromText('POINT(0 0)');
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
- [POLYGON]({% link {{ page.version.version }}/polygon.md %})
@@ -55,4 +54,3 @@ SELECT ST_GeomFromText('POINT(0 0)');
- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/polygon.md b/src/current/v26.2/polygon.md
index 1a61704faf3..1c9983a2542 100644
--- a/src/current/v26.2/polygon.md
+++ b/src/current/v26.2/polygon.md
@@ -62,7 +62,6 @@ SELECT ST_MakePolygon('LINESTRING(0 0, 0 1024, 1024 1024, 1024 0, 0 0)');
## See also
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial objects]({% link {{ page.version.version }}/spatial-data-overview.md %}#spatial-objects)
- [POINT]({% link {{ page.version.version }}/point.md %})
- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
@@ -70,4 +69,3 @@ SELECT ST_MakePolygon('LINESTRING(0 0, 0 1024, 1024 1024, 1024 0, 0 0)');
- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/row-level-security.md b/src/current/v26.2/row-level-security.md
index 4f8c8075c3c..d4dc779962a 100644
--- a/src/current/v26.2/row-level-security.md
+++ b/src/current/v26.2/row-level-security.md
@@ -37,7 +37,7 @@ For an example, refer to [RLS for Multi-Tenant Isolation](#rls-for-multi-tenant-
At a high level, the steps for using row-level security (RLS) are as follows:
-1. Choose which [schema objects]({% link {{ page.version.version }}/schema-design-overview.md %}) need row-level security. You can re-use existing schemas, or create new ones and [insert data]({% link {{ page.version.version }}/insert-data.md %}). ([`CREATE TABLE`]({% link {{ page.version.version }}/create-table.md %}), [`INSERT`]({% link {{ page.version.version }}/insert.md %}))
+1. Choose which [schema objects]({% link {{ page.version.version }}/schema-design-overview.md %}) need row-level security. You can re-use existing schemas, or create new ones and [insert data]({% link {{ page.version.version }}/insert.md %}). ([`CREATE TABLE`]({% link {{ page.version.version }}/create-table.md %}), [`INSERT`]({% link {{ page.version.version }}/insert.md %}))
2. [Create roles]({% link {{ page.version.version }}/create-role.md %}) & [grant access]({% link {{ page.version.version }}/grant.md %}) to schema objects by those roles. ([`CREATE ROLE`]({% link {{ page.version.version }}/create-role.md %}), [`GRANT`]({% link {{ page.version.version }}/grant.md %}))
3. Enable row-level security on the schema objects. ([`ALTER TABLE ... ENABLE ROW LEVEL SECURITY`]({% link {{ page.version.version }}/alter-table.md %}#enable-row-level-security))
4. Define row-level security policies on the schema objects which are assigned to specific roles. ([`CREATE POLICY`]({% link {{ page.version.version }}/create-policy.md %}))
diff --git a/src/current/v26.2/row-level-ttl.md b/src/current/v26.2/row-level-ttl.md
index 0a02b9c82dd..9613cf4dd8f 100644
--- a/src/current/v26.2/row-level-ttl.md
+++ b/src/current/v26.2/row-level-ttl.md
@@ -586,7 +586,6 @@ If you are migrating your TTL usage from an earlier version of CockroachDB, the
## See also
- [Bulk-delete Data]({% link {{ page.version.version }}/bulk-delete-data.md %})
-- [Delete data]({% link {{ page.version.version }}/delete-data.md %})
- [`DELETE`]({% link {{ page.version.version }}/delete.md %})
- [`SELECT` clause]({% link {{ page.version.version }}/select-clause.md %})
- [`AS OF SYSTEM TIME`]({% link {{ page.version.version }}/as-of-system-time.md %})
diff --git a/src/current/v26.2/run-multi-statement-transactions.md b/src/current/v26.2/run-multi-statement-transactions.md
index d26f45a6bd0..ab7e286f969 100644
--- a/src/current/v26.2/run-multi-statement-transactions.md
+++ b/src/current/v26.2/run-multi-statement-transactions.md
@@ -14,7 +14,7 @@ Make sure you have already:
- Set up a [local cluster]({% link {{ page.version.version }}/secure-a-cluster.md %}).
- [Installed a PostgreSQL client]({% link {{ page.version.version }}/install-client-drivers.md %}).
- [Connected to the database]({% link {{ page.version.version }}/connect-to-the-database.md %}).
-- [Inserted data]({% link {{ page.version.version }}/insert-data.md %}) that you now want to run queries against.
+- [Inserted data]({% link {{ page.version.version }}/insert.md %}) that you now want to run queries against.
{% include {{page.version.version}}/app/retry-errors.md %}
@@ -84,9 +84,9 @@ Reference information related to this task:
Other common tasks:
- [Connect to the Database]({% link {{ page.version.version }}/connect-to-the-database.md %})
-- [Insert Data]({% link {{ page.version.version }}/insert-data.md %})
-- [Update Data]({% link {{ page.version.version }}/update-data.md %})
-- [Delete Data]({% link {{ page.version.version }}/delete-data.md %})
+- [Insert Data]({% link {{ page.version.version }}/insert.md %})
+- [Update Data]({% link {{ page.version.version }}/update.md %})
+- [Delete Data]({% link {{ page.version.version }}/delete.md %})
- [Optimize Statement Performance][fast]
- [Troubleshoot SQL Statements]({% link {{ page.version.version }}/query-behavior-troubleshooting.md %})
diff --git a/src/current/v26.2/schema-design-indexes.md b/src/current/v26.2/schema-design-indexes.md
index 60a904b357e..1afecdb768b 100644
--- a/src/current/v26.2/schema-design-indexes.md
+++ b/src/current/v26.2/schema-design-indexes.md
@@ -274,14 +274,14 @@ To see an index definition, use a [`SHOW CREATE`]({% link {{ page.version.versio
(1 row)
~~~
-After creating a database, a user-defined schema, some tables, and secondary indexes, the database schema should be ready for your application to [write]({% link {{ page.version.version }}/insert-data.md %}) and read data.
+After creating a database, a user-defined schema, some tables, and secondary indexes, the database schema should be ready for your application to [write]({% link {{ page.version.version }}/insert.md %}) and read data.
It's likely that you will need to update your database schema at some point. We also recommend reading about [how online schema changes work in CockroachDB]({% link {{ page.version.version }}/online-schema-changes.md %}).
## What's next?
- [Online Schema Changes]({% link {{ page.version.version }}/online-schema-changes.md %})
-- [Insert Data]({% link {{ page.version.version }}/insert-data.md %})
+- [`INSERT`]({% link {{ page.version.version }}/insert.md %})
You might also be interested in the following pages:
diff --git a/src/current/v26.2/secure-a-cluster.md b/src/current/v26.2/secure-a-cluster.md
index 727f0357242..941b778f5b8 100644
--- a/src/current/v26.2/secure-a-cluster.md
+++ b/src/current/v26.2/secure-a-cluster.md
@@ -460,7 +460,7 @@ Adding capacity is as simple as starting more nodes with `cockroach start`.
- [Install the client driver]({% link {{ page.version.version }}/install-client-drivers.md %}) for your preferred language
- Learn more about [CockroachDB SQL]({% link {{ page.version.version }}/learn-cockroachdb-sql.md %}) and the [built-in SQL client]({% link {{ page.version.version }}/cockroach-sql.md %})
-- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
+- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and JSON support({% link {{ page.version.version }}/jsonb.md %})
You might also be interested in the following pages:
diff --git a/src/current/v26.2/simulate-a-multi-region-cluster-on-localhost.md b/src/current/v26.2/simulate-a-multi-region-cluster-on-localhost.md
index c539bbc0a0d..f77db92ae17 100644
--- a/src/current/v26.2/simulate-a-multi-region-cluster-on-localhost.md
+++ b/src/current/v26.2/simulate-a-multi-region-cluster-on-localhost.md
@@ -98,4 +98,3 @@ When you're done with your demo cluster, you can wipe the cluster by typing the
- [Low Latency Reads and Writes in a Multi-Region Cluster]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %})
- [Fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %})
- [Serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %})
- - [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
diff --git a/src/current/v26.2/spatial-data-overview.md b/src/current/v26.2/spatial-data-overview.md
index ec183bdaad9..f837e390f0a 100644
--- a/src/current/v26.2/spatial-data-overview.md
+++ b/src/current/v26.2/spatial-data-overview.md
@@ -13,7 +13,6 @@ See the links below for more information about how to use CockroachDB for spatia
## Getting Started
- [Install CockroachDB]({% link {{ page.version.version }}/install-cockroachdb.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
## Migrating spatial data into and out of CockroachDB
@@ -21,7 +20,6 @@ See the links below for more information about how to use CockroachDB for spatia
- [Migrate from GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %})
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
## Reference
@@ -71,4 +69,3 @@ In addition to the [generated reference documentation for spatial functions]({%
## See also
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/spatial-indexes.md b/src/current/v26.2/spatial-indexes.md
index 3fe202d9236..8afc9458a4a 100644
--- a/src/current/v26.2/spatial-indexes.md
+++ b/src/current/v26.2/spatial-indexes.md
@@ -6,7 +6,7 @@ keywords: gin, gin index, gin indexes, inverted index, inverted indexes, acceler
docs_area: reference.sql
---
-This page describes CockroachDB's support for indexing [spatial data]({% link {{ page.version.version }}/export-spatial-data.md %}), including:
+This page describes CockroachDB's support for indexing [spatial data]({% link {{ page.version.version }}/query-spatial-data.md %}), including:
- What spatial indexes are
- How they work
@@ -227,8 +227,6 @@ CREATE INDEX geog_idx_3 ON geo_table USING GIST(geog);
- [S2 Geometry Library](https://s2geometry.io/)
- [Indexes]({% link {{ page.version.version }}/indexes.md %})
- [Spatial Data Overview]({% link {{ page.version.version }}/spatial-data-overview.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
- [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %})
@@ -236,4 +234,3 @@ CREATE INDEX geog_idx_3 ON geo_table USING GIST(geog);
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/spatial-tutorial.md b/src/current/v26.2/spatial-tutorial.md
deleted file mode 100644
index fe3c942340a..00000000000
--- a/src/current/v26.2/spatial-tutorial.md
+++ /dev/null
@@ -1,1861 +0,0 @@
----
-title: Spatial Data
-summary: Tutorial for working with spatial data in CockroachDB.
-toc: true
-toc_not_nested: true
-docs_area: deploy
----
-
-In this tutorial, you will plan a vacation from New York City to the [Adirondack Mountains](https://visitadirondacks.com/about) in northern New York State to do some birdwatching while visiting local independent bookstores. In the process, you will explore several of CockroachDB's [spatial capabilities]({% link {{ page.version.version }}/spatial-data-overview.md %}):
-
-+ Importing spatial data from SQL files (including how to build spatial geometries from data in CSV files).
-+ Putting together separate spatial data sets to ask and answer potentially interesting questions.
-+ Using various [built-in functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions) for operating on spatial data.
-+ Creating [indexes]({% link {{ page.version.version }}/spatial-indexes.md %}) on spatial data.
-+ Performing [joins]({% link {{ page.version.version }}/joins.md %}) on spatial data, and using [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}) to make sure indexes are effective.
-+ Visualizing the output of your queries using free tools like 
-
-### (2) What is the total area of Loon sightings?
