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[phase-31 1/4] Sort schema foundation #6242

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d892f23
feat: replace fixed MetricDataPoint fields with dynamic tag HashMap
mattmkim Mar 18, 2026
4e928fe
feat: replace ParquetField enum with constants and dynamic validation
mattmkim Mar 18, 2026
d8d71ed
feat: derive sort order and bloom filters from batch schema
mattmkim Mar 18, 2026
ad115bf
feat: union schema accumulation and schema-agnostic ingest validation
mattmkim Mar 18, 2026
d0a995e
feat: dynamic column lookup in split writer
mattmkim Mar 18, 2026
35c3942
feat: remove ParquetSchema dependency from indexing actors
mattmkim Mar 18, 2026
33c4070
refactor: deduplicate test batch helpers
mattmkim Mar 18, 2026
7a5979f
lint
mattmkim Mar 30, 2026
440631b
Merge branch 'main' into matthew.kim/metrics-wide-schema
mattmkim Mar 31, 2026
5eae799
Merge branch 'main' into matthew.kim/metrics-wide-schema
mattmkim Mar 31, 2026
5b2304c
feat(31): sort schema foundation — proto, parser, display, validation…
g-talbot Mar 31, 2026
4d42fd9
fix: rustdoc link errors — use backticks for private items
g-talbot Apr 1, 2026
759c2ca
Update quickwit/quickwit-parquet-engine/src/table_config.rs
g-talbot Apr 7, 2026
6454f1d
Update quickwit/quickwit-parquet-engine/src/table_config.rs
g-talbot Apr 7, 2026
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5 changes: 4 additions & 1 deletion quickwit/Cargo.lock
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4 changes: 2 additions & 2 deletions quickwit/quickwit-control-plane/src/indexing_scheduler/mod.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,7 @@ pub struct IndexingSchedulerState {
///
/// Scheduling executes the following steps:
/// 1. Builds a [`PhysicalIndexingPlan`] from the list of logical indexing tasks. See
/// [`build_physical_indexing_plan`] for the implementation details.
/// `build_physical_indexing_plan` for the implementation details.
/// 2. Apply the [`PhysicalIndexingPlan`]: for each indexer, the scheduler send the indexing tasks
/// by gRPC. An indexer immediately returns an Ok and apply asynchronously the received plan. Any
/// errors (network) happening in this step are ignored. The scheduler runs a control loop that
Expand Down Expand Up @@ -98,7 +98,7 @@ pub struct IndexingSchedulerState {
/// Concretely, it will send the faulty nodes of the plan they are supposed to follow.
//
/// Finally, in order to give the time for each indexer to run their indexing tasks, the control
/// plane will wait at least [`MIN_DURATION_BETWEEN_SCHEDULING`] before comparing the desired
/// plane will wait at least `MIN_DURATION_BETWEEN_SCHEDULING` before comparing the desired
/// plan with the running plan.
pub struct IndexingScheduler {
cluster_id: String,
Expand Down
1 change: 1 addition & 0 deletions quickwit/quickwit-indexing/Cargo.toml
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Expand Up @@ -123,6 +123,7 @@ sqlx = { workspace = true, features = ["runtime-tokio", "postgres"] }
tempfile = { workspace = true }

quickwit-actors = { workspace = true, features = ["testsuite"] }
quickwit-parquet-engine = { workspace = true, features = ["testsuite"] }
quickwit-cluster = { workspace = true, features = ["testsuite"] }
quickwit-common = { workspace = true, features = ["testsuite"] }
quickwit-config = { workspace = true, features = ["testsuite"] }
Expand Down
2 changes: 0 additions & 2 deletions quickwit/quickwit-indexing/src/actors/indexing_pipeline.rs
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Expand Up @@ -637,10 +637,8 @@ impl IndexingPipeline {
.spawn(parquet_uploader);

