fix version number 2082 (#1053)

Author: leto-b

src/.vuepress/sidebar/V2.0.x/zh-Table.ts

@@ -172,7 +172,7 @@ export const zhSidebar = {
         { text: 'C#原生接口', link: 'Programming-CSharp-Native-API_apache' },
         { text: 'JDBC', link: 'Programming-JDBC_apache' },
         { text: 'MQTT协议', link: 'Programming-MQTT_apache' },
-        { text: 'RESTAPI V1 ', link: 'RestServiceV1' },
+        { text: 'RESTAPI V1 ', link: 'RestServiceV1_apache' },
       ],
     },
     {

src/.vuepress/sidebar_timecho/V2.0.x/zh-Table.ts

@@ -195,7 +195,7 @@ export const zhSidebar = {
         { text: 'JDBC', link: 'Programming-JDBC_timecho' },
         { text: 'ODBC', link: 'Programming-ODBC_timecho' },
         { text: 'MQTT协议', link: 'Programming-MQTT_timecho' },
-        { text: 'RESTAPI V1 ', link: 'RestServiceV1' },
+        { text: 'RESTAPI V1 ', link: 'RestServiceV1_timecho' },
       ],
     },
      {

src/UserGuide/Master/Table/AI-capability/AINode_Upgrade_timecho.md

@@ -92,19 +92,19 @@ SELECT * FROM FORECAST(
 * Built-in model inference does not require a registration process. By using the forecast function and specifying model_id, you can use the inference function of the model.
 * Parameter description
 
-| Parameter Name | Parameter Type | Parameter Attributes | Description | Required | Notes |
-|----------------|----------------|----------------------|-------------|----------|-------|
-| model_id | Scalar parameter | String type | Unique identifier of the prediction model | Yes | |
-| targets | Table parameter | SET SEMANTIC | Input data for the target variables to be predicted. IoTDB will automatically sort the data in ascending order of time before passing it to AINode. | Yes | Use SQL to describe the input data with target variables. If the input SQL is invalid, corresponding query errors will be reported. |
+| Parameter Name | Parameter Type | Parameter Attributes | Description                                                                                                                                                                                                                                                                                           | Required | Notes |
+|----------------|----------------|----------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|-------|
+| model_id | Scalar parameter | String type | Unique identifier of the prediction model                                                                                                                                                                                                                                                             | Yes | |
+| targets | Table parameter | SET SEMANTIC | Input data for the target variables to be predicted. IoTDB will automatically sort the data in ascending order of time before passing it to AINode.                                                                                                                                                   | Yes | Use SQL to describe the input data with target variables. If the input SQL is invalid, corresponding query errors will be reported. |
 | history_covs | Scalar parameter | String type (valid table model query SQL), default: none | Specifies historical data of covariates for this prediction task, which are used to assist in predicting target variables. AINode will not output prediction results for historical covariates. Before passing data to the model, AINode will automatically sort the data in ascending order of time. | No | 1. Query results can only contain FIELD columns; 2. Other: Different models may have specific requirements, and errors will be thrown if not met. |
-| future_covs | Scalar parameter | String type (valid table model query SQL), default: none | Specifies future data of some covariates for this prediction task, which are used to assist in predicting target variables. Before passing data to the model, AINode will automatically sort the data in ascending order of time. | No | 1. Can only be specified when history_covs is set; 2. The covariate names involved must be a subset of history_covs; 3. Query results can only contain FIELD columns; 4. Other: Different models may have specific requirements, and errors will be thrown if not met. |
-| auto_adapt         | Scalar parameter | Boolean type, default value: true              | Whether to enable adaptive processing for covariate inference. | No       | When adaptive mode is enabled: 1. If the set of future covariates (`future_covs`) is not a subset of the historical covariates (`history_covs`), any future covariates not present in the historical set will be automatically discarded. 2. If the length of any historical covariate does not match the length of the input target variable: a. If shorter, pad zeros at the beginning; b. If longer, discard the earliest data points. 3. If the length of any future covariate does not match the prediction length (`output_length`): a. If shorter, pad zeros at the end; b. If longer, discard the most recent data points. |
-| output_start_time | Scalar parameter | Timestamp type. Default value: last timestamp of target variable + output_interval | Starting timestamp of output prediction points [i.e., forecast start time] | No | Must be greater than the maximum timestamp of target variable timestamps |
-| output_length | Scalar parameter | INT32 type. Default value: 96 | Output window size | No | Must be greater than 0 |
-| output_interval | Scalar parameter | Time interval type. Default value: (last timestamp - first timestamp of input data) / n - 1 | Time interval between output prediction points. Supported units: ns, us, ms, s, m, h, d, w | No | Must be greater than 0 |
-| timecol | Scalar parameter | String type. Default value: time | Name of time column | No | Must be a TIMESTAMP column existing in targets |
-| preserve_input | Scalar parameter | Boolean type. Default value: false | Whether to retain all original rows of target variable input in the output result set | No | |
-| model_options | Scalar parameter | String type. Default value: empty string | Key-value pairs related to the model, such as whether to normalize the input. Different key-value pairs are separated by ';'. | No | |
+| future_covs | Scalar parameter | String type (valid table model query SQL), default: none | Specifies future data of some covariates for this prediction task, which are used to assist in predicting target variables. Before passing data to the model, AINode will automatically sort the data in ascending order of time.                                                                     | No | 1. Can only be specified when history_covs is set; 2. The covariate names involved must be a subset of history_covs; 3. Query results can only contain FIELD columns; 4. Other: Different models may have specific requirements, and errors will be thrown if not met. |
+| auto_adapt         | Scalar parameter | Boolean type, default value: true              | Whether to enable adaptive processing for covariate inference.(Support from V2.0.8.2)                                                                                                                                                                                                                 | No       | When adaptive mode is enabled: 1. If the set of future covariates (`future_covs`) is not a subset of the historical covariates (`history_covs`), any future covariates not present in the historical set will be automatically discarded. 2. If the length of any historical covariate does not match the length of the input target variable: a. If shorter, pad zeros at the beginning; b. If longer, discard the earliest data points. 3. If the length of any future covariate does not match the prediction length (`output_length`): a. If shorter, pad zeros at the end; b. If longer, discard the most recent data points. |
+| output_start_time | Scalar parameter | Timestamp type. Default value: last timestamp of target variable + output_interval | Starting timestamp of output prediction points [i.e., forecast start time]                                                                                                                                                                                                                            | No | Must be greater than the maximum timestamp of target variable timestamps |
+| output_length | Scalar parameter | INT32 type. Default value: 96 | Output window size                                                                                                                                                                                                                                                                                    | No | Must be greater than 0 |
+| output_interval | Scalar parameter | Time interval type. Default value: (last timestamp - first timestamp of input data) / n - 1 | Time interval between output prediction points. Supported units: ns, us, ms, s, m, h, d, w                                                                                                                                                                                                            | No | Must be greater than 0 |
+| timecol | Scalar parameter | String type. Default value: time | Name of time column                                                                                                                                                                                                                                                                                   | No | Must be a TIMESTAMP column existing in targets |
+| preserve_input | Scalar parameter | Boolean type. Default value: false | Whether to retain all original rows of target variable input in the output result set                                                                                                                                                                                                                 | No | |
+| model_options | Scalar parameter | String type. Default value: empty string | Key-value pairs related to the model, such as whether to normalize the input. Different key-value pairs are separated by ';'.                                                                                                                                                                         | No | |
 
