Table of Contents API documentation

Kafka Indexing Service

The Kafka indexing service enables the configuration of supervisors on the Overlord, which facilitate ingestion from Kafka by managing the creation and lifetime of Kafka indexing tasks. These indexing tasks read events using Kafka's own partition and offset mechanism and are therefore able to provide guarantees of exactly-once ingestion. They are also able to read non-recent events from Kafka and are not subject to the window period considerations imposed on other ingestion mechanisms. The supervisor oversees the state of the indexing tasks to coordinate handoffs, manage failures, and ensure that the scalability and replication requirements are maintained.

This service is provided in the druid-kafka-indexing-service core extension (see Including Extensions). Please note that the Kafka indexing service is currently designated as an experimental feature and is subject to the usual experimental caveats.

The Kafka indexing service uses the Java consumer that was introduced in Kafka 0.10.x. As there were protocol changes made in this version, Kafka 0.10.x consumers might not be compatible with older brokers. Ensure that your Kafka brokers are version 0.10.x or better before using this service. Refer Kafka upgrade guide if you are using older version of kafka brokers.

Submitting a Supervisor Spec

The Kafka indexing service requires that the druid-kafka-indexing-service extension be loaded on both the overlord and the middle managers. A supervisor for a dataSource is started by submitting a supervisor spec via HTTP POST to http://<OVERLORD_IP>:<OVERLORD_PORT>/druid/indexer/v1/supervisor, for example:

curl -X POST -H 'Content-Type: application/json' -d @supervisor-spec.json http://localhost:8090/druid/indexer/v1/supervisor

A sample supervisor spec is shown below:

  "type": "kafka",
  "dataSchema": {
    "dataSource": "metrics-kafka",
    "parser": {
      "type": "string",
      "parseSpec": {
        "format": "json",
        "timestampSpec": {
          "column": "timestamp",
          "format": "auto"
        "dimensionsSpec": {
          "dimensions": [],
          "dimensionExclusions": [
    "metricsSpec": [
        "name": "count",
        "type": "count"
        "name": "value_sum",
        "fieldName": "value",
        "type": "doubleSum"
        "name": "value_min",
        "fieldName": "value",
        "type": "doubleMin"
        "name": "value_max",
        "fieldName": "value",
        "type": "doubleMax"
    "granularitySpec": {
      "type": "uniform",
      "segmentGranularity": "HOUR",
      "queryGranularity": "NONE"
  "tuningConfig": {
    "type": "kafka",
    "maxRowsPerSegment": 5000000
  "ioConfig": {
    "topic": "metrics",
    "consumerProperties": {
      "bootstrap.servers": "localhost:9092"
    "taskCount": 1,
    "replicas": 1,
    "taskDuration": "PT1H"

Supervisor Configuration

Field Description Required
type The supervisor type, this should always be kafka. yes
dataSchema The schema that will be used by the Kafka indexing task during ingestion, see Ingestion Spec. yes
tuningConfig A KafkaSupervisorTuningConfig to configure the supervisor and indexing tasks, see below. no
ioConfig A KafkaSupervisorIOConfig to configure the supervisor and indexing tasks, see below. yes


The tuningConfig is optional and default parameters will be used if no tuningConfig is specified.

Field Type Description Required
type String The indexing task type, this should always be kafka. yes
maxRowsInMemory Integer The number of rows to aggregate before persisting. This number is the post-aggregation rows, so it is not equivalent to the number of input events, but the number of aggregated rows that those events result in. This is used to manage the required JVM heap size. Maximum heap memory usage for indexing scales with maxRowsInMemory * (2 + maxPendingPersists). no (default == 75000)
maxRowsPerSegment Integer The number of rows to aggregate into a segment; this number is post-aggregation rows. no (default == 5000000)
intermediatePersistPeriod ISO8601 Period The period that determines the rate at which intermediate persists occur. no (default == PT10M)
maxPendingPersists Integer 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). no (default == 0, meaning one persist can be running concurrently with ingestion, and none can be queued up)
indexSpec Object Tune how data is indexed, see 'IndexSpec' below for more details. no
reportParseExceptions Boolean If true, exceptions encountered during parsing will be thrown and will halt ingestion; if false, unparseable rows and fields will be skipped. no (default == false)
handoffConditionTimeout Long Milliseconds to wait for segment handoff. It must be >= 0, where 0 means to wait forever. This option is deprecated. Use completionTimeout of KafkaSupervisorIOConfig instead. no (default == 0)
resetOffsetAutomatically Boolean Whether to reset the consumer offset if the next offset that it is trying to fetch is less than the earliest available offset for that particular partition. The consumer offset will be reset to either the earliest or latest offset depending on useEarliestOffset property of KafkaSupervisorIOConfig (see below). This situation typically occurs when messages in Kafka are no longer available for consumption and therefore won't be ingested into Druid. If set to false then ingestion for that particular partition will halt and manual intervention is required to correct the situation, please see Reset Supervisor API below. no (default == false)
workerThreads Integer The number of threads that will be used by the supervisor for asynchronous operations. no (default == min(10, taskCount))
chatThreads Integer The number of threads that will be used for communicating with indexing tasks. no (default == min(10, taskCount * replicas))
chatRetries Integer The number of times HTTP requests to indexing tasks will be retried before considering tasks unresponsive. no (default == 8)
httpTimeout ISO8601 Period How long to wait for a HTTP response from an indexing task. no (default == PT10S)
shutdownTimeout ISO8601 Period How long to wait for the supervisor to attempt a graceful shutdown of tasks before exiting. no (default == PT80S)
offsetFetchPeriod ISO8601 Period How often the supervisor queries Kafka and the indexing tasks to fetch current offsets and calculate lag. no (default == PT30S, min == PT5S)