// ParquetPackager
let parquet_schema = quickwit_parquet_engine::schema::ParquetSchema::new();
let writer_config = quickwit_parquet_engine::storage::ParquetWriterConfig::default();
let split_writer = quickwit_parquet_engine::storage::ParquetSplitWriter::new(
parquet_schema,
writer_config,
self.params.indexing_directory.path(),
);
Expand Down
221 changes: 7 additions & 214 deletions quickwit/quickwit-indexing/src/actors/parquet_doc_processor.rs
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Expand Up @@ -24,7 +24,6 @@ use quickwit_common::rate_limited_tracing::rate_limited_warn;
use quickwit_common::runtimes::RuntimeType;
use quickwit_metastore::checkpoint::SourceCheckpointDelta;
use quickwit_parquet_engine::ingest::{IngestError, ParquetIngestProcessor};
use quickwit_parquet_engine::schema::ParquetSchema;
use quickwit_proto::types::{IndexId, SourceId};
use serde::Serialize;
use tokio::runtime::Handle;
Expand Down Expand Up @@ -143,8 +142,7 @@ impl ParquetDocProcessor {
source_id: SourceId,
indexer_mailbox: Mailbox<ParquetIndexer>,
) -> Self {
let schema = ParquetSchema::new();
let processor = ParquetIngestProcessor::new(schema);
let processor = ParquetIngestProcessor;
let counters = ParquetDocProcessorCounters::new(index_id.clone(), source_id.clone());

info!(
Expand Down Expand Up @@ -306,7 +304,7 @@ impl Handler<RawDocBatch> for ParquetDocProcessor {
// forever.
if !checkpoint_forwarded && !checkpoint_delta.is_empty() {
let empty_batch =
RecordBatch::new_empty(self.processor.schema().arrow_schema().clone());
RecordBatch::new_empty(std::sync::Arc::new(arrow::datatypes::Schema::empty()));
let processed_batch =
ProcessedParquetBatch::new(empty_batch, checkpoint_delta, force_commit);
ctx.send_message(&self.indexer_mailbox, processed_batch)
Expand Down Expand Up @@ -399,14 +397,8 @@ mod tests {

#[tokio::test]
async fn test_metrics_doc_processor_valid_arrow_ipc() {
use std::sync::Arc as StdArc;
use quickwit_parquet_engine::test_helpers::create_test_batch_with_tags;

use arrow::array::{
ArrayRef, BinaryViewArray, DictionaryArray, Float64Array, Int32Array, StringArray,
StructArray, UInt8Array, UInt64Array,
};
use arrow::datatypes::{DataType, Field, Int32Type};
use arrow::record_batch::RecordBatch;
let universe = Universe::with_accelerated_time();

let (indexer_mailbox, _indexer_inbox) = universe.create_test_mailbox::<ParquetIndexer>();
Expand All @@ -419,103 +411,7 @@ mod tests {
let (metrics_doc_processor_mailbox, metrics_doc_processor_handle) =
universe.spawn_builder().spawn(metrics_doc_processor);

// Create a test batch matching the metrics schema
let schema = ParquetSchema::new();
let num_rows = 3;

// Helper to create dictionary arrays
fn create_dict_array(values: &[&str]) -> ArrayRef {
let keys: Vec<i32> = (0..values.len()).map(|i| i as i32).collect();
let string_array = StringArray::from(values.to_vec());
StdArc::new(
DictionaryArray::<Int32Type>::try_new(
Int32Array::from(keys),
StdArc::new(string_array),
)
.unwrap(),
)
}

fn create_nullable_dict_array(values: &[Option<&str>]) -> ArrayRef {
let keys: Vec<Option<i32>> = values
.iter()
.enumerate()
.map(|(i, v)| v.map(|_| i as i32))
.collect();
let string_values: Vec<&str> = values.iter().filter_map(|v| *v).collect();
let string_array = StringArray::from(string_values);
StdArc::new(
DictionaryArray::<Int32Type>::try_new(
Int32Array::from(keys),
StdArc::new(string_array),
)
.unwrap(),
)
}