-
-Now that you have some sense of how loon sightings are distributed across the state, you may wonder: What is the area of the loon's approximate habitat (per these sightings) in New York State?
-
-To find the answer:
-
-1. Collect the geometries of all loon sightings together in a CTE as one geometry.
-1. Get the area of the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of the resulting geometry.
-1. Because the `birds.routes` data uses [SRID 4326]({% link {{ page.version.version }}/srid-4326.md %}), the resulting area is measured in degrees, which can be converted to square miles by casting the data to a `GEOGRAPHY` type and dividing by 1609 (the number of meters in a mile) squared.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_sightings
- AS (
- SELECT
- st_collect(routes.geom) AS the_geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- st_area(st_convexhull(the_geom)::GEOGRAPHY) / 1609 ^ 2
-FROM
- loon_sightings;
-~~~
-
-The result is an area of about 10,000 square miles, which can be visualized by a box with sides that are a little over 100 miles long. This answer can be verified by looking at the map output from [#1][q_01] and noting that most of the observation locations are well within 100 miles of each other.
-
-~~~
- ?column?
-----------------------
- 10430.691899422598
-(1 row)
-~~~
-
-### (3) How many Loon sightings have there been in NY state in the years 2000-2019?
-
-In addition to the [loon observation point locations][q_01] and the [area of those observations][q_02] (which you can take as a proxy for the size of the loon's local habitat), you may want to know exactly how many times a loon has been sighted in the area.
-
-To find the answer:
-
-1. Join `birds.birds` and `birds.observations` on the bird ID where the bird name is "Common Loon".
-1. Sum all of the sightings; the `GROUP BY` on bird names is necessary due to the use of the `sum` aggregate function.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-SELECT
- birds.name, sum(observations.count) AS sightings
-FROM
- birds.birds, birds.observations
-WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
-GROUP BY
- birds.name;
-~~~
-
-~~~
- name | sightings
---------------+------------
- Common Loon | 269
-(1 row)
-~~~
-
-### (4) How many Loon sightings were there in NY state in just the year 2019?
-
-You might like to get a sense of how many of the loon sightings were more recent. For example, if there have been fewer sightings of loons in recent years, you might wonder if the population were declining in NY State.
-
-To determine how many sightings there were in NY state in 2019, re-use the query from [#3][q_03], with the additional constraint that the observation year is 2019.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-SELECT
- birds.name, sum(observations.count) AS sightings
-FROM
- birds.birds, birds.observations
-WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.year = 2019
-GROUP BY
- birds.name;
-~~~
-
-If the sightings are evenly distributed, you might expect this to yield about 13 sightings. In fact, it's pretty close. This suggests that the loon population may have remained fairly stable over the years 2000-2019.
-
-~~~
- name | sightings
---------------+------------
- Common Loon | 12
-(1 row)
-~~~
-
-### (5) What is the density of Loon sightings per square mile in its range as defined by this data set in 2019?
-
-Since you are planning to do some hiking back into the woods to find the actual loons, you may get curious as to how densely the loon population is scattered over its habitat. From a practical perspective, the distribution is not actually even, since loons are most frequently located on lakes and ponds. Even so, it may serve as a useful proxy for the general areas in which to focus your birdwatching travels.
-
-To answer this question:
-
-1. Build a CTE that returns both the convex hull of loon habitat, as well as the sum of all loon observations in NY.
-1. Query the result table of the CTE from step 1 to divide the number of sightings by the area of the loon's habitat (the convex hull). As in [#2][q_02], do some arithmetic to convert from the unit of degrees returned by [SRID 4326]({% link {{ page.version.version }}/srid-4326.md %}) to square miles.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- sum(observations.count) AS sightings,
- st_convexhull(st_collect(routes.geom))
- AS the_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- AND observations.year = 2019
- )
-SELECT
- loon_habitat.sightings::FLOAT8
- / (
- SELECT
- st_area(loon_habitat.the_hull::GEOGRAPHY)
- / 1609 ^ 2
- )
-FROM
- loon_habitat;
-~~~
-
-It turns out that this is a pretty small number. There are not very many loon sightings per square mile within its overall habitat area, since lakes and ponds make up a small portion of the physical space inside the convex hull of observations.
-
-~~~
- ?column?
-------------------------
- 0.005078531189694916
-(1 row)
-~~~
-
-## Step 5. Find bookstores to visit
-
-### (6) What are the bookstores that lie within the Loon's habitat range in NY state?
-
-Now that you have asked (and answered) some exploratory questions that may inform your birdwatching activities while on vacation, it would be good to start thinking about what bookstores to visit as part of your travels.
-
-A natural question that arises is: given that you are looking for loon habitat, what are the bookstores located within that habitat?
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above CTE with a query against [the `bookstores` table](#the-bookstores-and-bookstore_routes-tables) that checks whether a bookstore's location is [contained]({% link {{ page.version.version }}/st_contains.md %}) by the loon habitat. Note that the query below [orders by]({% link {{ page.version.version }}/order-by.md %}) the store geometries so that stores in the list are clustered by location. This ordering may be useful if you want to travel between nearby stores. For more information about how this ordering is calculated, see [How CockroachDB's spatial indexing works]({% link {{ page.version.version }}/spatial-indexes.md %}#how-cockroachdbs-spatial-indexing-works).
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_sightings
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS loon_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- name, street, city, state, url, phone
-FROM
- tutorial.bookstores, loon_sightings
-WHERE
- st_contains(loon_hull, geom)
-ORDER BY
- geom;
-~~~
-
-~~~
- name | street | city | state | url | phone
----------------------------------------------------+-----------------------+----------------+-------+-------------------------------------------------+-----------------
- Blacktree Books | 5006 State Highway 23 | Oneonta | NY | https://blacktreeoneonta.square.site/ | 6074321200
- The Green Toad Bookstore | 198 Main St | Oneonta | NY | http://www.greentoadbookstore.com | (607) 433-8898
- The Treehouse Reading and Arts Center | 587 Main St Ste 304 | New York Mills | NY | http://treehousebookshop.com | 315-765-6262
- Gansevoort House Books at Gems Along the Mohawk | 800 Mohawk St | Herkimer | NY | |
- Gansevoort House Books at The Shoppes at 25 West | 25 W Mill Street | Little Falls | NY | http://www.gansevoorthouse.com/bookstore/ |
- Mysteries On Main Street | 144 W Main St | Johnstown | NY | https://www.facebook.com/MysteriesOnMainStreet/ | (518) 736-2665
- The Bookstore Plus Music & Art | 2491 Main St | Lake Placid | NY | http://www.thebookstoreplus.com | (518) 523-2950
- The Book Nook (Saranac Lake, NY) | 7 Broadway | Saranac Lake | NY | https://www.facebook.com/slbooknook/ | 6315999511
-(8 rows)
-~~~
-
-### (7) How many different species of bird habitats contain the location of the Book Nook in Saranac Lake, NY?
-
-As a birdwatcher, you probably do not want to only see the Common Loon. As long as you are spending time outside, it would be nice to see what other species of bird are in the area. This may provide even more birdwatching fun.
-
-Since you know you will want to hit up [The Book Nook in Saranac Lake, NY](https://www.facebook.com/slbooknook/) at least once on your trip, you decide to see what bird species' habitats contain the store's location. That way you can get a sense of the diversity of bird species in the area.
-
-To answer this question:
-
-1. Build a CTE that returns some information about the bookstore you want to visit.
-1. Build another CTE that returns information about the habitats of birds observed in NY state, and collects the habitat geometries together into one geometry.
-1. Join the results of the above CTEs and query the count of birds whose habitats contain the location of the bookstore.
-
-{{site.data.alerts.callout_info}}
-The final [`SELECT`]({% link {{ page.version.version }}/selection-queries.md %}) in the query below is doing a join that will not benefit from [spatial indexing]({% link {{ page.version.version }}/spatial-indexes.md %}), since both sides of the join are the results of [CTEs]({% link {{ page.version.version }}/common-table-expressions.md %}), and are therefore not indexed.
-{{site.data.alerts.end}}
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- the_book_nook
- AS (
- SELECT
- bookstores.name, street, city, state, geom
- FROM
- tutorial.bookstores
- WHERE
- state = 'NY' AND city = 'Saranac Lake'
- ),
- local_birds
- AS (
- SELECT
- birds.name,
- st_convexhull(st_collect(routes.geom))
- AS the_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.id = observations.bird_id
- AND observations.route_id = routes.id
- GROUP BY
- birds.name
- )
-SELECT
- count(local_birds.name)
-FROM
- local_birds, the_book_nook
-WHERE
- st_contains(local_birds.the_hull, the_book_nook.geom);
-~~~
-
-This is encouraging; there are over 120 species of birds for you to choose from whose habitats include the bookstore you want to visit!
-
-~~~
- count
----------
- 124
-(1 row)
-~~~
-
-You can verify the results by checking how many bird species have been sighted by the bird survey overall; you should expect that this number will be much larger than 124, and indeed it is:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-SELECT COUNT(name) FROM birds.birds;
-~~~
-
-~~~
- count
----------
- 756
-(1 row)
-~~~
-
-### (8) Which 25 birds were most often sighted within 10 miles of the Book Nook in Saranac Lake, NY during the 2000-2019 observation period?
-
-It's great that you [know how many bird species may be near a given bookstore][q_07]; however, that query didn't tell you which birds you have the best chance of seeing. Therefore, you'd like to figure out what birds are the most commonly seen in the area near a bookstore you want to visit: The Book Nook in Saranac Lake, NY.
-
-To answer this question:
-
-1. Build a CTE that returns some information about the bookstore you want to visit.
-1. Join the results of the above CTE and a query against the `birds` database that lists the names and observation totals (sums) of birds whose habitats are within 10 miles of the location of the bookstore.
-
-{{site.data.alerts.callout_info}}
-The query below can also be written using an explicit `ST_DWithin`, which is an [index-accelerated function]({% link {{ page.version.version }}/query-spatial-data.md %}#performance). CockroachDB optimizes `ST_Distance(...) < $some_value` to use `ST_DWithin` (see this query's [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}) output for details).
-{{site.data.alerts.end}}
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- the_book_nook
- AS (
- SELECT
- bookstores.name, street, city, state, geom
- FROM
- tutorial.bookstores
- WHERE
- state = 'NY' AND city = 'Saranac Lake'
- )
-SELECT
- birds.name, sum(observations.count) AS sightings
-FROM
- birds.birds,
- birds.observations,
- birds.routes,
- the_book_nook
-WHERE
- birds.id = observations.bird_id
- AND observations.route_id = routes.id
- AND st_distance(
- the_book_nook.geom::GEOGRAPHY,
- routes.geom::GEOGRAPHY
- )
- < (1609 * 10)
-GROUP BY
- birds.name
-ORDER BY
- sightings DESC
-LIMIT
- 25;
-~~~
-
-Perhaps surprisingly, the Red-eyed Vireo is the "winner", followed by a number of other fairly common birds. If you want a birdwatching challenge, you can reverse the sort order of the above query to find the rarest birds.
