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[GitHub] [druid] sthetland commented on a change in pull request #10935: First refactor of compaction
sthetland commented on a change in pull request #10935:
URL: https://github.com/apache/druid/pull/10935#discussion_r589825536
##########
File path: docs/ingestion/compaction.md
##########
@@ -0,0 +1,207 @@
+---
+id: compaction
+title: "Compaction"
+description: "Defines compaction and automatic compaction (auto-compaction or autocompaction) as a strategy for segment optimization. Use cases for compaction. Describes compaction task configuration."
+---
+
+<!--
+ ~ Licensed to the Apache Software Foundation (ASF) under one
+ ~ or more contributor license agreements. See the NOTICE file
+ ~ distributed with this work for additional information
+ ~ regarding copyright ownership. The ASF licenses this file
+ ~ to you under the Apache License, Version 2.0 (the
+ ~ "License"); you may not use this file except in compliance
+ ~ with the License. You may obtain a copy of the License at
+ ~
+ ~ http://www.apache.org/licenses/LICENSE-2.0
+ ~
+ ~ Unless required by applicable law or agreed to in writing,
+ ~ software distributed under the License is distributed on an
+ ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ ~ KIND, either express or implied. See the License for the
+ ~ specific language governing permissions and limitations
+ ~ under the License.
+ -->
+
+Compaction in Apache Druid is a strategy to optimize segment size. Compaction tasks read an existing set of segments for a given time range and combine the data into a new "compacted" set of segments. The compacted segments are generally larger, but there are fewer of them. Compaction can sometimes increase performance because it reduces the number of segments and, consequently, the per-segment processing and the memory overhead required for ingestion and for querying paths.
+
+As a strategy, compaction is effective when you have data arriving out of chronological order resulting in lots of small segments. For example if you are appending data using `appendToExisting` for [native batch](./native_batch.md) ingestion. Conversely, if you are rewriting your data with each ingestion task, you don't need to use compaction. See [Segment optimization](../operations/segment-optimization.md) for guidance to determine if compaction will help in your case.
+
+## Types of segment compaction
+You can configure the Druid Coordinator to perform automatic compaction, also called auto-compaction, for a datasource. Using a segment search policy, the coordinator periodically identifies segments for compaction starting with the newest to oldest. When segments can benefit from compaction, the coordinator automatically submits a compaction task.
+
+Automatic compaction works in most use cases and should be your first option. To learn more about automatic compaction, see [Compacting Segments](../design/coordinator.md#compacting-segments).
+
+In cases where you require more control over compaction, you can manually submit compaction tasks. For example:
+- Automatic compaction is too slow.
+- You want to force compaction for a specific time range.
+- Compacting recent data before older data suboptimal is suboptimal in your environment.
+
+See [Setting up a manual compaction task](#setting-up-manual-compaction) more about manual compaction tasks.
+
+
+## Data handling with compaction
+During compaction, Druid overwrites the original set of segments with the compacted set without modifying the data. During compaction Druid locks the segments for the time interval being compacted to ensure data consistency.
+
+If an ingestion task needs to write data to a segment for a time interval locked for compaction, the ingestion task supersedes the compaction task and the compaction task fails without finishing. For manual compaction tasks you can adjust the input spec interval to avoid conflicts between ingestion and compaction. For automatic compaction, you can set the `skipOffsetFromLatest` key to adjustment the auto compaction starting point from the current time to reduce the chance of conflicts between ingestion and compaction. See [Compaction dynamic configuration](../configuration/index.md#compaction-dynamic-configuration) for more information.
+
+### Segment granularity handling
+
+Unless you modify the segment granularity in the [granularity spec](#compaction-granularity-spec), Druid attempts to retain the granularity for the compacted segments. When segments have different segment granularities with no overlap in interval Druid creates a separate compaction task for each to retain the segment granularity in the compacted segment. If segments have different segment granularities before compaction but there is some overlap in interval, Druid attempts find start and end of the overlapping interval and uses the closest segment granularity level for the compacted segment.
+
+### Query granularity handling
+
+Unless you modify the query granularity in the [granularity spec](#compaction-granularity-spec), Druid retains the query granularity for the compacted segments. If segments have different query granularities before compaction, Druid chooses the finest level of granularity for the resulting compacted segment. For example if a compaction task combines two segments, one with day query granularity and one with minute query granularity, the resulting segment uses minute query granularity.
+
+> In Apache Druid 0.21.0 and prior, Druid sets the granularity for compacted segments to the default granularity of `NONE` regardless of the query granularity of the original segments.
+
+If you configure query granularity in compaction to go from a finer granularity like month to a coarser query granularity like year, then Druid overshadows the original segment with finer granularity. Because the new segments have a coarser granularity, running a kill task to remove the overshadowed segments for those intervals will cause you to permanently lose the finer granularity data.
+
+
+### Dimension handling
+Apache Druid supports schema changes. Therefore, dimensions can be different across segments even if they are a part of the same data source. See [Different schemas among segments](../design/segments.md#different-schemas-among-segments). If the input segments have different dimensions, the resulting compacted segment includes all dimensions of the input segments.
+
+Even when the input segments have the same set of dimensions, the dimension order or the data type of dimensions can be different. For example, the data type of some dimensions can be changed from `string` to primitive types, or the order of dimensions can be changed for better locality. In this case, the dimensions of recent segments precede that of old segments in terms of data types and the ordering because more recent segments are more likely to have the preferred order and data types.
+
+If you want to use your own ordering and types, you can specify a custom `dimensionsSpec` in the compaction task spec.
+
+### Rollup
+Druid only rolls up the output segment when `rollup` is set for all input segments. See [Roll-up](../ingestion/index.md#rollup) for more details.
+You can check that your segments are rolled up or not by using [Segment Metadata Queries](../querying/segmentmetadataquery.md#analysistypes).
+
+## Setting up manual compaction
+
+To perform a manual compaction, you submit a compaction task. Compaction tasks merge all segments for the defined interval according to the following syntax:
+
+```json
+{
+ "type": "compact",
+ "id": <task_id>,
+ "dataSource": <task_datasource>,
+ "ioConfig": <IO config>,
+ "dimensionsSpec" <custom dimensionsSpec>,
+ "metricsSpec" <custom metricsSpec>,
+ "tuningConfig" <parallel indexing task tuningConfig>,
+ "context": <task context>
+}
+```
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`type`|Task type. Should be `compact`|Yes|
+|`id`|Task id|No|
+|`dataSource`|Data source name to compact|Yes|
+|`ioConfig`|I/O configuration for compaction task. See [Compaction I/O configuration](#compaction-io-configuration) for details.|Yes|
+|`dimensionsSpec`|Custom dimensions spec. The compaction task uses the specified dimensions spec if it exists instead of generating one.|No|
+|`metricsSpec`|Custom metrics spec. The compaction task uses the specified metrics spec rather than generating one.|No|
+|`segmentGranularity`|When set, the compaction task changes the segment granularity for the given interval. Deprecated. Use `granularitySpec`. |No.|
+|`tuningConfig`|[Parallel indexing task tuningConfig](./native-batch.md#tuningconfig)|No|
+|`context`|[Task context](./tasks.md#context)|No|
+|`granularitySpec`|Custom `granularitySpec` to describe the `segmentGranularity` and `queryGranularity` for the compacted segments. See [Compaction granularitySpec](#compaction-granularity-spec).|No|
+
+To control the number of result segments per time chunk, you can set [maxRowsPerSegment](../configuration/index.md#compaction-dynamic-configuration) or [numShards](../ingestion/native-batch.md#tuningconfig).