let metric_name: ArrayRef = create_dict_array(&vec!["cpu.usage"; num_rows]);
let metric_type: ArrayRef = StdArc::new(UInt8Array::from(vec![0u8; num_rows]));
let metric_unit: ArrayRef = StdArc::new(StringArray::from(vec![Some("bytes"); num_rows]));
let timestamp_secs: ArrayRef = StdArc::new(UInt64Array::from(vec![100u64, 101u64, 102u64]));
let start_timestamp_secs: ArrayRef =
StdArc::new(UInt64Array::from(vec![None::<u64>; num_rows]));
let value: ArrayRef = StdArc::new(Float64Array::from(vec![42.0, 43.0, 44.0]));
let tag_service: ArrayRef = create_nullable_dict_array(&vec![Some("web"); num_rows]);
let tag_env: ArrayRef = create_nullable_dict_array(&vec![Some("prod"); num_rows]);
let tag_datacenter: ArrayRef =
create_nullable_dict_array(&vec![Some("us-east-1"); num_rows]);
let tag_region: ArrayRef = create_nullable_dict_array(&vec![None; num_rows]);
let tag_host: ArrayRef = create_nullable_dict_array(&vec![Some("host-001"); num_rows]);

// Create empty Variant (Struct with metadata and value BinaryView fields)
let metadata_array = StdArc::new(BinaryViewArray::from(vec![b"" as &[u8]; num_rows]));
let value_array = StdArc::new(BinaryViewArray::from(vec![b"" as &[u8]; num_rows]));
let attributes: ArrayRef = StdArc::new(StructArray::from(vec![
(
StdArc::new(Field::new("metadata", DataType::BinaryView, false)),
metadata_array.clone() as ArrayRef,
),
(
StdArc::new(Field::new("value", DataType::BinaryView, false)),
value_array.clone() as ArrayRef,
),
]));

let service_name: ArrayRef = create_dict_array(&vec!["my-service"; num_rows]);

let resource_attributes: ArrayRef = StdArc::new(StructArray::from(vec![
(
StdArc::new(Field::new("metadata", DataType::BinaryView, false)),
metadata_array as ArrayRef,
),
(
StdArc::new(Field::new("value", DataType::BinaryView, false)),
value_array as ArrayRef,
),
]));

let batch = RecordBatch::try_new(
schema.arrow_schema().clone(),
vec![
metric_name,
metric_type,
metric_unit,
timestamp_secs,
start_timestamp_secs,
value,
tag_service,
tag_env,
tag_datacenter,
tag_region,
tag_host,
attributes,
service_name,
resource_attributes,
],
)
.unwrap();

// Serialize to Arrow IPC
let batch = create_test_batch_with_tags(3, &["service"]);
let ipc_bytes = record_batch_to_ipc(&batch).unwrap();

// Create RawDocBatch with the IPC bytes
Expand Down Expand Up @@ -624,13 +520,8 @@ mod tests {
async fn test_metrics_doc_processor_with_indexer() {
use std::sync::Arc as StdArc;

use arrow::array::{
ArrayRef, BinaryViewArray, DictionaryArray, Float64Array, Int32Array, StringArray,
StructArray, UInt8Array, UInt64Array,
};
use arrow::datatypes::{DataType, Field, Int32Type};
use arrow::record_batch::RecordBatch;
use quickwit_parquet_engine::storage::{ParquetSplitWriter, ParquetWriterConfig};
use quickwit_parquet_engine::test_helpers::create_test_batch_with_tags;
use quickwit_proto::metastore::MockMetastoreService;
use quickwit_storage::RamStorage;

Expand All @@ -657,9 +548,8 @@ mod tests {
let (uploader_mailbox, _uploader_handle) = universe.spawn_builder().spawn(uploader);

// Create ParquetPackager
let parquet_schema = ParquetSchema::new();
let writer_config = ParquetWriterConfig::default();
let split_writer = ParquetSplitWriter::new(parquet_schema, writer_config, temp_dir.path());
let split_writer = ParquetSplitWriter::new(writer_config, temp_dir.path());
let packager = ParquetPackager::new(split_writer, uploader_mailbox);
let (packager_mailbox, packager_handle) = universe.spawn_builder().spawn(packager);

Expand All @@ -681,104 +571,7 @@ mod tests {
let (metrics_doc_processor_mailbox, metrics_doc_processor_handle) =
universe.spawn_builder().spawn(metrics_doc_processor);