-
-~~~
- name | sightings
------------------------------------------+------------
- Red-eyed Vireo | 2557
- White-throated Sparrow | 928
- Hermit Thrush | 924
- American Robin | 691
- Ovenbird | 650
- American Crow | 528
- (Myrtle Warbler) Yellow-rumped Warbler | 506
- Chipping Sparrow | 465
- Black-capped Chickadee | 390
- Yellow-bellied Sapsucker | 359
- Blue-headed Vireo | 357
- Blue Jay | 345
- Winter Wren | 344
- Cedar Waxwing | 272
- Blackburnian Warbler | 248
- Magnolia Warbler | 236
- Black-throated Green Warbler | 226
- Common Grackle | 218
- Red-breasted Nuthatch | 209
- Common Yellowthroat | 184
- Northern Parula | 175
- Nashville Warbler | 164
- Red-winged Blackbird | 152
- Least Flycatcher | 128
- American Redstart | 123
-(25 rows)
-~~~
-
-### (9) What does the shape of all bookstore locations that lie within the Loon's habitat look like?
-
-You [already discovered which bookstores are located within loon habitat][q_06]. However, you cannot really tell where these stores are located based on reading that query's output. You need to see them on the map. Therefore, for trip planning purposes, you decide you would like to look at the shape of the convex hull of the store locations.
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above CTE with a query against [the `bookstores` table](#the-bookstores-and-bookstore_routes-tables) that checks whether a bookstore's location is [contained]({% link {{ page.version.version }}/st_contains.md %}) by the loon habitat.
-1. Collect the geometries that result from the step above into a single geometry, calculate its convex hull, and return the results as [GeoJSON]({% link {{ page.version.version }}/geojson.md %}).
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS the_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- st_asgeojson(st_convexhull(st_collect(geom)))
-FROM
- tutorial.bookstores, loon_habitat
-WHERE
- st_contains(the_hull, geom);
-~~~
-
-~~~
- st_asgeojson
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
- {"type":"Polygon","coordinates":[[[-75.036815,42.448793],[-75.062138,42.454003],[-75.296575,43.098322],[-74.131673,44.326892],[-73.980251,44.279377],[-74.374175,43.006495],[-75.036815,42.448793]]]}
-(1 row)
-~~~
-
-Paste the result above into 
-
-### (10) What is the area of the shape of all bookstore locations that are in the Loon's habitat range within NY state?
-
-You have already [visualized the convex hull][q_09], but now you would like to calculate its area in square miles.
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above CTE with a query against [the `bookstores` table](#the-bookstores-and-bookstore_routes-tables) that checks whether a bookstore's location is [contained]({% link {{ page.version.version }}/st_contains.md %}) by the loon habitat.
-1. Get the area of the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of the resulting geometry.
-1. Collect the geometries that result from the step above into a single geometry, calculate its convex hull, and calculate the area of the hull. As in previous examples, note that because the `birds.routes` data uses [SRID 4326]({% link {{ page.version.version }}/srid-4326.md %}), the resulting area is measured in degrees, which is converted to square miles by casting the data to a `GEOGRAPHY` type and dividing by 1609 (the number of meters in a mile) squared.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS the_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- st_area(st_convexhull(st_collect(geom))::GEOGRAPHY)
- / 1609 ^ 2
-FROM
- tutorial.bookstores, loon_habitat
-WHERE
- st_contains(the_hull, geom);
-~~~
-
-The result is an area of about 3,500 square miles, which can be visualized by a box with sides that are approximately 60 miles long. This answer can be verified by looking at the map output from [#9][q_09] and noting that most of the points on the hull are within about 60 miles of each other.
-
-~~~
- ?column?
-----------------------
- 3564.1722404508437
-(1 row)
-~~~
-
-## Step 6. Plan your route
-
-### (11) How long is the route from Mysteries on Main Street in Johnstown, NY to The Book Nook in Saranac Lake, NY?
-
-So far, you have written queries that ask questions about bird habitats, bookstore locations, or some combination of the two. Because you are planning a trip, you want to think about how to travel between the bookstores in the loon habitat area. Depending on how far apart the bookstores are, you may want to visit more than one of them in a day, perhaps doing some birdwatching along the way.
-
-In order to accomplish this, you start looking at [the `bookstore_routes` table](#the-bookstores-and-bookstore_routes-tables) to see how far the distance is in miles between two of the bookstores you'd like to visit.
-
-To answer this question:
-
-1. Issue subqueries that find the IDs of two of the bookstores you'd like to travel between, as start and end points.
-1. Measure the length of the geometry that corresponds to those start and end IDs. Note that because the `bookstore_routes.geom` column has a SRID of 0 (which it inherited from the `roads` database from which it was created), you can convert to miles by casting the data to a `GEOGRAPHY` type, which uses meters, and then dividing by 1609 (the number of meters in a mile).
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-SELECT
- st_length(geom::GEOGRAPHY) / 1609
-FROM
- bookstore_routes
-WHERE
- end_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Johnstown' AND state = 'NY'
- )
- AND start_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Saranac Lake' AND state = 'NY'
- );
-~~~
-
-The answer is: these two stores are about 132 miles apart, measured in road miles.
-
-~~~
- ?column?
----------------------
- 132.6837073049756
-(1 row)
-~~~
-
-### (12) What does the route from Mysteries on Main Street in Johnstown, NY to The Book Nook in Saranac Lake, NY look like?
-
-You have [determined how long the drive between these two stores will be][q_11], but you would like to see what it looks like on the map.
-
-To find this out, you can re-use the query from the previous question, using `ST_AsGeoJSON` instead of `ST_Length`, and skipping the distance math to generate output in miles.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-SELECT
- st_asgeojson(geom)
-FROM
- bookstore_routes
-WHERE
- end_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Johnstown' AND state = 'NY'
- )
- AND start_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Saranac Lake' AND state = 'NY'
- );
-~~~
-
-The result is a very large chunk of JSON:
-
-~~~
- st_asgeojson
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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- 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-(1 row)
-~~~
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-Paste the result above into 
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-### (13) What were the 25 most-commonly-sighted birds in 2019 within 10 miles of the route between Mysteries on Main Street in Johnstown, NY and The Bookstore Plus in Lake Placid, NY?
-
-A natural question that arises once you start traveling between stores is: if I decide to visit two of these stores in a day, and do some birdwatching in between, what are some of the bird species I may expect to find? In this case you are looking at what species are near the route between [Mysteries on Main Street in Johnstown, NY](https://www.facebook.com/MysteriesOnMainStreet), and [The Bookstore Plus in Lake Placid, NY](http://www.thebookstoreplus.com).
-
-To answer this question:
-
-1. Build a CTE that returns the route between the two stores of interest (a geometry).
-1. Join the results of the above CTE with a query against [the `birds` database](#the-birds-database) that checks whether the distance between the route geometry and the location of the bird observation (`birds.routes.geom`) bookstore's location is less than the desired length of 10 miles. Note that because the call to `ST_Distance` is operating on shapes cast to `GEOGRAPHY` data type, the results are in meters, which then have to be converted to miles by dividing the result by 1609 (the number of meters in a mile).
-
-{{site.data.alerts.callout_info}}
-The query below can also be written using an explicit `ST_DWithin`, which is an [index-accelerated function]({% link {{ page.version.version }}/query-spatial-data.md %}#performance). CockroachDB optimizes `ST_Distance(...) < $some_value` to use `ST_DWithin` (see this query's [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}) output for details).
-{{site.data.alerts.end}}
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- bookstore_trip
- AS (
- SELECT
- geom
- FROM
- bookstore_routes
- WHERE
- start_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Johnstown'
- AND state = 'NY'
- )
- AND end_store_id
- = (
- SELECT
- id
- FROM
- tutorial.bookstores
- WHERE
- city = 'Lake Placid'
- AND state = 'NY'
- )
- )
-SELECT
- birds.birds.name,
- sum(birds.observations.count) AS sightings
-FROM
- birds.birds,
- birds.observations,
- birds.routes,
- bookstore_trip
-WHERE
- birds.birds.id = birds.observations.bird_id
- AND observations.route_id = routes.id
- AND observations.year = 2019
- AND st_distance(
- bookstore_trip.geom::GEOGRAPHY,
- birds.routes.geom::GEOGRAPHY
- )
- < (1609 * 10)
-GROUP BY
- birds.name
-ORDER BY
- sightings DESC
-LIMIT
- 25;
-~~~
-
-~~~
- name | sightings
------------------------------------------+------------
- Red-eyed Vireo | 124
- Ovenbird | 98
- Blue Jay | 56
- Black-capped Chickadee | 44
- American Robin | 43
- American Crow | 31
- White-throated Sparrow | 26
- Common Yellowthroat | 25
- Red-breasted Nuthatch | 19
- Blue-headed Vireo | 18
- Red-winged Blackbird | 16
- Veery | 16
- Winter Wren | 16
- American Redstart | 15
- Blackburnian Warbler | 15
- Chipping Sparrow | 14
- Least Flycatcher | 13
- Northern Parula | 13
- American Goldfinch | 13
- Magnolia Warbler | 12
- Cedar Waxwing | 12
- Song Sparrow | 11
- Hermit Thrush | 11
- Common Raven | 10
- (Myrtle Warbler) Yellow-rumped Warbler | 10
-(25 rows)
-~~~
-
-### (14) What are the 25 least often sighted other species of birds in the loon's sightings range?
-
-Since you are already looking for loons, you would like to know if there are other, more challenging birdwatching opportunities available within the loon habitat you are already visiting. Specifically, you would like to know: What are the least-often-observed bird species in the loon's habitat?
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above CTE and a query against [the `birds` database](#the-birds-database) that lists the names and observation totals (sums) of birds whose habitats are contained within the convex hull of loon habitat.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS loon_hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- birds.name, sum(birds.observations.count) AS sightings
-FROM
- birds.birds,
- birds.observations,
- birds.routes,
- loon_habitat
-WHERE
- birds.id = observations.bird_id
- AND observations.route_id = routes.id
- AND st_contains(loon_hull, routes.geom)
-GROUP BY
- birds.name
-ORDER BY
- sightings ASC
-LIMIT
- 25;
-~~~
-
-~~~
- name | sightings
----------------------------------------------------+------------
- Vesper Sparrow | 1
- Northern Saw-whet Owl | 1
- Sora | 1
- Least Bittern | 1
- Palm Warbler | 1
- Ring-necked Duck | 1
- American Three-toed Woodpecker | 1
- unid. Yellow-billed Cuckoo / Black-billed Cuckoo | 1
- unid. Accipiter hawk | 1
- Eastern Whip-poor-will | 1
- Golden-winged Warbler | 1
- Eastern Screech-Owl | 1
- Northern Goshawk | 1
- Upland Sandpiper | 1
- Hooded Warbler | 2
- Grasshopper Sparrow | 2
- Gadwall | 2
- Brewster Warbler (Golden-winged x Blue-winged) | 2
- Double-crested Cormorant | 3
- White-winged Crossbill | 3
- Marsh Wren | 3
- Pine Siskin | 3
- Great Horned Owl | 3
- Northern Mockingbird | 3
- Red-shouldered Hawk | 4
-(25 rows)
-~~~
-
-## Step 7. Get road and travel info
-
-### (15) What are the top 10 roads nearest to a Loon sighting location in NY?
-
-So far, you have learned [where the birds are](#step-4-scout-loon-locations), [where the overlaps are between birds and bookstores](#step-5-find-bookstores-to-visit), and [how to travel between bookstores while finding birds](#step-6-plan-your-route).
-
-Now you will start asking questions that involve looking at [the `roads` table](#the-roads-table) as well.
-
-Specifically, you would like to know: What are the 10 roads that are closest to a loon observation site? The answer to this question may help you figure out exactly which roads you should travel down to start the search for loons. The closer a road is to the sighting location, the better: it means there is probably less work to do to see the birds you are looking for.