+
+> You can run multiple compaction tasks at the same time. For example, you can run 12 compaction tasks per month instead of running a single task for the entire year.
+
+A compaction task internally generates an `index` task spec for performing compaction work with some fixed parameters. For example, its `inputSource` is always the [DruidInputSource](native-batch.md#druid-input-source), and `dimensionsSpec` and `metricsSpec`include all dimensions and metrics of the input segments by default.
+
+Compaction tasks exit without doing anything and issue a failure status code:
+- if the interval you specify has no data segments loaded<br>
+OR
+- if the interval you specify is empty.
+
+The output segment can have different metadata from the input segments unless all input segments have the same metadata.
+
+
+### Example compaction task
+The following JSON illustrates a compaction task to compact _all segments_ within the interval `2017-01-01/2018-01-01` and create new segments:
+
+```json
+{
+ "type" : "compact",
+ "dataSource" : "wikipedia",
+ "ioConfig" : {
+ "type": "compact",
+ "inputSpec": {
+ "type": "interval",
+ "interval": "2017-01-01/2018-01-01",
+ }
+ }
+}
+```
+
+This task doesn't specify a `granularitySpec` so Druid retains the original segment granularity unchanged when compaction is complete.
+
+### Compaction I/O configuration
+
+The compaction `ioConfig` requires specifying `inputSpec` as follows:
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`type`|Task type. Should be `compact`|Yes|
+|`inputSpec`|Input specification|Yes|
+
+There are two supported `inputSpec`s for now.
+
+The interval `inputSpec` is:
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`type`|Task type. Should be `interval`|Yes|
+|`interval`|Interval to compact|Yes|
+
+The segments `inputSpec` is:
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`type`|Task type. Should be `segments`|Yes|
+|`segments`|A list of segment IDs|Yes|
+
+### Compaction granularity spec
+
+You can optionally use the `granularitySpec` object to configure the segment granularity and the query granularity of the compacted segments. They syntax is as follows:
Review comment:
```suggestion
You can optionally use the `granularitySpec` object to configure the segment granularity and the query granularity of the compacted segments. Their syntax is as follows:
```
##########
File path: docs/ingestion/compaction.md
##########
@@ -0,0 +1,207 @@
+---
+id: compaction
+title: "Compaction"
+description: "Defines compaction and automatic compaction (auto-compaction or autocompaction) as a strategy for segment optimization. Use cases for compaction. Describes compaction task configuration."
+---
+
+<!--
+ ~ Licensed to the Apache Software Foundation (ASF) under one
+ ~ or more contributor license agreements. See the NOTICE file
+ ~ distributed with this work for additional information
+ ~ regarding copyright ownership. The ASF licenses this file
+ ~ to you under the Apache License, Version 2.0 (the
+ ~ "License"); you may not use this file except in compliance
+ ~ with the License. You may obtain a copy of the License at
+ ~
+ ~ http://www.apache.org/licenses/LICENSE-2.0
+ ~
+ ~ Unless required by applicable law or agreed to in writing,
+ ~ software distributed under the License is distributed on an
+ ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ ~ KIND, either express or implied. See the License for the
+ ~ specific language governing permissions and limitations
+ ~ under the License.
+ -->
+
+Compaction in Apache Druid is a strategy to optimize segment size. Compaction tasks read an existing set of segments for a given time range and combine the data into a new "compacted" set of segments. The compacted segments are generally larger, but there are fewer of them. Compaction can sometimes increase performance because it reduces the number of segments and, consequently, the per-segment processing and the memory overhead required for ingestion and for querying paths.
+
+As a strategy, compaction is effective when you have data arriving out of chronological order resulting in lots of small segments. For example if you are appending data using `appendToExisting` for [native batch](./native_batch.md) ingestion. Conversely, if you are rewriting your data with each ingestion task, you don't need to use compaction. See [Segment optimization](../operations/segment-optimization.md) for guidance to determine if compaction will help in your case.
+
+## Types of segment compaction
+You can configure the Druid Coordinator to perform automatic compaction, also called auto-compaction, for a datasource. Using a segment search policy, the coordinator periodically identifies segments for compaction starting with the newest to oldest. When segments can benefit from compaction, the coordinator automatically submits a compaction task.
+
+Automatic compaction works in most use cases and should be your first option. To learn more about automatic compaction, see [Compacting Segments](../design/coordinator.md#compacting-segments).
+
+In cases where you require more control over compaction, you can manually submit compaction tasks. For example:
+- Automatic compaction is too slow.
+- You want to force compaction for a specific time range.
+- Compacting recent data before older data suboptimal is suboptimal in your environment.
+
+See [Setting up a manual compaction task](#setting-up-manual-compaction) more about manual compaction tasks.
+
+
+## Data handling with compaction
+During compaction, Druid overwrites the original set of segments with the compacted set without modifying the data. During compaction Druid locks the segments for the time interval being compacted to ensure data consistency.
+
+If an ingestion task needs to write data to a segment for a time interval locked for compaction, the ingestion task supersedes the compaction task and the compaction task fails without finishing. For manual compaction tasks you can adjust the input spec interval to avoid conflicts between ingestion and compaction. For automatic compaction, you can set the `skipOffsetFromLatest` key to adjustment the auto compaction starting point from the current time to reduce the chance of conflicts between ingestion and compaction. See [Compaction dynamic configuration](../configuration/index.md#compaction-dynamic-configuration) for more information.
+
+### Segment granularity handling
+
+Unless you modify the segment granularity in the [granularity spec](#compaction-granularity-spec), Druid attempts to retain the granularity for the compacted segments. When segments have different segment granularities with no overlap in interval Druid creates a separate compaction task for each to retain the segment granularity in the compacted segment. If segments have different segment granularities before compaction but there is some overlap in interval, Druid attempts find start and end of the overlapping interval and uses the closest segment granularity level for the compacted segment.
+
+### Query granularity handling
+
+Unless you modify the query granularity in the [granularity spec](#compaction-granularity-spec), Druid retains the query granularity for the compacted segments. If segments have different query granularities before compaction, Druid chooses the finest level of granularity for the resulting compacted segment. For example if a compaction task combines two segments, one with day query granularity and one with minute query granularity, the resulting segment uses minute query granularity.
+
+> In Apache Druid 0.21.0 and prior, Druid sets the granularity for compacted segments to the default granularity of `NONE` regardless of the query granularity of the original segments.
