druid/docs/content/ingestion/compaction.md

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Compaction Task

Compaction tasks merge all segments of the given interval. The syntax is:

{
    "type": "compact",
    "id": <task_id>,
    "dataSource": <task_datasource>,
    "interval": <interval to specify segments to be merged>,
    "dimensions" <custom dimensionsSpec>,
    "tuningConfig" <index task tuningConfig>,
    "context": <task context>
}

Field	Description	Required
type	Task type. Should be compact	Yes
id	Task id	No
dataSource	dataSource name to be compacted	Yes
interval	interval of segments to be compacted	Yes
dimensions	custom dimensionsSpec. compaction task will use this dimensionsSpec if exist instead of generating one. See below for more details.	No
tuningConfig	Index task tuningConfig	No
context	Task context	No

An example of compaction task is

{
  "type" : "compact",
  "dataSource" : "wikipedia",
  "interval" : "2017-01-01/2018-01-01"
}

This compaction task reads all segments of the interval 2017-01-01/2018-01-01 and results in new segments. Note that intervals of the input segments are merged into a single interval of 2017-01-01/2018-01-01 no matter what the segmentGranularity was. To control the number of result segments, you can set targetPartitionSize or numShards. See indexTuningConfig for more details. To merge each day's worth of data into separate segments, you can submit multiple compact tasks, one for each day. They will run in parallel.

A compaction task internally generates an index task spec for performing compaction work with some fixed parameters. For example, its firehose is always the ingestSegmentSpec, and dimensionsSpec and metricsSpec include all dimensions and metrics of the input segments by default.

Compaction tasks will exit with a failure status code, without doing anything, if the interval you specify has no data segments loaded in it (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.

• Dimensions: since Druid supports schema change, the dimensions can be different across segments even if they are a part of the same dataSource. If the input segments have different dimensions, the output segment basically includes all dimensions of the input segments. However, even if 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. This is because more recent segments are more likely to have the new desired 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.
• Roll-up: the output segment is rolled up only when rollup is set for all input segments. See Roll-up for more details. You can check that your segments are rolled up or not by using Segment Metadata Queries.
• Partitioning: The compaction task is a special form of native batch indexing task, so it always uses hash-based partitioning on the full set of dimensions.