-
-To answer this question:
-
-1. Build a CTE called `loon_habitat` that collects all of the Common Loon sightings into a single geometry.
-1. Build another CTE called `nearby_roads` that joins the results of the subquery above with [the `roads` table](#the-roads-table) and pulls out the roads in NY state that are within a degree (about 60 nautical miles). Order the roads returned by their distance from a loon sighting. This will return some duplicate roads (since loons can be sighted multiple times along a single road), which is why you need to `LIMIT` to 20 here so you can get the list down to 10 later. Because the data in the `roads` table has an SRID of 0, you need to use `ST_SetSRID` to set its SRID to [4326]({% link {{ page.version.version }}/srid-4326.md %}). This step is necessary because `ST_Distance` cannot operate on geometries with differing SRIDs.
-1. Finally, query the results of the `nearby_roads` subquery to get a list of 10 distinct road names that you can plan to visit.
-
-{{site.data.alerts.callout_info}}
-The query below can also be written using an explicit `ST_DWithin`, which is an [index-accelerated function]({% link {{ page.version.version }}/query-spatial-data.md %}#performance). CockroachDB optimizes `ST_Distance(...) < $some_value` to use `ST_DWithin` (see this query's [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}) output for details).
-{{site.data.alerts.end}}
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_collect(routes.geom) AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- ),
- nearby_roads
- AS (
- SELECT
- roads.prime_name AS road_name
- FROM
- roads, loon_habitat
- WHERE
- st_distance(
- loon_habitat.geom,
- st_setsrid(roads.geom, 4326)
- )
- < 1
- ORDER BY
- st_distance(
- loon_habitat.geom,
- st_setsrid(roads.geom, 4326)
- )
- ASC
- LIMIT
- 20
- )
-SELECT
- DISTINCT road_name
-FROM
- nearby_roads
-LIMIT
- 10;
-~~~
-
-~~~
- prime_name
------------------------
- US Route 9
- State Route 30
- State Route 22 Spur
- State Route 22
- State Route 28
- State Route 86
- State Route 28N
- Interstate 87
- US Route 11
- State Route 421
-(10 rows)
-
-Time: 1.447s total (execution 1.446s / network 0.000s)
-~~~
-
-Unfortunately, this query is a bit slower than you would like: about 1.5 seconds on a single-node [`cockroach demo`]({% link {{ page.version.version }}/cockroach-demo.md %}) cluster on a laptop. There are several reasons for this:
-
-1. You haven't created any indexes at all yet. The query is likely to be doing full table scans, which you will need to hunt down with [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}).
-1. CockroachDB does not yet have built-in support for index-based nearest neighbor queries. If this feature is important to you, please comment with some information about your use case on [cockroachdb/cockroach#55227](https://github.com/cockroachdb/cockroach/issues/55227).
-
-Let's look at the `EXPLAIN` output to see if there is something that can be done to improve this query's performance:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-EXPLAIN WITH loon_habitat AS (SELECT st_collect(routes.geom) AS geom FROM birds.birds, birds.observations, birds.routes WHERE birds.name = 'Common Loon' AND birds.id = observations.bird_id AND observations.route_id = routes.id), nearby_roads AS (SELECT roads.prime_name AS road_name FROM roads, loon_habitat WHERE st_distance(loon_habitat.geom, st_setsrid(roads.geom, 4326)) < 1 ORDER BY st_distance(loon_habitat.geom, st_setsrid(roads.geom, 4326)) ASC LIMIT 20) SELECT DISTINCT road_name FROM nearby_roads LIMIT 10;
-~~~
-
-{% include_cached copy-clipboard.html %}
-~~~
- tree | field | description
--------------------------------------------------------------------+---------------------+---------------------------------------------------------
- | distribution | full
- | vectorized | true
- limit | |
- │ | count | 10
- └── distinct | |
- │ | distinct on | road_name
- └── render | |
- └── limit | |
- │ | count | 20
- └── sort | |
- │ | order | +column48
- └── render | |
- └── cross join | |
- │ | pred | st_dwithinexclusive(geom, st_setsrid(geom, 4326), 1.0)
- ├── scan | |
- │ | estimated row count | 225838
- │ | table | roads@primary
- │ | spans | FULL SCAN
- └── render | |
- └── group (scalar) | |
- └── hash join | |
- │ | equality | (route_id) = (id)
- │ | right cols are key |
- ├── hash join | |
- │ │ | equality | (bird_id) = (id)
- │ │ | right cols are key |
- │ ├── scan | |
- │ │ | Estimated row count | 85731
- │ │ | table | observations@primary
- │ │ | spans | FULL SCAN
- │ └── filter | |
- │ │ | filter | name = 'Common Loon'
- │ └── scan | |
- │ | estimated row count | 756
- │ | table | birds@primary
- │ | spans | FULL SCAN
- └── scan | |
- | estimated row count | 129
- | table | routes@primary
- | spans | FULL SCAN
-(40 rows)
-~~~
-
-As you can see above, there are several full table scans being performed (see `FULL SCAN`). For each table, you have a hypothesis on what is causing the scan; usually the hypothesis is that the table is missing an index.
-
-- For [the `roads` table](#the-roads-table), there is no [spatial index]({% link {{ page.version.version }}/spatial-indexes.md %}) on the `road.geom` column. This is probably the worst performance offender of the bunch, since this table has almost a quarter of a million rows. It makes sense that it's quite large, since it represents a road map of the entire United States.
- - In addition to adding an index to `roads.geom`, you will need to update the SRID of the `roads.geom` column to use [SRID 4326]({% link {{ page.version.version }}/srid-4326.md %}) so it matches the SRID of the loon habitat geometry. Updating the SRID to match will allow you to stop using `ST_SetSRID` on `roads.geom` as shown above, which will keep CockroachDB from taking advantage of the spatial index on that column.
-- On [the `birds.observations` table](#the-birds-database), there is no index on the `birds.observations.route_id` or `birds.observations.bird_id` columns, which are used for all bird information lookups.
-- On the [`birds.birds` table](#the-birds-database), there is no index on the `birds.birds.name` column which you're filtering on (this is also used by almost all of your other bird-related queries).
-
-Based on these hypotheses, you take the following steps:
-
-1. Add an index to the `roads.geom` column. Note that creating the index on `roads.geom` takes about a minute on a single-node [`cockroach demo`]({% link {{ page.version.version }}/cockroach-demo.md %}) cluster, since the table is relatively large.
-
- {% include_cached copy-clipboard.html %}
- ~~~ sql
- CREATE INDEX ON roads USING GIST(geom);
- ~~~
-
-1. Update the SRID of the `roads.geom` column from 0 to [4326]({% link {{ page.version.version }}/srid-4326.md %}). This will take a few seconds.
-
- {% include_cached copy-clipboard.html %}
- ~~~ sql
- UPDATE roads SET geom = st_transform(st_setsrid(geom, 4326), 4326) WHERE gid IS NOT NULL RETURNING NOTHING;
- ~~~
-
-1. Add indexes on the `birds.observations.route_id` and `birds.observations.bird_id` columns:
-
- {% include_cached copy-clipboard.html %}
- ~~~ sql
- CREATE INDEX ON birds.observations(bird_id);
- ~~~
-
- {% include_cached copy-clipboard.html %}
- ~~~ sql
- CREATE INDEX ON birds.observations(route_id);
- ~~~
-
-1. Add an index on the `birds.birds.name` column:
-
- {% include_cached copy-clipboard.html %}
- ~~~ sql
- CREATE INDEX ON birds.birds(name);
- ~~~
-
-Did adding all of these indexes and updating the road geometry SRIDs make this query any faster? Let's check. Note that the query below has been modified to no longer use the `ST_SetSRID` function.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_collect(routes.geom) AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- ),
- nearby_roads
- AS (
- SELECT
- roads.prime_name AS road_name
- FROM
- roads, loon_habitat
- WHERE
- st_distance(loon_habitat.geom, roads.geom)
- < 1
- ORDER BY
- st_distance(loon_habitat.geom, roads.geom)
- ASC
- LIMIT
- 20
- )
-SELECT
- DISTINCT road_name
-FROM
- nearby_roads
-LIMIT
- 10;
-~~~
-
-It looks like the answer is yes; you have sped up this query by about 30%:
-
-~~~
- prime_name
------------------------
- US Route 9
- State Route 30
- State Route 22 Spur
- State Route 22
- State Route 28
- State Route 86
- State Route 28N
- Interstate 87
- US Route 11
- State Route 421
-(10 rows)
-
-Time: 998ms total (execution 998ms / network 0ms)
-~~~
-
-To see why, look at the [`EXPLAIN`]({% link {{ page.version.version }}/explain.md %}) output:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-EXPLAIN WITH loon_habitat AS (SELECT st_collect(routes.geom) AS geom FROM birds.birds, birds.observations, birds.routes WHERE birds.name = 'Common Loon' AND birds.id = observations.bird_id AND observations.route_id = routes.ID), nearby_roads AS (SELECT roads.prime_name AS road_name FROM roads, loon_habitat WHERE st_distance(loon_habitat.geom, roads.geom) < 1 ORDER BY st_distance(loon_habitat.geom, roads.geom) ASC LIMIT 20) SELECT DISTINCT road_name FROM nearby_roads LIMIT 10;
-~~~
-
-~~~
- tree | field | description
---------------------------------------------------------------------------+-----------------------+----------------------------------------
- | distribution | full
- | vectorized | false
- limit | |
- │ | count | 10
- └── distinct | |
- │ | distinct on | road_name
- └── render | |
- └── limit | |
- │ | count | 20
- └── sort | |
- │ | order | +column49
- └── render | |
- └── lookup join | |
- │ | table | roads@primary
- │ | equality | (gid) = (gid)
- │ | equality cols are key |
- │ | pred | st_dwithinexclusive(geom, geom, 1.0)
- └── inverted join | |
- │ | table | roads@roads_geom_idx
- └── render | |
- └── group (scalar) | |
- └── hash join | |
- │ | equality | (route_id) = (id)
- │ | right cols are key |
- ├── lookup join | |
- │ │ | table | observations@primary
- │ │ | equality | (id) = (id)
- │ │ | equality cols are key |
- │ └── lookup join | |
- │ │ | table | observations@observations_bird_id_idx
- │ │ | equality | (id) = (bird_id)
- │ └── scan | |
- │ | estimated row count | 1
- │ | table | birds@birds_name_idx
- │ | spans | [/'Common Loon' - /'Common Loon']
- └── scan | |
- | estimated row count | 129
- | table | routes@primary
- | spans | FULL SCAN
-(39 rows)
-~~~
-
-Based on the output above, you notice the following improvements:
-
-- The [spatial index]({% link {{ page.version.version }}/spatial-indexes.md %}) on `roads.geom` is being used by the join between that column and the loon habitat geometry. This is the biggest reason for improved performance, since [the `roads` table](#the-roads-table) has ~225,000 rows.
-- You no longer appear to be scanning all ~85,000 rows of [the `birds.observations` table](#the-birds-database) thanks to the index on the `birds.observations.bird_id` column.
-- You are probably getting a small speedup from the index on [`birds.birds.name`](#the-birds-database) (although that table is not very large, only about 750 rows).
-
-### (16) How many miles of roads are contained within the portion of the Loon habitat that lies within NY state?
-
-It may not be immediately relevant to your birdwatching or book-buying travels, but a question of general interest that could arise is: How many miles of roads are contained within loon habitat?