+
+If you configure query granularity in compaction to go from a finer granularity like month to a coarser query granularity like year, then Druid overshadows the original segment with finer granularity. Because the new segments have a coarser granularity, running a kill task to remove the overshadowed segments for those intervals will cause you to permanently lose the finer granularity data.
+
+
+### Dimension handling
+Apache Druid supports schema changes. Therefore, dimensions can be different across segments even if they are a part of the same data source. See [Different schemas among segments](../design/segments.md#different-schemas-among-segments). If the input segments have different dimensions, the resulting compacted segment includes all dimensions of the input segments.
+
+Even when the input segments have the same set of dimensions, the dimension order or the data type of dimensions can be different. For example, the data type of some dimensions can be changed from `string` to primitive types, or the order of dimensions can be changed for better locality. In this case, the dimensions of recent segments precede that of old segments in terms of data types and the ordering because more recent segments are more likely to have the preferred order and data types.
+
+If you want to use your own ordering and types, you can specify a custom `dimensionsSpec` in the compaction task spec.
+
+### Rollup
+Druid only rolls up the output segment when `rollup` is set for all input segments. See [Roll-up](../ingestion/index.md#rollup) for more details.
+You can check that your segments are rolled up or not by using [Segment Metadata Queries](../querying/segmentmetadataquery.md#analysistypes).
+
+## Setting up manual compaction
+
+To perform a manual compaction, you submit a compaction task. Compaction tasks merge all segments for the defined interval according to the following syntax:
+
+```json
+{
+ "type": "compact",
+ "id": <task_id>,
+ "dataSource": <task_datasource>,
+ "ioConfig": <IO config>,
+ "dimensionsSpec" <custom dimensionsSpec>,
+ "metricsSpec" <custom metricsSpec>,
+ "tuningConfig" <parallel indexing task tuningConfig>,
+ "context": <task context>
+}
+```
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`type`|Task type. Should be `compact`|Yes|
+|`id`|Task id|No|
+|`dataSource`|Data source name to compact|Yes|
+|`ioConfig`|I/O configuration for compaction task. See [Compaction I/O configuration](#compaction-io-configuration) for details.|Yes|
+|`dimensionsSpec`|Custom dimensions spec. The compaction task uses the specified dimensions spec if it exists instead of generating one.|No|
+|`metricsSpec`|Custom metrics spec. The compaction task uses the specified metrics spec rather than generating one.|No|
+|`segmentGranularity`|When set, the compaction task changes the segment granularity for the given interval. Deprecated. Use `granularitySpec`. |No.|
+|`tuningConfig`|[Parallel indexing task tuningConfig](./native-batch.md#tuningconfig)|No|
+|`context`|[Task context](./tasks.md#context)|No|
+|`granularitySpec`|Custom `granularitySpec` to describe the `segmentGranularity` and `queryGranularity` for the compacted segments. See [Compaction granularitySpec](#compaction-granularity-spec).|No|
+
+To control the number of result segments per time chunk, you can set [maxRowsPerSegment](../configuration/index.md#compaction-dynamic-configuration) or [numShards](../ingestion/native-batch.md#tuningconfig).
+
+> You can run multiple compaction tasks at the same time. For example, you can run 12 compaction tasks per month instead of running a single task for the entire year.
+
+A compaction task internally generates an `index` task spec for performing compaction work with some fixed parameters. For example, its `inputSource` is always the [DruidInputSource](native-batch.md#druid-input-source), and `dimensionsSpec` and `metricsSpec`include all dimensions and metrics of the input segments by default.
Review comment:
```suggestion
A compaction task internally generates an `index` task spec for performing compaction work with some fixed parameters. For example, its `inputSource` is always the [DruidInputSource](../native-batch.md#druid-input-source), and `dimensionsSpec` and `metricsSpec` include all dimensions and metrics of the input segments by default.
```
##########
File path: docs/ingestion/compaction.md
##########
@@ -0,0 +1,207 @@
+---
+id: compaction
+title: "Compaction"
+description: "Defines compaction and automatic compaction (auto-compaction or autocompaction) as a strategy for segment optimization. Use cases for compaction. Describes compaction task configuration."
+---
+
+<!--
+ ~ Licensed to the Apache Software Foundation (ASF) under one
+ ~ or more contributor license agreements. See the NOTICE file
+ ~ distributed with this work for additional information
+ ~ regarding copyright ownership. The ASF licenses this file
+ ~ to you under the Apache License, Version 2.0 (the
+ ~ "License"); you may not use this file except in compliance
+ ~ with the License. You may obtain a copy of the License at
+ ~
+ ~ http://www.apache.org/licenses/LICENSE-2.0
+ ~
+ ~ Unless required by applicable law or agreed to in writing,
+ ~ software distributed under the License is distributed on an
+ ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ ~ KIND, either express or implied. See the License for the
+ ~ specific language governing permissions and limitations
+ ~ under the License.
+ -->
+
+Compaction in Apache Druid is a strategy to optimize segment size. Compaction tasks read an existing set of segments for a given time range and combine the data into a new "compacted" set of segments. The compacted segments are generally larger, but there are fewer of them. Compaction can sometimes increase performance because it reduces the number of segments and, consequently, the per-segment processing and the memory overhead required for ingestion and for querying paths.
+
+As a strategy, compaction is effective when you have data arriving out of chronological order resulting in lots of small segments. For example if you are appending data using `appendToExisting` for [native batch](./native_batch.md) ingestion. Conversely, if you are rewriting your data with each ingestion task, you don't need to use compaction. See [Segment optimization](../operations/segment-optimization.md) for guidance to determine if compaction will help in your case.
+
+## Types of segment compaction
+You can configure the Druid Coordinator to perform automatic compaction, also called auto-compaction, for a datasource. Using a segment search policy, the coordinator periodically identifies segments for compaction starting with the newest to oldest. When segments can benefit from compaction, the coordinator automatically submits a compaction task.
+
+Automatic compaction works in most use cases and should be your first option. To learn more about automatic compaction, see [Compacting Segments](../design/coordinator.md#compacting-segments).
+
+In cases where you require more control over compaction, you can manually submit compaction tasks. For example:
+- Automatic compaction is too slow.
+- You want to force compaction for a specific time range.
+- Compacting recent data before older data suboptimal is suboptimal in your environment.
+
+See [Setting up a manual compaction task](#setting-up-manual-compaction) more about manual compaction tasks.
+
+
+## Data handling with compaction
+During compaction, Druid overwrites the original set of segments with the compacted set without modifying the data. During compaction Druid locks the segments for the time interval being compacted to ensure data consistency.
+
+If an ingestion task needs to write data to a segment for a time interval locked for compaction, the ingestion task supersedes the compaction task and the compaction task fails without finishing. For manual compaction tasks you can adjust the input spec interval to avoid conflicts between ingestion and compaction. For automatic compaction, you can set the `skipOffsetFromLatest` key to adjustment the auto compaction starting point from the current time to reduce the chance of conflicts between ingestion and compaction. See [Compaction dynamic configuration](../configuration/index.md#compaction-dynamic-configuration) for more information.
