Tasks are run on middle managers and always operate on a single data source. Tasks are submitted using [POST requests](../design/indexing-service.html).
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 (See [Partitioning](batch-ingestion.html#partitioning-specification)).
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](../design/index.html#roll-up) for more details.
You can check that your segments are rolled up or not by using [Segment Metadata Queries](../querying/segmentmetadataquery.html#analysistypes).
Kill tasks delete all information about a segment and removes it from deep storage. Killable segments must be disabled (used==0) in the Druid segment table. The available grammar is:
The convert task suite takes active segments and will recompress them using a new IndexSpec. This is handy when doing activities like migrating from Concise to Roaring, or adding dimension compression to old segments.
Upon success the new segments will have the same version as the old segment with `_converted` appended. A convert task may be run against the same interval for the same datasource multiple times. Each execution will append another `_converted` to the version for the segments
There are two types of conversion tasks. One is the Hadoop convert task, and the other is the indexing service convert task. The Hadoop convert task runs on a hadoop cluster, and simply leaves a task monitor on the indexing service (similar to the hadoop batch task). The indexing service convert task runs the actual conversion on the indexing service.
|`dataSource`|String|The datasource to search for segments|Yes|
|`interval`|Interval string|The interval in the datasource to look for segments|Yes|
|`indexSpec`|json|The compression specification for the index|Yes|
|`force`|boolean|Forces the convert task to continue even if binary versions indicate it has been updated recently (you probably want to do this)|No (false)|
|`validate`|boolean|Runs validation between the old and new segment before reporting task success|No (true)|
Unlike the hadoop convert task, the indexing service task draws its output path from the indexing service's configuration.
The indexSpec defines segment storage format options to be used at indexing time, such as bitmap type and column
compression formats. The indexSpec is optional and default parameters will be used if not specified.
|Field|Type|Description|Required|
|-----|----|-----------|--------|
|bitmap|Object|Compression format for bitmap indexes. Should be a JSON object; see below for options.|no (defaults to Concise)|
|dimensionCompression|String|Compression format for dimension columns. Choose from `LZ4`, `LZF`, or `uncompressed`.|no (default == `LZ4`)|
|metricCompression|String|Compression format for metric columns. Choose from `LZ4`, `LZF`, `uncompressed`, or `none`.|no (default == `LZ4`)|
|longEncoding|String|Encoding format for metric and dimension columns with type long. Choose from `auto` or `longs`. `auto` encodes the values using offset or lookup table depending on column cardinality, and store them with variable size. `longs` stores the value as is with 8 bytes each.|no (default == `longs`)|
##### Bitmap types
For Concise bitmaps:
|Field|Type|Description|Required|
|-----|----|-----------|--------|
|type|String|Must be `concise`.|yes|
For Roaring bitmaps:
|Field|Type|Description|Required|
|-----|----|-----------|--------|
|type|String|Must be `roaring`.|yes|
|compressRunOnSerialization|Boolean|Use a run-length encoding where it is estimated as more space efficient.|no (default == `true`)|
Once an overlord node accepts a task, the task acquires locks for the data source and intervals specified in the task.
There are two lock types, i.e., _shared lock_ and _exclusive lock_.
- A task needs to acquire a shared lock before it reads segments of an interval. Multiple shared locks can be acquired for the same dataSource and interval. Shared locks are always preemptable, but they don't preempt each other.
- A task needs to acquire an exclusive lock before it writes segments for an interval. An exclusive lock is also preemptable except while the task is publishing segments.
Each task can have different lock priorities. The locks of higher-priority tasks can preempt the locks of lower-priority tasks. The lock preemption works based on _optimistic locking_. When a lock is preempted, it is not notified to the owner task immediately. Instead, it's notified when the owner task tries to acquire the same lock again. (Note that lock acquisition is idempotent unless the lock is preempted.) In general, tasks don't compete for acquiring locks because they usually targets different dataSources or intervals.
A task writing data into a dataSource must acquire exclusive locks for target intervals. Note that exclusive locks are still preemptable. That is, they also be able to be preempted by higher priority locks unless they are _publishing segments_ in a critical section. Once publishing segments is finished, those locks become preemptable again.
Druid's indexing tasks use locks for atomic data ingestion. Each lock is acquired for the combination of a dataSource and an interval. Once a task acquires a lock, it can write data for the dataSource and the interval of the acquired lock unless the lock is released or preempted. Please see [the below Locking section](#locking)
Each task has a priority which is used for lock acquisition. The locks of higher-priority tasks can preempt the locks of lower-priority tasks if they try to acquire for the same dataSource and interval. If some locks of a task are preempted, the behavior of the preempted task depends on the task implementation. Usually, most tasks finish as failed if they are preempted.
Tasks can have different default priorities depening on their types. Here are a list of default priorities. Higher the number, higher the priority.
|task type|default priority|
|---------|----------------|
|Realtime index task|75|
|Batch index task|50|
|Merge/Append/Compaction task|25|
|Other tasks|0|
You can override the task priority by setting your priority in the task context like below.