-
-To answer this:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above CTE and a query against [the `roads` table](#the-roads-table) that sums the mileage of all roads that are contained within the convex hull of loon habitat.
-
-{{site.data.alerts.callout_info}}
-Because you are using `ST_Contains`, the query below only sums the road mileages of roads whose geometries lie entirely within the loon habitat.
-{{site.data.alerts.end}}
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS hull
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- sum(roads.miles)
-FROM
- roads, loon_habitat
-WHERE
- roads.state = 'NY'
- AND st_contains(
- loon_habitat.hull,
- roads.geom
- );
-~~~
-
-The answer to your question is that there are about 1,700 miles of roads within the Common Loon's habitat. Further, you can change the `WHERE` clause of the above query slightly to say `AND (NOT (ST_Contains(...)))` to find out how many miles of roads in NY state are _not_ within loon habitat (it's about 14,600).
-
-~~~
- sum
------------------------
- 1704.65731711040306
-(1 row)
-~~~
-
-### (17) Which bookstore in the Loon's region in NY has the fewest miles of roads within a 10 mile radius of the store?
-
-As you are driving around the Adirondacks, searching for loons as well as your next great read, the question occurs to you: Which bookstore is the most remotely located? This is important since one of your goals for this vacation is to "get away from it all." You do not have population density data, but you do have road data. Therefore you decide to use "miles of roads near the store" as a rough proxy for which store location is the most remote: the fewer miles of roads near the store, the more remote you can assume it is.
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Build a CTE that joins [the `bookstores` table](#the-bookstores-and-bookstore_routes-tables) and the results of the above subquery to generate a set of bookstores inside the loon's habitat area.
-1. Finally, generate a query that joins the results of the above subquery against [the `roads` table](#the-roads-table) based on which roads are within a 10 mile radius. This generates a list of bookstores and the number of miles of nearby roads, sorted in order of which store has the fewest miles of road nearby.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- ),
- loon_bookstores
- AS (
- SELECT
- bookstores.name,
- address,
- bookstores.geom AS geom
- FROM
- bookstores, loon_habitat
- WHERE
- st_contains(
- loon_habitat.geom,
- bookstores.geom
- )
- )
-SELECT
- loon_bookstores.name,
- address,
- sum(roads.miles)::INT AS nearby_road_miles
-FROM
- roads, loon_bookstores
-WHERE
- st_distance(
- loon_bookstores.geom,
- roads.geom
- )
- < (10 / 69)
-GROUP BY
- loon_bookstores.name, address
-ORDER BY
- nearby_road_miles ASC;
-~~~
-
-You get an answer, but unfortunately it's rather slow: about 6 seconds!
-
-~~~
- name | address | nearby_road_miles
----------------------------------------------------+------------------------------------------------+--------------------
- The Bookstore Plus Music & Art | 2491 Main St, Lake Placid, NY, 12946 | 40
- The Book Nook (Saranac Lake, NY) | 7 Broadway, Saranac Lake, NY, 12983 | 81
- Blacktree Books | 5006 State Highway 23, Oneonta, NY, 13820 | 106
- The Green Toad Bookstore | 198 Main St, Oneonta, NY, 13820 | 107
- Gansevoort House Books at Gems Along the Mohawk | 800 Mohawk St, Herkimer, NY, 13350 | 136
- Gansevoort House Books at The Shoppes at 25 West | 25 W Mill Street, Little Falls, NY, 13365 | 155
- The Treehouse Reading and Arts Center | 587 Main St Ste 304, New York Mills, NY, 13417 | 156
- Mysteries On Main Street | 144 W Main St, Johnstown, NY, 12095 | 172
-(8 rows)
-
-Time: 6.214s total (execution 6.214s / network 0.000s)
-~~~
-
-Let's look at the `EXPLAIN` output to see if there is something that can be done to improve this query's performance:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-EXPLAIN WITH loon_habitat AS (SELECT st_convexhull(st_collect(routes.geom)) AS geom FROM birds.birds, birds.observations, birds.routes WHERE birds.name = 'Common Loon' AND birds.id = observations.bird_id AND observations.route_id = routes.id), loon_bookstores AS (SELECT bookstores.name, address, bookstores.geom AS geom FROM bookstores, loon_habitat WHERE st_contains(loon_habitat.geom, bookstores.geom)) SELECT loon_bookstores.name, address, sum(roads.miles)::INT AS nearby_road_miles FROM roads, loon_bookstores WHERE st_distance(loon_bookstores.geom, roads.geom) < (10 / 69) GROUP BY loon_bookstores.name, address ORDER BY nearby_road_miles ASC;
-~~~
-
-~~~
- tree | field | description
-----------------------------------------------------------------+-----------------------+-------------------------------------------------------
- | distribution | full
- | vectorized | true
- sort | |
- │ | order | +nearby_road_miles
- └── render | |
- └── group | |
- │ | group by | name, address
- └── cross join | |
- │ | pred | st_dwithinexclusive(geom, geom, 0.14492753623188406)
- ├── scan | |
- │ | estimated row count | 225838
- │ | table | roads@primary
- │ | spans | FULL SCAN
- └── render | |
- └── cross join | |
- │ | pred | st_contains(geom, geom)
- ├── scan | |
- │ | estimated row count | 2913
- │ | table | bookstores@primary
- │ | spans | FULL SCAN
- └── render | |
- └── group (scalar) | |
- └── hash join | |
- │ | equality | (route_id) = (id)
- │ | right cols are key |
- ├── lookup join | |
- │ │ | table | observations@primary
- │ │ | equality | (id) = (id)
- │ │ | equality cols are key |
- │ └── lookup join | |
- │ │ | table | observations@observations_bird_id_idx
- │ │ | equality | (id) = (bird_id)
- │ └── scan | |
- │ | estimated row count | 1
- │ | table | birds@birds_name_idx
- │ | spans | [/'Common Loon' - /'Common Loon']
- └── scan | |
- | estimated row count | 129
- | table | routes@primary
- | spans | FULL SCAN
-(40 rows)
-~~~
-
-Looking at these results from the bottom up, you can see that:
-
-1. There is a full table scan happening on the `birds.birds.routes` table. Since it's so small (129 rows), let's move on for now.
-1. There is a full table scan on the `bookstores` table. Since the predicate is [`ST_Contains`]({% link {{ page.version.version }}/st_contains.md %}), you probably need to add a [spatial index]({% link {{ page.version.version }}/spatial-indexes.md %}) on `bookstores.geom`.
-1. There is a full table scan happening on the `roads` table. This is a serious problem due to the size of that table: ~225,000 rows. You already have an index on the `roads.geom` column for the `ST_DWithin` predicate to use, so you need to find another way to reduce the number of rows scanned. You can do what you did in [query 16][q_16] and add a check that the `roads.state = 'NY'`, since you are only looking at roads inside New York State.
-
-Based on these observations, you add an index to the `bookstores.geom` column:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-CREATE INDEX ON bookstores USING GIST(geom);
-~~~
-
-After adding the index, you modify your query as shown below to filter on `roads.state`. This looks only at roads in New York (as in [query 16][q_16]):
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_convexhull(st_collect(routes.geom))
- AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- ),
- loon_bookstores
- AS (
- SELECT
- bookstores.name,
- address,
- bookstores.geom AS geom
- FROM
- bookstores, loon_habitat
- WHERE
- st_contains(
- loon_habitat.geom,
- bookstores.geom
- )
- )
-SELECT
- loon_bookstores.name,
- address,
- sum(roads.miles)::INT8 AS nearby_road_miles
-FROM
- roads, loon_bookstores
-WHERE
- roads.state = 'NY'
- AND st_distance(
- roads.geom,
- loon_bookstores.geom
- )
- < (10 / 69)
-GROUP BY
- loon_bookstores.name, address
-ORDER BY
- nearby_road_miles ASC;
-~~~
-
-The steps above brings your query execution down to about 400ms!
-
-~~~
- name | address | nearby_road_miles
----------------------------------------------------+------------------------------------------------+--------------------
- The Bookstore Plus Music & Art | 2491 Main St, Lake Placid, NY, 12946 | 40
- The Book Nook (Saranac Lake, NY) | 7 Broadway, Saranac Lake, NY, 12983 | 81
- Blacktree Books | 5006 State Highway 23, Oneonta, NY, 13820 | 106
- The Green Toad Bookstore | 198 Main St, Oneonta, NY, 13820 | 107
- Gansevoort House Books at Gems Along the Mohawk | 800 Mohawk St, Herkimer, NY, 13350 | 136
- Gansevoort House Books at The Shoppes at 25 West | 25 W Mill Street, Little Falls, NY, 13365 | 155
- The Treehouse Reading and Arts Center | 587 Main St Ste 304, New York Mills, NY, 13417 | 156
- Mysteries On Main Street | 144 W Main St, Johnstown, NY, 12095 | 172
-(8 rows)
-
-Time: 376ms total (execution 376ms / network 0ms)
-~~~
-
-When you look at `EXPLAIN` for the modified query, you see why: the filter on `roads.state='NY'` means you are scanning far fewer columns of the `roads` table (~8,900), and the index on `bookstores.geom` means you are using that index now as well:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-EXPLAIN WITH loon_habitat AS (SELECT st_convexhull(st_collect(routes.geom)) AS geom FROM birds.birds, birds.observations, birds.routes WHERE birds.name = 'Common Loon' AND birds.id = observations.bird_id AND observations.route_id = routes.id), loon_bookstores AS (SELECT bookstores.name, address, bookstores.geom AS geom FROM bookstores, loon_habitat WHERE st_contains(loon_habitat.geom, bookstores.geom)) SELECT loon_bookstores.name, address, sum(roads.miles)::INT8 AS nearby_road_miles FROM roads, loon_bookstores WHERE roads.state = 'NY' AND st_distance(roads.geom, loon_bookstores.geom) < (10 / 69) GROUP BY loon_bookstores.name, address ORDER BY nearby_road_miles ASC;
-~~~
-
-~~~
- tree | field | description
----------------------------------------------------------------------+-----------------------+-------------------------------------------------------------------------
- | distribution | full
- | vectorized | true
- sort | |
- │ | order | +nearby_road_miles
- └── render | |
- └── group | |
- │ | group by | name, address
- └── cross join | |
- │ | pred | st_dwithinexclusive(geom, geom, 0.14492753623188406)
- ├── index join | |
- │ │ | table | roads@primary
- │ └── scan | |
- │ | estimated row count | 8899
- │ | table | roads@roads_state_idx
- │ | spans | [/'NY' - /'NY']
- └── render | |
- └── lookup join | |
- │ | table | bookstores@primary
- │ | equality | (id) = (id)
- │ | equality cols are key |
- │ | pred | st_contains(geom, geom)
- └── inverted join | |
- │ | table | bookstores@bookstores_geom_idx
- └── render | |
- └── group (scalar) | |
- └── hash join | |
- │ | equality | (route_id) = (id)
- │ | right cols are key |
- ├── lookup join | |
- │ │ | table | observations@primary
- │ │ | equality | (id) = (id)
- │ │ | equality cols are key |
- │ └── lookup join | |
- │ │ | table | observations@observations_bird_id_idx
- │ │ | equality | (id) = (bird_id)
- │ └── scan | |
- │ | estimated row count | 1
- │ | table | birds@birds_name_idx
- │ | spans | [/'Common Loon' - /'Common Loon']
- └── scan | |
- | estimated row count | 129
- | table | routes@primary
- | spans | FULL SCAN
-~~~
-
-## Step 8. Find other birds
-
-### (18) What are the hawks sighted in the same region as the Common Loon?