+
+### Segment granularity handling
+
+Unless you modify the segment granularity in the [granularity spec](#compaction-granularity-spec), Druid attempts to retain the granularity for the compacted segments. When segments have different segment granularities with no overlap in interval Druid creates a separate compaction task for each to retain the segment granularity in the compacted segment. If segments have different segment granularities before compaction but there is some overlap in interval, Druid attempts find start and end of the overlapping interval and uses the closest segment granularity level for the compacted segment.
+
+### Query granularity handling
+
+Unless you modify the query granularity in the [granularity spec](#compaction-granularity-spec), Druid retains the query granularity for the compacted segments. If segments have different query granularities before compaction, Druid chooses the finest level of granularity for the resulting compacted segment. For example if a compaction task combines two segments, one with day query granularity and one with minute query granularity, the resulting segment uses minute query granularity.
+
+> In Apache Druid 0.21.0 and prior, Druid sets the granularity for compacted segments to the default granularity of `NONE` regardless of the query granularity of the original segments.
+
+If you configure query granularity in compaction to go from a finer granularity like month to a coarser query granularity like year, then Druid overshadows the original segment with finer granularity. Because the new segments have a coarser granularity, running a kill task to remove the overshadowed segments for those intervals will cause you to permanently lose the finer granularity data.
+
+
+### Dimension handling
+Apache Druid supports schema changes. Therefore, dimensions can be different across segments even if they are a part of the same data source. See [Different schemas among segments](../design/segments.md#different-schemas-among-segments). If the input segments have different dimensions, the resulting compacted segment includes all dimensions of the input segments.
+
+Even when the input segments have the same set of dimensions, the dimension order or the data type of dimensions can be different. For example, the data type of some dimensions can be changed from `string` to primitive types, or the order of dimensions can be changed for better locality. In this case, the dimensions of recent segments precede that of old segments in terms of data types and the ordering because more recent segments are more likely to have the preferred order and data types.
+
+If you want to use your own ordering and types, you can specify a custom `dimensionsSpec` in the compaction task spec.
+
+### Rollup
+Druid only rolls up the output segment when `rollup` is set for all input segments. See [Roll-up](../ingestion/index.md#rollup) for more details.
+You can check that your segments are rolled up or not by using [Segment Metadata Queries](../querying/segmentmetadataquery.md#analysistypes).
+
+## Setting up manual compaction
+
+To perform a manual compaction, you submit a compaction task. Compaction tasks merge all segments for the defined interval according to the following syntax:
+
+```json
+{
+ "type": "compact",
+ "id": <task_id>,
+ "dataSource": <task_datasource>,
+ "ioConfig": <IO config>,
+ "dimensionsSpec" <custom dimensionsSpec>,
+ "metricsSpec" <custom metricsSpec>,
+ "tuningConfig" <parallel indexing task tuningConfig>,
+ "context": <task context>
+}
+```
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`type`|Task type. Should be `compact`|Yes|
+|`id`|Task id|No|
+|`dataSource`|Data source name to compact|Yes|
+|`ioConfig`|I/O configuration for compaction task. See [Compaction I/O configuration](#compaction-io-configuration) for details.|Yes|
+|`dimensionsSpec`|Custom dimensions spec. The compaction task uses the specified dimensions spec if it exists instead of generating one.|No|
+|`metricsSpec`|Custom metrics spec. The compaction task uses the specified metrics spec rather than generating one.|No|
+|`segmentGranularity`|When set, the compaction task changes the segment granularity for the given interval. Deprecated. Use `granularitySpec`. |No.|
+|`tuningConfig`|[Parallel indexing task tuningConfig](./native-batch.md#tuningconfig)|No|
+|`context`|[Task context](./tasks.md#context)|No|
+|`granularitySpec`|Custom `granularitySpec` to describe the `segmentGranularity` and `queryGranularity` for the compacted segments. See [Compaction granularitySpec](#compaction-granularity-spec).|No|
+
+To control the number of result segments per time chunk, you can set [maxRowsPerSegment](../configuration/index.md#compaction-dynamic-configuration) or [numShards](../ingestion/native-batch.md#tuningconfig).
+
+> You can run multiple compaction tasks at the same time. For example, you can run 12 compaction tasks per month instead of running a single task for the entire year.
Review comment:
This note isn't clear to me. Perhaps "You can schedule multiple compaction tasks.. "? And then perhaps it means to say something like "... 12 compaction tasks -- one per month -- instead of running..."
##########
File path: docs/ingestion/compaction.md
##########
@@ -0,0 +1,207 @@
+---
+id: compaction
+title: "Compaction"
+description: "Defines compaction and automatic compaction (auto-compaction or autocompaction) as a strategy for segment optimization. Use cases for compaction. Describes compaction task configuration."
+---
+
+<!--
+ ~ Licensed to the Apache Software Foundation (ASF) under one
+ ~ or more contributor license agreements. See the NOTICE file
+ ~ distributed with this work for additional information
+ ~ regarding copyright ownership. The ASF licenses this file
+ ~ to you under the Apache License, Version 2.0 (the
+ ~ "License"); you may not use this file except in compliance
+ ~ with the License. You may obtain a copy of the License at
+ ~
+ ~ http://www.apache.org/licenses/LICENSE-2.0
+ ~
+ ~ Unless required by applicable law or agreed to in writing,
+ ~ software distributed under the License is distributed on an
+ ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ ~ KIND, either express or implied. See the License for the
+ ~ specific language governing permissions and limitations
+ ~ under the License.
+ -->
+
+Compaction in Apache Druid is a strategy to optimize segment size. Compaction tasks read an existing set of segments for a given time range and combine the data into a new "compacted" set of segments. The compacted segments are generally larger, but there are fewer of them. Compaction can sometimes increase performance because it reduces the number of segments and, consequently, the per-segment processing and the memory overhead required for ingestion and for querying paths.
+
+As a strategy, compaction is effective when you have data arriving out of chronological order resulting in lots of small segments. For example if you are appending data using `appendToExisting` for [native batch](./native_batch.md) ingestion. Conversely, if you are rewriting your data with each ingestion task, you don't need to use compaction. See [Segment optimization](../operations/segment-optimization.md) for guidance to determine if compaction will help in your case.
Review comment:
```suggestion
As a strategy, compaction is effective when you have data arriving out of chronological order resulting in lots of small segments. This often happens, for example, if you are appending data using `appendToExisting` for [native batch](./native_batch.md) ingestion. Conversely, if you are rewriting your data with each ingestion task, you don't need to use compaction. See [Segment optimization](../operations/segment-optimization.md) for guidance to determine if compaction will help in your case.