// Create a test batch
let schema = ParquetSchema::new();
let num_rows = 5;

fn create_dict_array(values: &[&str]) -> ArrayRef {
let keys: Vec<i32> = (0..values.len()).map(|i| i as i32).collect();
let string_array = StringArray::from(values.to_vec());
StdArc::new(
DictionaryArray::<Int32Type>::try_new(
Int32Array::from(keys),
StdArc::new(string_array),
)
.unwrap(),
)
}

fn create_nullable_dict_array(values: &[Option<&str>]) -> ArrayRef {
let keys: Vec<Option<i32>> = values
.iter()
.enumerate()
.map(|(i, v)| v.map(|_| i as i32))
.collect();
let string_values: Vec<&str> = values.iter().filter_map(|v| *v).collect();
let string_array = StringArray::from(string_values);
StdArc::new(
DictionaryArray::<Int32Type>::try_new(
Int32Array::from(keys),
StdArc::new(string_array),
)
.unwrap(),
)
}

let metric_name: ArrayRef = create_dict_array(&vec!["cpu.usage"; num_rows]);
let metric_type: ArrayRef = StdArc::new(UInt8Array::from(vec![0u8; num_rows]));
let metric_unit: ArrayRef = StdArc::new(StringArray::from(vec![Some("bytes"); num_rows]));
let timestamps: Vec<u64> = (0..num_rows).map(|i| 100 + i as u64).collect();
let timestamp_secs: ArrayRef = StdArc::new(UInt64Array::from(timestamps));
let start_timestamp_secs: ArrayRef =
StdArc::new(UInt64Array::from(vec![None::<u64>; num_rows]));
let values: Vec<f64> = (0..num_rows).map(|i| 42.0 + i as f64).collect();
let value: ArrayRef = StdArc::new(Float64Array::from(values));
let tag_service: ArrayRef = create_nullable_dict_array(&vec![Some("web"); num_rows]);
let tag_env: ArrayRef = create_nullable_dict_array(&vec![Some("prod"); num_rows]);
let tag_datacenter: ArrayRef =
create_nullable_dict_array(&vec![Some("us-east-1"); num_rows]);
let tag_region: ArrayRef = create_nullable_dict_array(&vec![None; num_rows]);
let tag_host: ArrayRef = create_nullable_dict_array(&vec![Some("host-001"); num_rows]);

// Create empty Variant (Struct with metadata and value BinaryView fields)
let metadata_array = StdArc::new(BinaryViewArray::from(vec![b"" as &[u8]; num_rows]));
let value_array = StdArc::new(BinaryViewArray::from(vec![b"" as &[u8]; num_rows]));
let attributes: ArrayRef = StdArc::new(StructArray::from(vec![
(
StdArc::new(Field::new("metadata", DataType::BinaryView, false)),
metadata_array.clone() as ArrayRef,
),
(
StdArc::new(Field::new("value", DataType::BinaryView, false)),
value_array.clone() as ArrayRef,
),
]));

let service_name: ArrayRef = create_dict_array(&vec!["my-service"; num_rows]);

let resource_attributes: ArrayRef = StdArc::new(StructArray::from(vec![
(
StdArc::new(Field::new("metadata", DataType::BinaryView, false)),
metadata_array as ArrayRef,
),
(
StdArc::new(Field::new("value", DataType::BinaryView, false)),
value_array as ArrayRef,
),
]));

let batch = RecordBatch::try_new(
schema.arrow_schema().clone(),
vec![
metric_name,
metric_type,
metric_unit,
timestamp_secs,
start_timestamp_secs,
value,
tag_service,
tag_env,
tag_datacenter,
tag_region,
tag_host,
attributes,
service_name,
resource_attributes,
],
)
.unwrap();

// Serialize to Arrow IPC
let batch = create_test_batch_with_tags(5, &["service"]);
let ipc_bytes = record_batch_to_ipc(&batch).unwrap();

// Create RawDocBatch with force_commit to trigger split production
Expand Down
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