-
-If you get tired of looking for Common Loons (and it's understandable that you might), you may want to widen your search to other types of birds. You have already looked at [different bird species based on frequency of observation][q_13]. Now you turn your attention to a specific type of bird: hawks.
-
-Due to your long birdwatching experience, you recall that hawks are in the family "Accipitridae". You can use this knowledge to answer the question: What hawks live in the same region as the Common Loon in New York State?
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above subquery with [the `birds` database](#the-birds-database) to find the names and observation counts of the birds that:
- 1. Are in the family "Accipitridae".
- 1. Have sighting locations whose geometry is contained by the hull describing the Common Loon's habitat.
-1. Order the birds in the list by how frequently they are sighted, since you may want to look for the most common hawks first.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_collect(routes.geom) AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- birds.name, sum(birds.observations.count) AS sightings
-FROM
- birds.birds,
- birds.observations,
- birds.routes,
- loon_habitat
-WHERE
- birds.family = 'Accipitridae'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- AND st_contains(loon_habitat.geom, routes.geom)
-GROUP BY
- birds.name
-ORDER BY
- sightings DESC;
-~~~
-
-Based on this data, it looks like the most commonly sighted hawk by far in the area over the years 2000-2019 is the Broad-winged Hawk:
-
-~~~
- name | sightings
------------------------+------------
- Broad-winged Hawk | 115
- Red-tailed Hawk | 33
- Northern Harrier | 12
- Red-shouldered Hawk | 11
- Sharp-shinned Hawk | 9
- Bald Eagle | 9
- Cooper Hawk | 7
- Northern Goshawk | 5
- unid. Accipiter hawk | 1
-(9 rows)
-~~~
-
-### (19) What if you also want to look for owls as well as hawks?
-
-If you are a fan of owls as well as hawks, you can make several updates to [the previous query][q_18], which just looked for hawks. In particular, you want to group the hawks together and the owls together.
-
-To answer this question:
-
-1. Build a CTE that returns the [convex hull]({% link {{ page.version.version }}/st_convexhull.md %}) of Common Loon habitat.
-1. Join the results of the above subquery with [the `birds` database](#the-birds-database) to find the names and observation counts of the birds that:
- 1. Are in the family "Accipitridae" (hawks) _or_ the family "Strigidae" (owls).
- 1. Have sighting locations whose geometry is contained by the hull describing the Common Loon's habitat.
-1. Group the birds by name and family, and within each grouping order the birds by how frequently they are sighted, since you may want to look for the most common hawks or owls first.
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
-WITH
- loon_habitat
- AS (
- SELECT
- st_collect(routes.geom) AS geom
- FROM
- birds.birds,
- birds.observations,
- birds.routes
- WHERE
- birds.name = 'Common Loon'
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- )
-SELECT
- birds.name,
- birds.family,
- sum(birds.observations.count) AS sightings
-FROM
- birds.birds,
- birds.observations,
- birds.routes,
- loon_habitat
-WHERE
- (
- birds.family = 'Accipitridae'
- OR birds.family = 'Strigidae'
- )
- AND birds.id = observations.bird_id
- AND observations.route_id = routes.id
- AND st_contains(loon_habitat.geom, routes.geom)
-GROUP BY
- birds.name, birds.family
-ORDER BY
- birds.family, sightings DESC;
-~~~
-
-~~~
- name | family | sightings
-------------------------+--------------+------------
- Broad-winged Hawk | Accipitridae | 115
- Red-tailed Hawk | Accipitridae | 33
- Northern Harrier | Accipitridae | 12
- Red-shouldered Hawk | Accipitridae | 11
- Sharp-shinned Hawk | Accipitridae | 9
- Bald Eagle | Accipitridae | 9
- Cooper Hawk | Accipitridae | 7
- Northern Goshawk | Accipitridae | 5
- unid. Accipiter hawk | Accipitridae | 1
- Barred Owl | Strigidae | 44
- Eastern Screech-Owl | Strigidae | 3
- Great Horned Owl | Strigidae | 1
- Northern Saw-whet Owl | Strigidae | 1
-(13 rows)
-~~~
-
-## Next steps
-
-Now that you are familiar with writing and tuning spatial queries, you are ready for the following steps:
-
-- Learn more details about the [spatial features]({% link {{ page.version.version }}/spatial-data-overview.md %}) supported by CockroachDB.
-
-- Learn how to migrate spatial data from [shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %}), [GeoPackages]({% link {{ page.version.version }}/migrate-from-geopackage.md %}), [GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %}), or [OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %}) into CockroachDB.
-
-- Learn how CockroachDB's [spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %}) work.
-
-## Appendix
-
-### Spatial features and corresponding queries
-
-The queries are presented above in a "narrative order" that corresponds roughly to the order in which you might ask questions about bookstores and loon habitat as you plan for your vacation.
-
-However, you may just want to see what queries are exercising a specific [spatial feature]({% link {{ page.version.version }}/spatial-data-overview.md %}) supported by CockroachDB. The table below provides a mapping from a feature (such as 'spatial indexing' or 'spatial joins') to the queries that use that feature.
-
-| Feature | Queries | Description(s) |
-|------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Spatial data set preparation | N/A | For information about how this data set was prepared, see the relevant portions of the [Data set description](#data-set-description). |
-| Loading spatial data | N/A | For loading instructions on loading the data set, see [Loading the data set](#step-3-load-the-data-set). |
-| Spatial joins | [#6][q_06], [#7][q_07], [#8][q_08], [#9][q_09], [#10][q_10], [#13][q_13], [#14][q_14], [#15][q_15], [#16][q_16], [#17][q_17], [#18][q_18], [#19][q_19] | Joins that involve comparing spatial features from two or more tables, usually with a predicate such as [`ST_Contains`]({% link {{ page.version.version }}/st_contains.md %}). |
-| Spatial indexing | [#15][q_15], [#17][q_17] | These queries show explicit spatial index creation; many of the spatial join queries listed above could use these indexes, once created. |
-| Spatial relationships | [#6][q_06], [#7][q_07], [#8][q_08], [#9][q_09], [#10][q_10], [#13][q_13], [#14][q_14], [#15][q_15], [#16][q_16], [#17][q_17], [#18][q_18], [#19][q_19] | Comparing spatial relationships with predicates such as [`ST_Contains`]({% link {{ page.version.version }}/st_contains.md %}) and `ST_DWithin`. |
-| Nearest-neighbor searching | [#15][q_15] | This query shows how to execute a nearest-neighbor search using CockroachDB. |
-
-### Data set description
-
-This section contains an overview of the data sets and how they were constructed and put together. Because this data set is a "compound data set" that is built from several other data sets, there is some complexity to the schema that you will have to navigate.
-
-The data set contains two databases:
-
-- [The `tutorial` database](#the-tutorial-database)
-- [The `birds` database](#the-birds-database)
-
-#### The `tutorial` database
-
-The `tutorial` database contains the following tables:
-
-- [The `bookstores` table](#the-bookstores-and-bookstore_routes-tables) stores the independent bookstore information such as name, address, geo-location, etc.
-- [The `bookstore_routes` table](#the-bookstores-and-bookstore_routes-tables) is a small table containing just the routes between the local bookstores that lie within the loon's Adirondack habitat (this will be explained in more detail below).
-- [The `roads` table](#the-roads-table) is where the U.S. National Atlas road data is kept.
-
-##### The `bookstores` and `bookstore_routes` tables
-
-Below is an entity-relationship diagram showing the `bookstores` and `bookstore_routes` tables (generated using [DBeaver]({% link {{ page.version.version }}/dbeaver.md %})):
-
-
-
-As mentioned above, the `bookstores` table was created by scraping web data from the [American Booksellers Association website's member directory](https://bookweb.org/member_directory/search/ABAmember). In addition, the `geom` column was constructed by doing some [address geocoding](https://wikipedia.org/wiki/Address_geocoding) that converted each bookstore's address to a lon/lat pair and converted to a spatial object using `ST_MakePoint`. For each bookstore, the script did a bit of parsing and geocoding and ran essentially the following query:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
--- sqlchecker: ignore
-INSERT
-INTO
- bookstores
- (
- name,
- url,
- phone,
- address,
- street,
- city,
- state,
- zip,
- description,
- geom
- )
-VALUES
- (
- '$name',
- '$url',
- '$phone',
- '$address',
- '$street',
- '$city',
- '$state',
- '$zip',
- '$description',
- (
- SELECT
- st_setsrid(
- st_makepoint('$lon', '$lat'),
- 4326
- )
- )
- );
-~~~
-
-Similarly, the paths (specifically [MultiLinestrings]({% link {{ page.version.version }}/multilinestring.md %})) between bookstores in the `bookstore_routes` table were also calculated using a script that made use of a simple greedy algorithm to find paths between the bookstores using the `roads` table (described below). The algorithm always looks for the next road that will bring you closer to the destination. It is fine for this small use case, but it is not robust enough for any kind of production use, since it does not perform any backtracking when it gets stuck in a local optimum. The algorithm works as follows, and assumes a starting bookstore _A_ and an ending bookstore _B_:
-
-1. (Start) Find the road nearest _A_; call this road _R_A.
-1. Find the road nearest the destination _B_; call this road _R_B.
-1. Do _R_A and _R_B intersect? If so, the algorithm is done. You have arrived at your destination. (This is the recursive base case.)
-1. Otherwise, find the roads that intersect with _R_A. Of these roads, choose the one that passes closest to _R_B. Call it _R_A'.
-1. Do _R_A' and _R_B intersect? If so, the algorithm is done. You have arrived at your destination.
-1. If the algorithm has arrived at this step without finishing, it goes back to step 4 and applies that step to the current "closest road to destination" _R_A', and keeps operating steps 4-6 recursively until it has found a complete path or it hits some kind of heuristic timeout.
-
-{{site.data.alerts.callout_info}}
-For more information about production quality map routing software that uses OpenStreetMap, see [the OpenStreetMap wiki page on Routing](https://wiki.openstreetmap.org/wiki/Routing).
-{{site.data.alerts.end}}
-
-{{site.data.alerts.callout_info}}
-There are multiple ways to do geocoding. You can use REST API-based services or do the geocoding yourself by processing [TIGER/LINE address data](https://www2.census.gov/geo/tiger/tigerua/) using a library for that purpose. For more information about some of the options available to you, see [this OpenStreetMap wiki page on geocoders](https://wiki.openstreetmap.org/wiki/Search_engines) or [this list of geocoding software](http://www.tsusiatsoftware.net/geocode.html).
-{{site.data.alerts.end}}
-
-##### The `roads` table
-
-Meanwhile, the `roads` table has many columns; the most important ones used in this tutorial are `state`, `geom`, `miles`, and `prime_name` (the human-readable name of the road).
-
-
-
-For more information about what the other columns in `roads` mean, see the [full data set description](https://www.sciencebase.gov/catalog/file/get/581d052be4b08da350d524ce?f=__disk__60%2F6b%2F4e%2F606b4e564884da8cca57ffeb229cd817006616e0&transform=1&allowOpen=true).