```
##########
File path: docs/ingestion/compaction.md
##########
@@ -0,0 +1,207 @@
+---
+id: compaction
+title: "Compaction"
+description: "Defines compaction and automatic compaction (auto-compaction or autocompaction) as a strategy for segment optimization. Use cases for compaction. Describes compaction task configuration."
+---
+
+<!--
+ ~ Licensed to the Apache Software Foundation (ASF) under one
+ ~ or more contributor license agreements. See the NOTICE file
+ ~ distributed with this work for additional information
+ ~ regarding copyright ownership. The ASF licenses this file
+ ~ to you under the Apache License, Version 2.0 (the
+ ~ "License"); you may not use this file except in compliance
+ ~ with the License. You may obtain a copy of the License at
+ ~
+ ~ http://www.apache.org/licenses/LICENSE-2.0
+ ~
+ ~ Unless required by applicable law or agreed to in writing,
+ ~ software distributed under the License is distributed on an
+ ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ ~ KIND, either express or implied. See the License for the
+ ~ specific language governing permissions and limitations
+ ~ under the License.
+ -->
+
+Compaction in Apache Druid is a strategy to optimize segment size. Compaction tasks read an existing set of segments for a given time range and combine the data into a new "compacted" set of segments. The compacted segments are generally larger, but there are fewer of them. Compaction can sometimes increase performance because it reduces the number of segments and, consequently, the per-segment processing and the memory overhead required for ingestion and for querying paths.
+
+As a strategy, compaction is effective when you have data arriving out of chronological order resulting in lots of small segments. For example if you are appending data using `appendToExisting` for [native batch](./native_batch.md) ingestion. Conversely, if you are rewriting your data with each ingestion task, you don't need to use compaction. See [Segment optimization](../operations/segment-optimization.md) for guidance to determine if compaction will help in your case.
+
+## Types of segment compaction
+You can configure the Druid Coordinator to perform automatic compaction, also called auto-compaction, for a datasource. Using a segment search policy, the coordinator periodically identifies segments for compaction starting with the newest to oldest. When segments can benefit from compaction, the coordinator automatically submits a compaction task.
+
+Automatic compaction works in most use cases and should be your first option. To learn more about automatic compaction, see [Compacting Segments](../design/coordinator.md#compacting-segments).
+
+In cases where you require more control over compaction, you can manually submit compaction tasks. For example:
+- Automatic compaction is too slow.
+- You want to force compaction for a specific time range.
+- Compacting recent data before older data suboptimal is suboptimal in your environment.
+
+See [Setting up a manual compaction task](#setting-up-manual-compaction) more about manual compaction tasks.
Review comment:
```suggestion
See [Setting up a manual compaction task](#setting-up-manual-compaction) for more about manual compaction tasks.
```
##########
File path: docs/configuration/index.md
##########
@@ -820,24 +820,24 @@ A description of the compaction config is:
|`taskPriority`|[Priority](../ingestion/tasks.md#priority) of compaction task.|no (default = 25)|
|`inputSegmentSizeBytes`|Maximum number of total segment bytes processed per compaction task. Since a time chunk must be processed in its entirety, if the segments for a particular time chunk have a total size in bytes greater than this parameter, compaction will not run for that time chunk. Because each compaction task runs with a single thread, setting this value too far above 1–2GB will result in compaction tasks taking an excessive amount of time.|no (default = 419430400)|
|`maxRowsPerSegment`|Max number of rows per segment after compaction.|no|
-|`skipOffsetFromLatest`|The offset for searching segments to be compacted. Strongly recommended to set for realtime dataSources. |no (default = "P1D")|
+|`skipOffsetFromLatest`|The offset for searching segments to be compacted in [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) duration format. Strongly recommended to set for realtime dataSources. See [Data handling with compaction](../ingestion/compaction.md#data-handling-with-compaction)|no (default = "P1D")|
|`tuningConfig`|Tuning config for compaction tasks. See below [Compaction Task TuningConfig](#compaction-tuningconfig).|no|
|`taskContext`|[Task context](../ingestion/tasks.md#context) for compaction tasks.|no|
+|`granularitySpec`|Custom `granularitySpec` to describe the `segmentGranularity` for the compacted segments.|No|
An example of compaction config is:
```json
{
- "dataSource": "wikiticker"
+ "dataSource": "wikiticker",
+ "granularitySpec" : {
+ "segmentGranularity : "none"
+ }
}
```
-Note that compaction tasks can fail if their locks are revoked by other tasks of higher priorities.
-Since realtime tasks have a higher priority than compaction task by default,
-it can be problematic if there are frequent conflicts between compaction tasks and realtime tasks.
-If this is the case, the coordinator's automatic compaction might get stuck because of frequent compaction task failures.
-This kind of problem may happen especially in Kafka/Kinesis indexing systems which allow late data arrival.
-If you see this problem, it's recommended to set `skipOffsetFromLatest` to some large enough value to avoid such conflicts between compaction tasks and realtime tasks.
+Compaction tasks fail when higher priority tasks cause Druid to revokes their locks. By default, realtime tasks like ingestion have a higher priority than compaction tasks. Therefore frequent conflicts between compaction tasks and realtime tasks can cause the coordinator's automatic compaction to get stuck.
+You may see this issue with streaming ingestion from Kafka and Kinesis that ingest late arriving data. To mitigate this problem, set `skipOffsetFromLatest` to a value large enough to avoid conflicts between compaction tasks and realtime ingestion tasks.
Review comment:
I think the first "that" should be a "which"? i.e.:
"You may see this issue with streaming ingestion sources such as Kafka and Kinesis, which require ingestion of late-arriving data."
This is a clarifying rewrite in general though. I feel as though I now understand `skipOffsetFromLatest`!
##########
File path: docs/ingestion/index.md
##########
@@ -196,7 +196,7 @@ that datasource leads to much faster query times. This can often be done with ju
footprint, since abbreviated datasources tend to be substantially smaller.
- If you are using a [best-effort rollup](#perfect-rollup-vs-best-effort-rollup) ingestion configuration that does not guarantee perfect
rollup, you can potentially improve your rollup ratio by switching to a guaranteed perfect rollup option, or by
-[reindexing](data-management.md#compaction-and-reindexing) your data in the background after initial ingestion.
+[reindexing](data-management.md#reingesting-data) or [compacting](./compaction.md) your data in the background after initial ingestion.
Review comment:
```suggestion
[reindexing](../data-management.md#reingesting-data) or [compacting](../compaction.md) your data in the background after initial ingestion.