 Notes:
 * **Default behavior**: Predict all columns of targets. Currently, only supports INT32, INT64, FLOAT, DOUBLE types.

src/UserGuide/Master/Table/API/Programming-Python-Native-API_timecho.md

@@ -46,7 +46,7 @@ Note: Do not use a newer client to connect to an older server, as this may cause
 | execute_query_statement     | Executes a query SQL statement and retrieves results. | sql: `str`                           | `SessionDataSet` |
 | close                       | Closes the session and releases resources.            | None                                 | None             |
 
-**Since V2.0.8**, `SessionDataSet` provides methods for batch DataFrame retrieval to efficiently handle large-volume queries:
+**Since V2.0.8.2**, `SessionDataSet` provides methods for batch DataFrame retrieval to efficiently handle large-volume queries:
 
 ```python
 # Batch DataFrame retrieval

src/UserGuide/Master/Table/Basic-Concept/Table-Management_timecho.md

@@ -67,7 +67,7 @@ comment
 **Note:**
 
 1. When creating a table, you do not need to specify a time column. IoTDB automatically adds a column named "time" and places it as the first column. All other columns can be added by enabling the `enable_auto_create_schema` option in the database configuration, or through the session interface for automatic creation or by using table modification statements.
-2. Since version V2.0.8, tables support custom naming of the time column during creation. The order of the custom time column in the table is determined by the order in the creation SQL. The related constraints are as follows:
+2. Since version V2.0.8.2, tables support custom naming of the time column during creation. The order of the custom time column in the table is determined by the order in the creation SQL. The related constraints are as follows:
 
    - When the column category is set to TIME, the data type must be TIMESTAMP.
    - Each table allows at most one time column (columnCategory = TIME).

src/UserGuide/Master/Table/SQL-Manual/SQL-Metadata-Operations_timecho.md

@@ -212,7 +212,7 @@ CREATE TABLE tableC (
   "Temperature" int32 FIELD COMMENT 'temperature'
  ) with (TTL=DEFAULT);
 
- -- Custom time column: named time_test, located in the second column of the table. (Support from V2.0.8)
+ -- Custom time column: named time_test, located in the second column of the table. (Support from V2.0.8.2)
  CREATE TABLE table1 (
      region STRING TAG, 
      time_user_defined TIMESTAMP TIME, 

src/UserGuide/Master/Table/User-Manual/Audit-Log_timecho.md

@@ -38,6 +38,23 @@ Audit logs serve as the record credentials of a database, enabling tracking of v
 
 Edit the `iotdb-system.properties` file to enable audit logging using the following parameters:
 
+
+* V2.0.8.1
+
+| Parameter Name                              | Description                                                                                                 | Data Type | Default Value                 | Activation Method |
+|-------------------------------------------|------------------------------------------------------------------------------------------------------------|-----------|-------------------------------|-------------------|
+| `enable_audit_log`                        | Whether to enable audit logging. true: enabled. false: disabled.                                          | Boolean   | false                         | Hot Reload        |
+| `auditable_operation_type`                | Operation type selection. DML: all DML operations are logged; DDL: all DDL operations are logged; QUERY: all query operations are logged; CONTROL: all control statements are logged. | String    | DML,DDL,QUERY,CONTROL         | Hot Reload        |
+| `auditable_operation_level`               | Permission level selection. global: log all audit events; object: only log events related to data instances. Containment relationship: object < global. For example: when set to global, all audit logs are recorded normally; when set to object, only operations on specific data instances are recorded. | String    | global                        | Hot Reload        |
+| `auditable_operation_result`              | Audit result selection. success: log only successful events; fail: log only failed events                  | String    | success,fail                  | Hot Reload        |
+| `audit_log_ttl_in_days`                   | Audit log TTL (Time To Live). Logs older than this threshold will expire.                                   | Double    | -1.0 (never deleted)          | Hot Reload        |
