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druid/docs/content/ingestion/tasks.md

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Tasks

Tasks are run on middle managers and always operate on a single data source. Tasks are submitted using POST requests.

There are several different types of tasks.

Segment Creation Tasks

Hadoop Index Task

See batch ingestion.

Index Task

The Index Task is a simpler variation of the Index Hadoop task that is designed to be used for smaller data sets. The task executes within the indexing service and does not require an external Hadoop setup to use. The grammar of the index task is as follows:

{
  "type" : "index",
  "spec" : {
    "dataSchema" : {
      "dataSource" : "wikipedia",
      "parser" : {
        "type" : "string",
        "parseSpec" : {
          "format" : "json",
          "timestampSpec" : {
            "column" : "timestamp",
            "format" : "auto"
          },
          "dimensionsSpec" : {
            "dimensions": ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"],
            "dimensionExclusions" : [],
            "spatialDimensions" : []
          }
        }
      },
      "metricsSpec" : [
        {
          "type" : "count",
          "name" : "count"
        },
        {
          "type" : "doubleSum",
          "name" : "added",
          "fieldName" : "added"
        },
        {
          "type" : "doubleSum",
          "name" : "deleted",
          "fieldName" : "deleted"
        },
        {
          "type" : "doubleSum",
          "name" : "delta",
          "fieldName" : "delta"
        }
      ],
      "granularitySpec" : {
        "type" : "uniform",
        "segmentGranularity" : "DAY",
        "queryGranularity" : "NONE",
        "intervals" : [ "2013-08-31/2013-09-01" ]
      }
    },
    "ioConfig" : {
      "type" : "index",
      "firehose" : {
        "type" : "local",
        "baseDir" : "examples/indexing/",
        "filter" : "wikipedia_data.json"
       }
    },
    "tuningConfig" : {
      "type" : "index",
      "targetPartitionSize" : 5000000,
      "maxRowsInMemory" : 75000
    }
  }
}

Task Properties

property description required?
type The task type, this should always be "index". yes
id The task ID. If this is not explicitly specified, Druid generates the task ID using task type, data source name, interval, and date-time stamp. no
spec The ingestion spec including the data schema, IOConfig, and TuningConfig. See below for more details. yes
context Context containing various task configuration parameters. See below for more details. no

Task Priority

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

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.

"context" : {
  "priority" : 100
}

DataSchema

This field is required.

See Ingestion

IOConfig

property description default required?
type The task type, this should always be "index". none yes
firehose Specify a Firehose here. none yes
appendToExisting Creates segments as additional shards of the latest version, effectively appending to the segment set instead of replacing it. This will only work if the existing segment set has extendable-type shardSpecs (which can be forced by setting 'forceExtendableShardSpecs' in the tuning config). false no

TuningConfig

The tuningConfig is optional and default parameters will be used if no tuningConfig is specified. See below for more details.

property description default required?
type The task type, this should always be "index". none yes
targetPartitionSize Used in sharding. Determines how many rows are in each segment. 5000000 no
maxRowsInMemory Used in determining when intermediate persists to disk should occur. 75000 no
maxTotalRows Total number of rows in segments waiting for being published. Used in determining when intermediate publish should occur. 150000 no
numShards Directly specify the number of shards to create. If this is specified and 'intervals' is specified in the granularitySpec, the index task can skip the determine intervals/partitions pass through the data. numShards cannot be specified if targetPartitionSize is set. null no
indexSpec defines segment storage format options to be used at indexing time, see IndexSpec null no
maxPendingPersists Maximum number of persists that can be pending but not started. If this limit would be exceeded by a new intermediate persist, ingestion will block until the currently-running persist finishes. Maximum heap memory usage for indexing scales with maxRowsInMemory * (2 + maxPendingPersists). 0 (meaning one persist can be running concurrently with ingestion, and none can be queued up) no
forceExtendableShardSpecs Forces use of extendable shardSpecs. Experimental feature intended for use with the Kafka indexing service extension. false no
forceGuaranteedRollup Forces guaranteeing the perfect rollup. The perfect rollup optimizes the total size of generated segments and querying time while indexing time will be increased. This flag cannot be used with either appendToExisting of IOConfig or forceExtendableShardSpecs. For more details, see the below Segment publishing modes section. false no
reportParseExceptions If true, exceptions encountered during parsing will be thrown and will halt ingestion; if false, unparseable rows and fields will be skipped. false no
publishTimeout Milliseconds to wait for publishing segments. It must be >= 0, where 0 means to wait forever. 0 no

IndexSpec

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)

Segment publishing modes

While ingesting data using the Index task, it creates segments from the input data and publishes them. For segment publishing, the Index task supports two segment publishing modes, i.e., bulk publishing mode and incremental publishing mode for perfect rollup and best-effort rollup, respectively.