```
##########
File path: docs/configuration/index.md
##########
@@ -820,24 +820,24 @@ A description of the compaction config is:
|`taskPriority`|[Priority](../ingestion/tasks.md#priority) of compaction task.|no (default = 25)|
|`inputSegmentSizeBytes`|Maximum number of total segment bytes processed per compaction task. Since a time chunk must be processed in its entirety, if the segments for a particular time chunk have a total size in bytes greater than this parameter, compaction will not run for that time chunk. Because each compaction task runs with a single thread, setting this value too far above 1–2GB will result in compaction tasks taking an excessive amount of time.|no (default = 419430400)|
|`maxRowsPerSegment`|Max number of rows per segment after compaction.|no|
-|`skipOffsetFromLatest`|The offset for searching segments to be compacted. Strongly recommended to set for realtime dataSources. |no (default = "P1D")|
+|`skipOffsetFromLatest`|The offset for searching segments to be compacted in [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) duration format. Strongly recommended to set for realtime dataSources. See [Data handling with compaction](../ingestion/compaction.md#data-handling-with-compaction)|no (default = "P1D")|
|`tuningConfig`|Tuning config for compaction tasks. See below [Compaction Task TuningConfig](#compaction-tuningconfig).|no|
|`taskContext`|[Task context](../ingestion/tasks.md#context) for compaction tasks.|no|
+|`granularitySpec`|Custom `granularitySpec` to describe the `segmentGranularity` for the compacted segments.|No|
An example of compaction config is:
```json
{
- "dataSource": "wikiticker"
+ "dataSource": "wikiticker",
+ "granularitySpec" : {
+ "segmentGranularity : "none"
+ }
}
```
-Note that compaction tasks can fail if their locks are revoked by other tasks of higher priorities.
-Since realtime tasks have a higher priority than compaction task by default,
-it can be problematic if there are frequent conflicts between compaction tasks and realtime tasks.
-If this is the case, the coordinator's automatic compaction might get stuck because of frequent compaction task failures.
-This kind of problem may happen especially in Kafka/Kinesis indexing systems which allow late data arrival.
-If you see this problem, it's recommended to set `skipOffsetFromLatest` to some large enough value to avoid such conflicts between compaction tasks and realtime tasks.
+Compaction tasks fail when higher priority tasks cause Druid to revokes their locks. By default, realtime tasks like ingestion have a higher priority than compaction tasks. Therefore frequent conflicts between compaction tasks and realtime tasks can cause the coordinator's automatic compaction to get stuck.
Review comment:
```suggestion
Compaction tasks fail when higher priority tasks cause Druid to revoke their locks. By default, realtime tasks like ingestion have a higher priority than compaction tasks. Therefore frequent conflicts between compaction tasks and realtime tasks can cause the coordinator's automatic compaction to get stuck.
```
##########
File path: docs/ingestion/compaction.md
##########
@@ -0,0 +1,207 @@
+---
+id: compaction
+title: "Compaction"
+description: "Defines compaction and automatic compaction (auto-compaction or autocompaction) as a strategy for segment optimization. Use cases for compaction. Describes compaction task configuration."
+---
+
+<!--
+ ~ Licensed to the Apache Software Foundation (ASF) under one
+ ~ or more contributor license agreements. See the NOTICE file
+ ~ distributed with this work for additional information
+ ~ regarding copyright ownership. The ASF licenses this file
+ ~ to you under the Apache License, Version 2.0 (the
+ ~ "License"); you may not use this file except in compliance
+ ~ with the License. You may obtain a copy of the License at
+ ~
+ ~ http://www.apache.org/licenses/LICENSE-2.0
+ ~
+ ~ Unless required by applicable law or agreed to in writing,
+ ~ software distributed under the License is distributed on an
+ ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ ~ KIND, either express or implied. See the License for the
+ ~ specific language governing permissions and limitations
+ ~ under the License.
+ -->
+
+Compaction in Apache Druid is a strategy to optimize segment size. Compaction tasks read an existing set of segments for a given time range and combine the data into a new "compacted" set of segments. The compacted segments are generally larger, but there are fewer of them. Compaction can sometimes increase performance because it reduces the number of segments and, consequently, the per-segment processing and the memory overhead required for ingestion and for querying paths.
+
+As a strategy, compaction is effective when you have data arriving out of chronological order resulting in lots of small segments. For example if you are appending data using `appendToExisting` for [native batch](./native_batch.md) ingestion. Conversely, if you are rewriting your data with each ingestion task, you don't need to use compaction. See [Segment optimization](../operations/segment-optimization.md) for guidance to determine if compaction will help in your case.
+
+## Types of segment compaction
+You can configure the Druid Coordinator to perform automatic compaction, also called auto-compaction, for a datasource. Using a segment search policy, the coordinator periodically identifies segments for compaction starting with the newest to oldest. When segments can benefit from compaction, the coordinator automatically submits a compaction task.
+
+Automatic compaction works in most use cases and should be your first option. To learn more about automatic compaction, see [Compacting Segments](../design/coordinator.md#compacting-segments).
+
+In cases where you require more control over compaction, you can manually submit compaction tasks. For example:
+- Automatic compaction is too slow.
+- You want to force compaction for a specific time range.
+- Compacting recent data before older data suboptimal is suboptimal in your environment.
+
+See [Setting up a manual compaction task](#setting-up-manual-compaction) more about manual compaction tasks.
+
+
+## Data handling with compaction
+During compaction, Druid overwrites the original set of segments with the compacted set without modifying the data. During compaction Druid locks the segments for the time interval being compacted to ensure data consistency.
+
+If an ingestion task needs to write data to a segment for a time interval locked for compaction, the ingestion task supersedes the compaction task and the compaction task fails without finishing. For manual compaction tasks you can adjust the input spec interval to avoid conflicts between ingestion and compaction. For automatic compaction, you can set the `skipOffsetFromLatest` key to adjustment the auto compaction starting point from the current time to reduce the chance of conflicts between ingestion and compaction. See [Compaction dynamic configuration](../configuration/index.md#compaction-dynamic-configuration) for more information.
+
+### Segment granularity handling
+
+Unless you modify the segment granularity in the [granularity spec](#compaction-granularity-spec), Druid attempts to retain the granularity for the compacted segments. When segments have different segment granularities with no overlap in interval Druid creates a separate compaction task for each to retain the segment granularity in the compacted segment. If segments have different segment granularities before compaction but there is some overlap in interval, Druid attempts find start and end of the overlapping interval and uses the closest segment granularity level for the compacted segment.
+
+### Query granularity handling
+
+Unless you modify the query granularity in the [granularity spec](#compaction-granularity-spec), Druid retains the query granularity for the compacted segments. If segments have different query granularities before compaction, Druid chooses the finest level of granularity for the resulting compacted segment. For example if a compaction task combines two segments, one with day query granularity and one with minute query granularity, the resulting segment uses minute query granularity.
+
+> In Apache Druid 0.21.0 and prior, Druid sets the granularity for compacted segments to the default granularity of `NONE` regardless of the query granularity of the original segments.
+
+If you configure query granularity in compaction to go from a finer granularity like month to a coarser query granularity like year, then Druid overshadows the original segment with finer granularity. Because the new segments have a coarser granularity, running a kill task to remove the overshadowed segments for those intervals will cause you to permanently lose the finer granularity data.