-
-{{site.data.alerts.callout_info}}
-The `roads` table was imported from a [shapefile](https://prd-tnm.s3.amazonaws.com/StagedProducts/Small-scale/data/Transportation/roadtrl010g.shp_nt00920.tar.gz) using the method described in [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %}).
-{{site.data.alerts.end}}
-
-#### The `birds` database
-
-The `birds` database contains several tables that you will have to [join]({% link {{ page.version.version }}/joins.md %}) together in most of the queries shown elsewhere on this page. The multi-table design reflects [the schema of the actual data set](https://www.sciencebase.gov/catalog/file/get/5ea04e9a82cefae35a129d65?f=__disk__b4%2F2f%2Fcf%2Fb42fcfe28a799db6e8c97200829ea1ebaccbf8ea&transform=1&allowOpen=true), which is split across files that map to these tables.
-
-The tables in the `birds` database are diagrammed below:
-
-- `birds` is a list of ~750 bird species. Most queries will use the `name` column.
-- `routes` is a list of ~130 prescribed locations that the birdwatchers helping with the survey visit each year. The `geom` associated with each route is a [Point]({% link {{ page.version.version }}/point.md %}) marking the latitude and longitude of the route's starting point. For details, see the [schema](https://www.sciencebase.gov/catalog/file/get/5ea04e9a82cefae35a129d65?f=__disk__b4%2F2f%2Fcf%2Fb42fcfe28a799db6e8c97200829ea1ebaccbf8ea&transform=1&allowOpen=true) (search for the text "routes.csv").
-- `observations` describes the ~85,000 times and places in which birds of various species were actually seen. The `bird_id` is a [foreign key]({% link {{ page.version.version }}/foreign-key.md %}) to the ID in the `birds` table, and the `route_id` points to the ID of the `routes` table.
-
-
-
-Each of these tables were populated using a script that parsed [the CSV files available for download](https://www.sciencebase.gov/catalog/item/52b1dfa8e4b0d9b325230cd9) and added the data using [`INSERT`]({% link {{ page.version.version }}/insert.md %}) statements. For the `routes` table, once again the `ST_MakePoint` function was used to create a geometry from the lon/lat values in the CSV as follows:
-
-{% include_cached copy-clipboard.html %}
-~~~ sql
--- sqlchecker: ignore
-INSERT
-INTO
- routes (id, name, country, state, geom)
-VALUES
- (
- '$route_id',
- '$route_name',
- '$country',
- '$state',
- (
- SELECT
- st_setsrid(
- st_makepoint('$lon', '$lat'),
- 4326
- )
- )
- );
-~~~
-
-{{site.data.alerts.callout_info}}
-This data is stored in a separate `birds` database due to the fact that it is split into several tables. It could also have been added to the `tutorial` database by naming the tables something like `bird_species`, `bird_routes`, and `bird_observations`.
-{{site.data.alerts.end}}
-
-## See also
-
-- [Install CockroachDB]({% link {{ page.version.version }}/install-cockroachdb.md %})
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial Data Overview]({% link {{ page.version.version }}/spatial-data-overview.md %})
-- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
-- [Spatial & GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
-- [Migrate from Shapefiles]({% link {{ page.version.version }}/migrate-from-shapefiles.md %})
-- [Migrate from GeoJSON]({% link {{ page.version.version }}/migrate-from-geojson.md %})
-- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
-- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
-- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
-- [POINT]({% link {{ page.version.version }}/point.md %})
-- [LINESTRING]({% link {{ page.version.version }}/linestring.md %})
-- [POLYGON]({% link {{ page.version.version }}/polygon.md %})
-- [MULTIPOINT]({% link {{ page.version.version }}/multipoint.md %})
-- [MULTILINESTRING]({% link {{ page.version.version }}/multilinestring.md %})
-- [MULTIPOLYGON]({% link {{ page.version.version }}/multipolygon.md %})
-- [GEOMETRYCOLLECTION]({% link {{ page.version.version }}/geometrycollection.md %})
-- [Well known text]({% link {{ page.version.version }}/well-known-text.md %})
-- [Well known binary]({% link {{ page.version.version }}/well-known-binary.md %})
-- [GeoJSON]({% link {{ page.version.version }}/geojson.md %})
-- [SRID 4326 - longitude and latitude]({% link {{ page.version.version }}/srid-4326.md %})
-- [`ST_Contains`]({% link {{ page.version.version }}/st_contains.md %})
-- [`ST_ConvexHull`]({% link {{ page.version.version }}/st_convexhull.md %})
-- [`ST_CoveredBy`]({% link {{ page.version.version }}/st_coveredby.md %})
-- [`ST_Covers`]({% link {{ page.version.version }}/st_covers.md %})
-- [`ST_Disjoint`]({% link {{ page.version.version }}/st_disjoint.md %})
-- [`ST_Equals`]({% link {{ page.version.version }}/st_equals.md %})
-- [`ST_Intersects`]({% link {{ page.version.version }}/st_intersects.md %})
-- [`ST_Overlaps`]({% link {{ page.version.version }}/st_overlaps.md %})
-- [`ST_Touches`]({% link {{ page.version.version }}/st_touches.md %})
-- [`ST_Union`]({% link {{ page.version.version }}/st_union.md %})
-- [`ST_Within`]({% link {{ page.version.version }}/st_within.md %})
-- [Troubleshooting overview]({% link {{ page.version.version }}/troubleshooting-overview.md %})
-- [Support resources]({% link {{ page.version.version }}/support-resources.md %})
-
-{% comment %} Reference links {% endcomment %}
-
-[q_01]: #1-where-has-the-common-loon-been-sighted-by-the-nabbs-in-the-years-2000-2019-in-ny-state
-[q_02]: #2-what-is-the-total-area-of-loon-sightings
-[q_03]: #3-how-many-loon-sightings-have-there-been-in-ny-state-in-the-years-2000-2019
-[q_04]: #4-how-many-loon-sightings-were-there-in-ny-state-in-just-the-year-2019
-[q_05]: #5-what-is-the-density-of-loon-sightings-per-square-mile-in-its-range-as-defined-by-this-data-set-in-2019
-[q_06]: #6-what-are-the-bookstores-that-lie-within-the-loons-habitat-range-in-ny-state
-[q_07]: #7-how-many-different-species-of-bird-habitats-contain-the-location-of-the-book-nook-in-saranac-lake-ny
-[q_08]: #8-which-25-birds-were-most-often-sighted-within-10-miles-of-the-book-nook-in-saranac-lake-ny-during-the-2000-2019-observation-period
-[q_09]: #9-what-does-the-shape-of-all-bookstore-locations-that-lie-within-the-loons-habitat-look-like
-[q_10]: #10-what-is-the-area-of-the-shape-of-all-bookstore-locations-that-are-in-the-loons-habitat-range-within-ny-state
-[q_11]: #11-how-long-is-the-route-from-mysteries-on-main-street-in-johnstown-ny-to-the-book-nook-in-saranac-lake-ny
-[q_12]: #12-what-does-the-route-from-mysteries-on-main-street-in-johnstown-ny-to-the-book-nook-in-saranac-lake-ny-look-like
-[q_13]: #13-what-were-the-25-most-commonly-sighted-birds-in-2019-within-10-miles-of-the-route-between-mysteries-on-main-street-in-johnstown-ny-and-the-bookstore-plus-in-lake-placid-ny
-[q_14]: #14-what-are-the-25-least-often-sighted-other-species-of-birds-in-the-loons-sightings-range
-[q_15]: #15-what-are-the-top-10-roads-nearest-to-a-loon-sighting-location-in-ny
-[q_16]: #16-how-many-miles-of-roads-are-contained-within-the-portion-of-the-loon-habitat-that-lies-within-ny-state
-[q_17]: #17-which-bookstore-in-the-loons-region-in-ny-has-the-fewest-miles-of-roads-within-a-10-mile-radius-of-the-store
-[q_18]: #18-what-are-the-hawks-sighted-in-the-same-region-as-the-common-loon
-[q_19]: #19-what-if-you-also-want-to-look-for-owls-as-well-as-hawks
-
-{% comment %} EOF {% endcomment %}
diff --git a/src/current/v26.2/srid-4326.md b/src/current/v26.2/srid-4326.md
index 8cad5bd5680..fac33a2eabc 100644
--- a/src/current/v26.2/srid-4326.md
+++ b/src/current/v26.2/srid-4326.md
@@ -118,11 +118,9 @@ ERROR: st_contains(): operation on mixed SRIDs forbidden: (Point, 0) != (Point,
## See also
- [Spatial Data Overview]({% link {{ page.version.version }}/spatial-data-overview.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Well known text]({% link {{ page.version.version }}/well-known-text.md %})
- [Well known binary]({% link {{ page.version.version }}/well-known-binary.md %})
- [GeoJSON]({% link {{ page.version.version }}/geojson.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/st_contains.md b/src/current/v26.2/st_contains.md
index bbefa91d1ff..df021f60ac2 100644
--- a/src/current/v26.2/st_contains.md
+++ b/src/current/v26.2/st_contains.md
@@ -79,8 +79,6 @@ SELECT st_contains(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95
## See also
-- [Export Spatial Data]({% link {{ page.version.version }}/export-spatial-data.md %})
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -101,4 +99,3 @@ SELECT st_contains(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/st_convexhull.md b/src/current/v26.2/st_convexhull.md
index db6e7dbdcfa..21560b0b8b6 100644
--- a/src/current/v26.2/st_convexhull.md
+++ b/src/current/v26.2/st_convexhull.md
@@ -246,8 +246,6 @@ In this example, we will generate the convex hull of a single geometry. The geo
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -268,4 +266,3 @@ In this example, we will generate the convex hull of a single geometry. The geo
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/st_coveredby.md b/src/current/v26.2/st_coveredby.md
index 6d5ea7b4f2e..b3c284591a9 100644
--- a/src/current/v26.2/st_coveredby.md
+++ b/src/current/v26.2/st_coveredby.md
@@ -72,8 +72,6 @@ SELECT ST_CoveredBy(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -9
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -94,4 +92,3 @@ SELECT ST_CoveredBy(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -9
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/st_covers.md b/src/current/v26.2/st_covers.md
index 4b991c3d9ee..0ec1b9eafb7 100644
--- a/src/current/v26.2/st_covers.md
+++ b/src/current/v26.2/st_covers.md
@@ -74,8 +74,6 @@ SELECT ST_Covers(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -95,4 +93,3 @@ SELECT ST_Covers(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/st_disjoint.md b/src/current/v26.2/st_disjoint.md
index 451ec0e470b..458afa5d543 100644
--- a/src/current/v26.2/st_disjoint.md
+++ b/src/current/v26.2/st_disjoint.md
@@ -73,8 +73,6 @@ SELECT st_disjoint(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -95,4 +93,3 @@ SELECT st_disjoint(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/st_equals.md b/src/current/v26.2/st_equals.md
index dfe0d847d4e..8a41dc956e4 100644
--- a/src/current/v26.2/st_equals.md
+++ b/src/current/v26.2/st_equals.md
@@ -69,8 +69,6 @@ SELECT st_equals(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -91,4 +89,3 @@ SELECT st_equals(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/st_intersects.md b/src/current/v26.2/st_intersects.md
index 5c37a5be90f..88fb3ae25a0 100644
--- a/src/current/v26.2/st_intersects.md
+++ b/src/current/v26.2/st_intersects.md
@@ -70,8 +70,6 @@ SELECT st_intersects(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -92,4 +90,3 @@ SELECT st_intersects(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/st_overlaps.md b/src/current/v26.2/st_overlaps.md
index 3072009f7c3..3312131b6b5 100644
--- a/src/current/v26.2/st_overlaps.md
+++ b/src/current/v26.2/st_overlaps.md
@@ -71,8 +71,6 @@ SELECT st_overlaps(st_geomfromtext('SRID=4326;POLYGON((-79.995888 40.440624,-74.