+| `audit_log_space_tl_in_GB`                | Audit log SpaceTL. Logs will start rotating when total space reaches this threshold.                       | Double    | 1.0                             | Hot Reload        |
+| `audit_log_batch_interval_in_ms`          | Batch write interval for audit logs                                                                          | Long      | 1000                            | Hot Reload        |
+| `audit_log_batch_max_queue_bytes`         | Maximum byte size of the queue for batch processing audit logs. Subsequent write operations will be blocked when this threshold is exceeded. | Long      | 268435456                       | Hot Reload        |
+
+* V2.0.8.2
+
+
 | Parameter Name                              | Description                                                                                                 | Data Type | Default Value                 | Activation Method |
 |-------------------------------------------|------------------------------------------------------------------------------------------------------------|-----------|-------------------------------|-------------------|
 | `enable_audit_log`                        | Whether to enable audit logging. true: enabled. false: disabled.                                          | Boolean   | false                         | Hot Reload        |

src/UserGuide/Master/Table/User-Manual/Tree-to-Table_timecho.md

@@ -80,7 +80,7 @@ The name of the view, which follows the same rules as a table name (for specific
         * If a device in the tree model does not contain certain declared FIELD columns, or if their data types are inconsistent with the declared FIELD columns, the value for that FIELD column will always be `NULL`when querying that device.
     * If no FIELD columns are specified, the system automatically scans for all measurements under the `prefixPath`subtree (including all ordinary sequence measurements and measurements defined in any templates whose mounted paths overlap with the `prefixPath`) during creation. The column names will use the measurement names from the tree model.
         * The tree model cannot have measurements with the same name (case-insensitive) but different data types.
-* `TIME`: When creating a view, you do not need to specify a time column. IoTDB automatically adds a column named "time" and places it as the first column. Since version V2.0.8-beta, views support **custom naming of the time column** during creation. The order of the custom time column in the view is determined by the order in the creation SQL. The related constraints are as follows:
+* `TIME`: When creating a view, you do not need to specify a time column. IoTDB automatically adds a column named "time" and places it as the first column. Since version V2.0.8.2, views support **custom naming of the time column** during creation. The order of the custom time column in the view is determined by the order in the creation SQL. The related constraints are as follows:
     * When the column category is set to `TIME`, the data type must be `TIMESTAMP`.
     * Each view allows at most one time column (columnCategory = TIME).
     * If no time column is explicitly defined, no other column can use `time` as its name to avoid conflicts with the system's default time column naming.
@@ -143,7 +143,7 @@ with (ttl=604800000)
 AS root.db.**
 ```
 
-When customizing the time column (supported since V2.0.8), the SQL changes are as follows:
+When customizing the time column (supported since V2.0.8.2), the SQL changes are as follows:
 
 ```SQL
 CREATE OR REPLACE VIEW viewdb."wind_turbine"
@@ -161,7 +161,7 @@ AS root.db.**
 ### 2.2 Modifying a Table View
 #### 2.2.1 Syntax Definition
 
-The ALTER VIEW function supports modifying the view name, adding columns, renaming columns, modifying FIELD column data type (supported since V2.0.8), deleting columns, setting the view's TTL property, and adding comments via COMMENT.
+The ALTER VIEW function supports modifying the view name, adding columns, renaming columns, modifying FIELD column data type (supported since V2.0.8.2), deleting columns, setting the view's TTL property, and adding comments via COMMENT.
 
 ```SQL
 -- Rename view

src/UserGuide/Master/Tree/API/Programming-Python-Native-API_timecho.md

@@ -558,7 +558,7 @@ df = ...
 ```
 
 
-**Since V2.0.8**, `SessionDataSet` provides methods for batch DataFrame retrieval to efficiently handle large-volume queries:
+**Since V2.0.8.2**, `SessionDataSet` provides methods for batch DataFrame retrieval to efficiently handle large-volume queries:
 
 ```python
 # Batch DataFrame retrieval