+
+
+### Dimension handling
+Apache Druid supports schema changes. Therefore, dimensions can be different across segments even if they are a part of the same data source. See [Different schemas among segments](../design/segments.md#different-schemas-among-segments). If the input segments have different dimensions, the resulting compacted segment includes all dimensions of the input segments.
+
+Even when the input segments have the same set of dimensions, the dimension order or the data type of dimensions can be different. For example, the data type of some dimensions can be changed from `string` to primitive types, or the order of dimensions can be changed for better locality. In this case, the dimensions of recent segments precede that of old segments in terms of data types and the ordering because more recent segments are more likely to have the preferred order and data types.
+
+If you want to use your own ordering and types, you can specify a custom `dimensionsSpec` in the compaction task spec.
+
+### Rollup
+Druid only rolls up the output segment when `rollup` is set for all input segments. See [Roll-up](../ingestion/index.md#rollup) for more details.
+You can check that your segments are rolled up or not by using [Segment Metadata Queries](../querying/segmentmetadataquery.md#analysistypes).
+
+## Setting up manual compaction
+
+To perform a manual compaction, you submit a compaction task. Compaction tasks merge all segments for the defined interval according to the following syntax:
+
+```json
+{
+ "type": "compact",
+ "id": <task_id>,
+ "dataSource": <task_datasource>,
+ "ioConfig": <IO config>,
+ "dimensionsSpec" <custom dimensionsSpec>,
+ "metricsSpec" <custom metricsSpec>,
+ "tuningConfig" <parallel indexing task tuningConfig>,
+ "context": <task context>
+}
+```
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`type`|Task type. Should be `compact`|Yes|
+|`id`|Task id|No|
+|`dataSource`|Data source name to compact|Yes|
+|`ioConfig`|I/O configuration for compaction task. See [Compaction I/O configuration](#compaction-io-configuration) for details.|Yes|
+|`dimensionsSpec`|Custom dimensions spec. The compaction task uses the specified dimensions spec if it exists instead of generating one.|No|
+|`metricsSpec`|Custom metrics spec. The compaction task uses the specified metrics spec rather than generating one.|No|
+|`segmentGranularity`|When set, the compaction task changes the segment granularity for the given interval. Deprecated. Use `granularitySpec`. |No.|
+|`tuningConfig`|[Parallel indexing task tuningConfig](./native-batch.md#tuningconfig)|No|
+|`context`|[Task context](./tasks.md#context)|No|
+|`granularitySpec`|Custom `granularitySpec` to describe the `segmentGranularity` and `queryGranularity` for the compacted segments. See [Compaction granularitySpec](#compaction-granularity-spec).|No|
+
+To control the number of result segments per time chunk, you can set [maxRowsPerSegment](../configuration/index.md#compaction-dynamic-configuration) or [numShards](../ingestion/native-batch.md#tuningconfig).
+
+> You can run multiple compaction tasks at the same time. For example, you can run 12 compaction tasks per month instead of running a single task for the entire year.
+
+A compaction task internally generates an `index` task spec for performing compaction work with some fixed parameters. For example, its `inputSource` is always the [DruidInputSource](native-batch.md#druid-input-source), and `dimensionsSpec` and `metricsSpec`include all dimensions and metrics of the input segments by default.
+
+Compaction tasks exit without doing anything and issue a failure status code:
+- if the interval you specify has no data segments loaded<br>
+OR
+- if the interval you specify is empty.
+
+The output segment can have different metadata from the input segments unless all input segments have the same metadata.
Review comment:
I might slow this down a bit, and make it clear that (I think) it's a caveat or note (not a capability). Maybe something like:
```suggestion
Note that the metadata between compaction input segments and the resulting output segment may differ if the metadata among the input segments differs as well. If all input segments have the same metadata, however, the resulting output segment will have the same metadata as all input segments.
```
... or something like that.
##########
File path: docs/ingestion/compaction.md
##########
@@ -0,0 +1,207 @@
+---
+id: compaction
+title: "Compaction"
+description: "Defines compaction and automatic compaction (auto-compaction or autocompaction) as a strategy for segment optimization. Use cases for compaction. Describes compaction task configuration."
+---
+
+<!--
+ ~ Licensed to the Apache Software Foundation (ASF) under one
+ ~ or more contributor license agreements. See the NOTICE file
+ ~ distributed with this work for additional information
+ ~ regarding copyright ownership. The ASF licenses this file
+ ~ to you under the Apache License, Version 2.0 (the
+ ~ "License"); you may not use this file except in compliance
+ ~ with the License. You may obtain a copy of the License at
+ ~
+ ~ http://www.apache.org/licenses/LICENSE-2.0
+ ~
+ ~ Unless required by applicable law or agreed to in writing,
+ ~ software distributed under the License is distributed on an
+ ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ ~ KIND, either express or implied. See the License for the
+ ~ specific language governing permissions and limitations
+ ~ under the License.
+ -->
+
+Compaction in Apache Druid is a strategy to optimize segment size. Compaction tasks read an existing set of segments for a given time range and combine the data into a new "compacted" set of segments. The compacted segments are generally larger, but there are fewer of them. Compaction can sometimes increase performance because it reduces the number of segments and, consequently, the per-segment processing and the memory overhead required for ingestion and for querying paths.
+
+As a strategy, compaction is effective when you have data arriving out of chronological order resulting in lots of small segments. For example if you are appending data using `appendToExisting` for [native batch](./native_batch.md) ingestion. Conversely, if you are rewriting your data with each ingestion task, you don't need to use compaction. See [Segment optimization](../operations/segment-optimization.md) for guidance to determine if compaction will help in your case.
+
+## Types of segment compaction
+You can configure the Druid Coordinator to perform automatic compaction, also called auto-compaction, for a datasource. Using a segment search policy, the coordinator periodically identifies segments for compaction starting with the newest to oldest. When segments can benefit from compaction, the coordinator automatically submits a compaction task.
+
+Automatic compaction works in most use cases and should be your first option. To learn more about automatic compaction, see [Compacting Segments](../design/coordinator.md#compacting-segments).
+
+In cases where you require more control over compaction, you can manually submit compaction tasks. For example:
+- Automatic compaction is too slow.
+- You want to force compaction for a specific time range.
+- Compacting recent data before older data suboptimal is suboptimal in your environment.
+
+See [Setting up a manual compaction task](#setting-up-manual-compaction) more about manual compaction tasks.
+
+
+## Data handling with compaction
+During compaction, Druid overwrites the original set of segments with the compacted set without modifying the data. During compaction Druid locks the segments for the time interval being compacted to ensure data consistency.
+
+If an ingestion task needs to write data to a segment for a time interval locked for compaction, the ingestion task supersedes the compaction task and the compaction task fails without finishing. For manual compaction tasks you can adjust the input spec interval to avoid conflicts between ingestion and compaction. For automatic compaction, you can set the `skipOffsetFromLatest` key to adjustment the auto compaction starting point from the current time to reduce the chance of conflicts between ingestion and compaction. See [Compaction dynamic configuration](../configuration/index.md#compaction-dynamic-configuration) for more information.