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -93,4 +91,3 @@ SELECT st_overlaps(st_geomfromtext('SRID=4326;POLYGON((-79.995888 40.440624,-74.
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/st_touches.md b/src/current/v26.2/st_touches.md
index 813ea527048..3590989364d 100644
--- a/src/current/v26.2/st_touches.md
+++ b/src/current/v26.2/st_touches.md
@@ -71,8 +71,6 @@ SELECT st_touches(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -93,4 +91,3 @@ SELECT st_touches(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/st_union.md b/src/current/v26.2/st_union.md
index 526119dea2e..3f5a33e6330 100644
--- a/src/current/v26.2/st_union.md
+++ b/src/current/v26.2/st_union.md
@@ -247,8 +247,6 @@ In this example, we will generate a single geometry from many individual points
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
@@ -269,4 +267,3 @@ In this example, we will generate a single geometry from many individual points
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/st_within.md b/src/current/v26.2/st_within.md
index a83da70c86b..4c0badceb8a 100644
--- a/src/current/v26.2/st_within.md
+++ b/src/current/v26.2/st_within.md
@@ -77,10 +77,7 @@ SELECT ST_Within(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
## See also
-- [Export Spatial Data](export-spatial-data.html)
-- [Spatial tutorial]({% link {{ page.version.version }}/spatial-tutorial.md %})
- [Spatial and GIS Glossary of Terms]({% link {{ page.version.version }}/architecture/glossary.md %})
-- [Spatial indexes]({% link {{ page.version.version }}/spatial-indexes.md %})
- [Spatial functions]({% link {{ page.version.version }}/functions-and-operators.md %}#spatial-functions)
- [`ST_Covers`]({% link {{ page.version.version }}/st_covers.md %})
- [`ST_CoveredBy`]({% link {{ page.version.version }}/st_coveredby.md %})
@@ -99,4 +96,3 @@ SELECT ST_Within(st_geomfromtext('SRID=4326;POLYGON((-87.906471 43.038902, -95.9
- [Migrate from GeoPackage]({% link {{ page.version.version }}/migrate-from-geopackage.md %})
- [Migrate from OpenStreetMap]({% link {{ page.version.version }}/migrate-from-openstreetmap.md %})
- [Introducing Distributed Spatial Data in CockroachDB](https://www.cockroachlabs.com/blog/spatial-data/) (blog post)
-- [Using GeoServer with CockroachDB]({% link {{ page.version.version }}/geoserver.md %})
diff --git a/src/current/v26.2/start-a-local-cluster-in-docker-linux.md b/src/current/v26.2/start-a-local-cluster-in-docker-linux.md
index 5cc91fc7ab0..fe34cbaa255 100644
--- a/src/current/v26.2/start-a-local-cluster-in-docker-linux.md
+++ b/src/current/v26.2/start-a-local-cluster-in-docker-linux.md
@@ -37,4 +37,4 @@ Once you've [installed the official CockroachDB Docker image]({% link {{ page.ve
- [Create a CockroachDB Cloud account](https://cockroachlabs.cloud/signup?experience=enterprise) where you can [generate and manage licenses]({% link {{ page.version.version }}/licensing-faqs.md %}) for CockroachDB installations
- Learn more about [CockroachDB SQL]({% link {{ page.version.version }}/learn-cockroachdb-sql.md %}) and the [built-in SQL client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [Install the client driver]({% link {{ page.version.version }}/install-client-drivers.md %}) for your preferred language
-- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
+- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/jsonb.md %})
diff --git a/src/current/v26.2/start-a-local-cluster-in-docker-mac.md b/src/current/v26.2/start-a-local-cluster-in-docker-mac.md
index e1d43d8140c..8dabc54bc70 100644
--- a/src/current/v26.2/start-a-local-cluster-in-docker-mac.md
+++ b/src/current/v26.2/start-a-local-cluster-in-docker-mac.md
@@ -34,4 +34,4 @@ Once you've [installed the official CockroachDB Docker image]({% link {{ page.ve
- [Create a CockroachDB Cloud account](https://cockroachlabs.cloud/signup?experience=enterprise) where you can [generate and manage licenses]({% link {{ page.version.version }}/licensing-faqs.md %}) for CockroachDB installations
- Learn more about [CockroachDB SQL]({% link {{ page.version.version }}/learn-cockroachdb-sql.md %}) and the [built-in SQL client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [Install the client driver]({% link {{ page.version.version }}/install-client-drivers.md %}) for your preferred language
-- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
+- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/jsonb.md %})
diff --git a/src/current/v26.2/start-a-local-cluster-in-docker-windows.md b/src/current/v26.2/start-a-local-cluster-in-docker-windows.md
index da1430fd68c..bc53678e6ff 100644
--- a/src/current/v26.2/start-a-local-cluster-in-docker-windows.md
+++ b/src/current/v26.2/start-a-local-cluster-in-docker-windows.md
@@ -366,4 +366,4 @@ The CockroachDB [DB Console]({% link {{ page.version.version }}/ui-overview.md %
- [Create a CockroachDB Cloud account](https://cockroachlabs.cloud/signup?experience=enterprise) where you can [generate and manage licenses]({% link {{ page.version.version }}/licensing-faqs.md %}) for CockroachDB installations
- Learn more about [CockroachDB SQL]({% link {{ page.version.version }}/learn-cockroachdb-sql.md %}) and the [built-in SQL client]({% link {{ page.version.version }}/cockroach-sql.md %})
- [Install the client driver]({% link {{ page.version.version }}/install-client-drivers.md %}) for your preferred language
-- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
+- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/jsonb.md %})
diff --git a/src/current/v26.2/start-a-local-cluster.md b/src/current/v26.2/start-a-local-cluster.md
index c5ed79b8dd8..f2f6af525ef 100644
--- a/src/current/v26.2/start-a-local-cluster.md
+++ b/src/current/v26.2/start-a-local-cluster.md
@@ -389,4 +389,4 @@ Adding capacity is as simple as starting more nodes with `cockroach start`.
- [Create a CockroachDB Cloud account](https://cockroachlabs.cloud/signup?experience=enterprise) where you can [generate and manage licenses]({% link {{ page.version.version }}/licensing-faqs.md %}) for CockroachDB installations
- [Install the client driver]({% link {{ page.version.version }}/install-client-drivers.md %}) for your preferred language
- Learn more about [CockroachDB SQL]({% link {{ page.version.version }}/learn-cockroachdb-sql.md %}) and the [built-in SQL client]({% link {{ page.version.version }}/cockroach-sql.md %})
-- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/demo-json-support.md %})
+- Further explore CockroachDB capabilities like [fault tolerance and automated repair]({% link {{ page.version.version }}/demo-cockroachdb-resilience.md %}), [multi-region performance]({% link {{ page.version.version }}/demo-low-latency-multi-region-deployment.md %}), [serializable transactions]({% link {{ page.version.version }}/demo-serializable.md %}), and [JSON support]({% link {{ page.version.version }}/jsonb.md %})
diff --git a/src/current/v26.2/update-data.md b/src/current/v26.2/update-data.md
deleted file mode 100644
index f78c16677d0..00000000000
--- a/src/current/v26.2/update-data.md
+++ /dev/null
@@ -1,436 +0,0 @@
----
-title: Update Data
-summary: How to update the data in CockroachDB from various programming languages
-toc: true
-docs_area: develop
----
-
-This page has instructions for updating existing rows of data in CockroachDB, using the following [SQL statements]({% link {{ page.version.version }}/sql-statements.md %}):
-
-- [`UPDATE`](#use-update), which updates existing rows in a table.
-- [`UPSERT`](#use-upsert), which inserts new rows in a table, updating existing rows that conflict on a primary key.
-- [`INSERT ... ON CONFLICT ... DO UPDATE`](#use-insert-on-conflict), which inserts new rows in a table, updating existing rows in the event of a conflict on specified, `UNIQUE`-constrained columns.
-
-## Before you begin
-
-Before reading this page, do the following:
-
-- [Create a CockroachDB {{ site.data.products.standard }} cluster]({% link cockroachcloud/quickstart.md %}) or [start a local cluster]({% link cockroachcloud/quickstart.md %}?filters=local).
-- [Install a Driver or ORM Framework]({% link {{ page.version.version }}/install-client-drivers.md %}).
-- [Connect to the database]({% link {{ page.version.version }}/connect-to-the-database.md %}).
-- [Create a database schema]({% link {{ page.version.version }}/schema-design-overview.md %}).
-- [Insert data]({% link {{ page.version.version }}/insert-data.md %}) that you now want to update.
-
- In the examples on this page, we use sample [`movr`]({% link {{ page.version.version }}/movr.md %}) data imported with the [`cockroach workload` command]({% link {{ page.version.version }}/cockroach-workload.md %}).
-
-## Use `UPDATE`
-
-To update existing rows in a table, use an [`UPDATE` statement]({% link {{ page.version.version }}/update.md %}) with a `WHERE` clause that filters on the columns that identify the rows that you want to update.
-
-{{site.data.alerts.callout_info}}
-To update a large number of rows (i.e., tens of thousands of rows or more), we recommend iteratively updating subsets of the rows that you want to update, until all of the rows have been updated. You can write a script to do this, or you can write a loop into your application.
-
-For guidance and an example, see [Bulk-update Data]({% link {{ page.version.version }}/bulk-update-data.md %}).
-{{site.data.alerts.end}}
-
-### `UPDATE` SQL syntax
-
-In SQL, `UPDATE` statements generally take the following form:
-
-~~~
-UPDATE {table} SET {update_column} = {update_value} WHERE {filter_column} = {filter_value}
-~~~
-
-Where:
-
-- `{table}` is the table to update.
-- `{update_column}` is the column to update.
-- `{update_value}` is the new value of the column in the matching row.
-- `{filter_column}` is the column to filter on.
-- `{filter_value}` is the matching value for the filter.
-
-{{site.data.alerts.callout_success}}
-For detailed reference documentation on the `UPDATE` statement, including additional examples, see the [`UPDATE` syntax page]({% link {{ page.version.version }}/update.md %}).
-{{site.data.alerts.end}}
-
-### `UPDATE` best practices
-
-Here are some best practices to follow when updating rows:
-
-- Always specify a `WHERE` clause in `UPDATE` queries. If no `WHERE` clause is specified, CockroachDB will update all of the rows in the specified table.
-- To update a large number of rows (i.e., tens of thousands of rows or more), use a [batch-update loop]({% link {{ page.version.version }}/bulk-update-data.md %}).
-- When executing `UPDATE` statements from an application, make sure that you wrap the SQL-executing functions in [a retry loop that handles transaction errors]({% link {{ page.version.version }}/query-behavior-troubleshooting.md %}#transaction-retry-errors) that can occur under [contention]({% link {{ page.version.version }}/performance-best-practices-overview.md %}#transaction-contention).
-
-### `UPDATE` example
-
-Suppose you want to change the status of all of the vehicles owned by a particular MovR user. To update all rows in the `vehicles` table with the `owner_id` equal to `bd70a3d7-0a3d-4000-8000-000000000025`:
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