In the bulk publishing mode, every segment is published at the very end of the index task. Until then, created segments are stored in the memory and local storage of the node running the index task. As a result, this mode might cause a problem due to limited storage capacity, and is not recommended to use in production.

On the contrary, in the incremental publishing mode, segments are incrementally published, that is they can be published in the middle of the index task. More precisely, the index task collects data and stores created segments in the memory and disks of the node running that task until the total number of collected rows exceeds maxTotalRows. Once it exceeds, the index task immediately publishes all segments created until that moment, cleans all published segments up, and continues to ingest remaining data.

To enable bulk publishing mode, forceGuaranteedRollup should be set in the TuningConfig. Note that this option cannot be used with either forceExtendableShardSpecs of TuningConfig or appendToExisting of IOConfig.

Segment Merging Tasks

Append Task

Append tasks append a list of segments together into a single segment (one after the other). The grammar is:

{
    "type": "append",
    "id": <task_id>,
    "dataSource": <task_datasource>,
    "segments": <JSON list of DataSegment objects to append>,
    "aggregations": <optional list of aggregators>,
    "context": <task context>
}

Merge Task

Merge tasks merge a list of segments together. Any common timestamps are merged. If rollup is disabled as part of ingestion, common timestamps are not merged and rows are reordered by their timestamp.

The grammar is:

{
    "type": "merge",
    "id": <task_id>,
    "dataSource": <task_datasource>,
    "aggregations": <list of aggregators>,
    "rollup": <whether or not to rollup data during a merge>,
    "segments": <JSON list of DataSegment objects to merge>,
    "context": <task context>
}

Same Interval Merge Task

Same Interval Merge task is a shortcut of merge task, all segments in the interval are going to be merged.

The grammar is:

{
    "type": "same_interval_merge",
    "id": <task_id>,
    "dataSource": <task_datasource>,
    "aggregations": <list of aggregators>,
    "rollup": <whether or not to rollup data during a merge>,
    "interval": <DataSegment objects in this interval are going to be merged>,
    "context": <task context>
}

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 merges all segments of the interval 2017-01-01/2018-01-01 into a single segment. To merge each day's worth of data into a separate segment, 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.

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). 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.

Segment Destroying Tasks

Kill Task

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:

{
    "type": "kill",
    "id": <task_id>,
    "dataSource": <task_datasource>,
    "interval" : <all_segments_in_this_interval_will_die!>,
    "context": <task context>
}

Misc. Tasks

Version Converter Task

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.

Hadoop Convert Segment Task

{
  "type": "hadoop_convert_segment",
  "dataSource":"some_datasource",
  "interval":"2013/2015",
  "indexSpec":{"bitmap":{"type":"concise"},"dimensionCompression":"lz4","metricCompression":"lz4"},
  "force": true,
  "validate": false,
  "distributedSuccessCache":"hdfs://some-hdfs-nn:9000/user/jobrunner/cache",
  "jobPriority":"VERY_LOW",
  "segmentOutputPath":"s3n://somebucket/somekeyprefix"
}

The values are described below.

Field Type Description Required
type String Convert task identifier Yes: hadoop_convert_segment
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
validate boolean Runs validation between the old and new segment before reporting task success No
distributedSuccessCache URI A location where hadoop should put intermediary files. Yes
jobPriority org.apache.hadoop.mapred.JobPriority as String The priority to set for the hadoop job No
segmentOutputPath URI A base uri for the segment to be placed. Same format as other places a segment output path is needed Yes

Indexing Service Convert Segment Task

{
  "type": "convert_segment",
  "dataSource":"some_datasource",
  "interval":"2013/2015",
  "indexSpec":{"bitmap":{"type":"concise"},"dimensionCompression":"lz4","metricCompression":"lz4"},
  "force": true,
  "validate": false
}
Field Type Description Required (default)
type String Convert task identifier Yes: convert_segment
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.

Noop Task

These tasks start, sleep for a time and are used only for testing. The available grammar is:

{
    "type": "noop",
    "id": <optional_task_id>,
    "interval" : <optional_segment_interval>,
    "runTime" : <optional_millis_to_sleep>,
    "firehose": <optional_firehose_to_test_connect>
}

Task Context

The task context is used for various task configuration parameters. The following parameters apply to all task types.

property default description
taskLockTimeout 300000 task lock timeout in millisecond. For more details, see the below Locking section.
priority Different based on task types. See Task Priority. Task priority
When a task acquires a lock, it sends a request via HTTP and awaits until it receives a response containing the lock acquisition result. As a result, an HTTP timeout error can occur if `taskLockTimeout` is greater than `druid.server.http.maxIdleTime` of overlords.

Locking

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.

Tasks do not need to explicitly release locks, they are released upon task completion. Tasks may potentially release locks early if they desire. Task ids are unique by naming them using UUIDs or the timestamp in which the task was created. Tasks are also part of a "task group", which is a set of tasks that can share interval locks.