+
+### Segment granularity handling
+
+Unless you modify the segment granularity in the [granularity spec](#compaction-granularity-spec), Druid attempts to retain the granularity for the compacted segments. When segments have different segment granularities with no overlap in interval Druid creates a separate compaction task for each to retain the segment granularity in the compacted segment. If segments have different segment granularities before compaction but there is some overlap in interval, Druid attempts find start and end of the overlapping interval and uses the closest segment granularity level for the compacted segment.
+
+### Query granularity handling
+
+Unless you modify the query granularity in the [granularity spec](#compaction-granularity-spec), Druid retains the query granularity for the compacted segments. If segments have different query granularities before compaction, Druid chooses the finest level of granularity for the resulting compacted segment. For example if a compaction task combines two segments, one with day query granularity and one with minute query granularity, the resulting segment uses minute query granularity.
+
+> In Apache Druid 0.21.0 and prior, Druid sets the granularity for compacted segments to the default granularity of `NONE` regardless of the query granularity of the original segments.
+
+If you configure query granularity in compaction to go from a finer granularity like month to a coarser query granularity like year, then Druid overshadows the original segment with finer granularity. Because the new segments have a coarser granularity, running a kill task to remove the overshadowed segments for those intervals will cause you to permanently lose the finer granularity data.
+
+
+### Dimension handling
+Apache Druid supports schema changes. Therefore, dimensions can be different across segments even if they are a part of the same data source. See [Different schemas among segments](../design/segments.md#different-schemas-among-segments). If the input segments have different dimensions, the resulting compacted segment includes all dimensions of the input segments.
+
+Even when the input segments have the same set of dimensions, the dimension order or the data type of dimensions can be different. For example, the data type of some dimensions can be changed from `string` to primitive types, or the order of dimensions can be changed for better locality. In this case, the dimensions of recent segments precede that of old segments in terms of data types and the ordering because more recent segments are more likely to have the preferred order and data types.
+
+If you want to use your own ordering and types, you can specify a custom `dimensionsSpec` in the compaction task spec.
+
+### Rollup
+Druid only rolls up the output segment when `rollup` is set for all input segments. See [Roll-up](../ingestion/index.md#rollup) for more details.
+You can check that your segments are rolled up or not by using [Segment Metadata Queries](../querying/segmentmetadataquery.md#analysistypes).
+
+## Setting up manual compaction
+
+To perform a manual compaction, you submit a compaction task. Compaction tasks merge all segments for the defined interval according to the following syntax:
+
+```json
+{
+ "type": "compact",
+ "id": <task_id>,
+ "dataSource": <task_datasource>,
+ "ioConfig": <IO config>,
+ "dimensionsSpec" <custom dimensionsSpec>,
+ "metricsSpec" <custom metricsSpec>,
+ "tuningConfig" <parallel indexing task tuningConfig>,
+ "context": <task context>
+}
+```
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`type`|Task type. Should be `compact`|Yes|
+|`id`|Task id|No|
+|`dataSource`|Data source name to compact|Yes|
+|`ioConfig`|I/O configuration for compaction task. See [Compaction I/O configuration](#compaction-io-configuration) for details.|Yes|
+|`dimensionsSpec`|Custom dimensions spec. The compaction task uses the specified dimensions spec if it exists instead of generating one.|No|
+|`metricsSpec`|Custom metrics spec. The compaction task uses the specified metrics spec rather than generating one.|No|
+|`segmentGranularity`|When set, the compaction task changes the segment granularity for the given interval. Deprecated. Use `granularitySpec`. |No.|
+|`tuningConfig`|[Parallel indexing task tuningConfig](./native-batch.md#tuningconfig)|No|
+|`context`|[Task context](./tasks.md#context)|No|
+|`granularitySpec`|Custom `granularitySpec` to describe the `segmentGranularity` and `queryGranularity` for the compacted segments. See [Compaction granularitySpec](#compaction-granularity-spec).|No|
+
+To control the number of result segments per time chunk, you can set [maxRowsPerSegment](../configuration/index.md#compaction-dynamic-configuration) or [numShards](../ingestion/native-batch.md#tuningconfig).
+
+> You can run multiple compaction tasks at the same time. For example, you can run 12 compaction tasks per month instead of running a single task for the entire year.
+
+A compaction task internally generates an `index` task spec for performing compaction work with some fixed parameters. For example, its `inputSource` is always the [DruidInputSource](native-batch.md#druid-input-source), and `dimensionsSpec` and `metricsSpec`include all dimensions and metrics of the input segments by default.
+
+Compaction tasks exit without doing anything and issue a failure status code:
+- if the interval you specify has no data segments loaded<br>
+OR
+- if the interval you specify is empty.
+
+The output segment can have different metadata from the input segments unless all input segments have the same metadata.
+
+
+### Example compaction task
+The following JSON illustrates a compaction task to compact _all segments_ within the interval `2017-01-01/2018-01-01` and create new segments:
+
+```json
+{
+ "type" : "compact",
+ "dataSource" : "wikipedia",
+ "ioConfig" : {
+ "type": "compact",
+ "inputSpec": {
+ "type": "interval",
+ "interval": "2017-01-01/2018-01-01",
+ }
+ }
+}
+```
+
+This task doesn't specify a `granularitySpec` so Druid retains the original segment granularity unchanged when compaction is complete.
+
+### Compaction I/O configuration
+
+The compaction `ioConfig` requires specifying `inputSpec` as follows:
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`type`|Task type. Should be `compact`|Yes|
+|`inputSpec`|Input specification|Yes|
+
+There are two supported `inputSpec`s for now.
+
+The interval `inputSpec` is:
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`type`|Task type. Should be `interval`|Yes|
+|`interval`|Interval to compact|Yes|
+
+The segments `inputSpec` is:
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`type`|Task type. Should be `segments`|Yes|
+|`segments`|A list of segment IDs|Yes|
+
+### Compaction granularity spec
+
+You can optionally use the `granularitySpec` object to configure the segment granularity and the query granularity of the compacted segments. They syntax is as follows:
+```json
+ "type": "compact",
+ "id": <task_id>,
+ "dataSource": <task_datasource>,
+ ...
+ ,
+ "granularitySpec": {
+ "segmentGranularity": <time_period>,
+ "queryGranularity": <time_period>
+ }
+ ...
+```
+
+`granularitySpec` takes the following keys:
+
+|Field|Description|Required|
+|-----|-----------|--------|
+|`segmentGranularity`|Time chunking period for the segment granularity. Defaults to 'null'. Accepts all [Query granularities](../querying/granularities.md).|No|
+|`queryGranularity`|Time chunking period for the query granularity. Defaults to 'null'. Accepts all [Query granularities](../querying/granularities.md). Not supported for automatic compaction.|No|
+
+For example, to set the set the segment granularity to "day" and the query granularity to "hour":
Review comment:
```suggestion
For example, to set the segment granularity to "day" and the query granularity to "hour":
```
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