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)


Field Type Description Required
topic String The Kafka topic to read from. This must be a specific topic as topic patterns are not supported. yes
consumerProperties Map A map of properties to be passed to the Kafka consumer. This must contain a property bootstrap.servers with a list of Kafka brokers in the form: <BROKER_1>:<PORT_1>,<BROKER_2>:<PORT_2>,.... yes
replicas Integer The number of replica sets, where 1 means a single set of tasks (no replication). Replica tasks will always be assigned to different workers to provide resiliency against node failure. no (default == 1)
taskCount Integer The maximum number of reading tasks in a replica set. This means that the maximum number of reading tasks will be taskCount * replicas and the total number of tasks (reading + publishing) will be higher than this. See 'Capacity Planning' below for more details. The number of reading tasks will be less than taskCount if taskCount > {numKafkaPartitions}. no (default == 1)
taskDuration ISO8601 Period The length of time before tasks stop reading and begin publishing their segment. Note that segments are only pushed to deep storage and loadable by historical nodes when the indexing task completes. no (default == PT1H)
startDelay ISO8601 Period The period to wait before the supervisor starts managing tasks. no (default == PT5S)
period ISO8601 Period How often the supervisor will execute its management logic. Note that the supervisor will also run in response to certain events (such as tasks succeeding, failing, and reaching their taskDuration) so this value specifies the maximum time between iterations. no (default == PT30S)
useEarliestOffset Boolean If a supervisor is managing a dataSource for the first time, it will obtain a set of starting offsets from Kafka. This flag determines whether it retrieves the earliest or latest offsets in Kafka. Under normal circumstances, subsequent tasks will start from where the previous segments ended so this flag will only be used on first run. no (default == false)
completionTimeout ISO8601 Period The length of time to wait before declaring a publishing task as failed and terminating it. If this is set too low, your tasks may never publish. The publishing clock for a task begins roughly after taskDuration elapses. no (default == PT30M)
lateMessageRejectionPeriod ISO8601 Period Configure tasks to reject messages with timestamps earlier than this period before the task was created; for example if this is set to PT1H and the supervisor creates a task at 2016-01-01T12:00Z, messages with timestamps earlier than 2016-01-01T11:00Z will be dropped. This may help prevent concurrency issues if your data stream has late messages and you have multiple pipelines that need to operate on the same segments (e.g. a realtime and a nightly batch ingestion pipeline). no (default == none)
earlyMessageRejectionPeriod ISO8601 Period Configure tasks to reject messages with timestamps later than this period after the task was created; for example if this is set to PT1H and the supervisor creates a task at 2016-01-01T12:00Z, messages with timestamps later than 2016-01-01T13:00Z will be dropped. no (default == none)
skipOffsetGaps Boolean Whether or not to allow gaps of missing offsets in the Kafka stream. This is required for compatibility with implementations such as MapR Streams which does not guarantee consecutive offsets. If this is false, an exception will be thrown if offsets are not consecutive. no (default == false)

Supervisor API

The following endpoints are available on the Overlord:

Create Supervisor

POST /druid/indexer/v1/supervisor

Use Content-Type: application/json and provide a supervisor spec in the request body.

Calling this endpoint when there is already an existing supervisor for the same dataSource will cause:

  • The running supervisor to signal its managed tasks to stop reading and begin publishing.
  • The running supervisor to exit.
  • A new supervisor to be created using the configuration provided in the request body. This supervisor will retain the existing publishing tasks and will create new tasks starting at the offsets the publishing tasks ended on.

Seamless schema migrations can thus be achieved by simply submitting the new schema using this endpoint.

Shutdown Supervisor

POST /druid/indexer/v1/supervisor/<supervisorId>/shutdown

Note that this will cause all indexing tasks managed by this supervisor to immediately stop and begin publishing their segments.

Get Supervisor IDs

GET /druid/indexer/v1/supervisor

Returns a list of the currently active supervisors.

Get Supervisor Spec

GET /druid/indexer/v1/supervisor/<supervisorId>

Returns the current spec for the supervisor with the provided ID.

Get Supervisor Status Report

GET /druid/indexer/v1/supervisor/<supervisorId>/status

Returns a snapshot report of the current state of the tasks managed by the given supervisor. This includes the latest offsets as reported by Kafka, the consumer lag per partition, as well as the aggregate lag of all partitions. The consumer lag per partition may be reported as negative values if the supervisor has not received a recent latest offset response from Kafka. The aggregate lag value will always be >= 0.

Get All Supervisor History

GET /druid/indexer/v1/supervisor/history

Returns an audit history of specs for all supervisors (current and past).

Get Supervisor History

GET /druid/indexer/v1/supervisor/<supervisorId>/history

Returns an audit history of specs for the supervisor with the provided ID.

Reset Supervisor

POST /druid/indexer/v1/supervisor/<supervisorId>/reset

The indexing service keeps track of the latest persisted Kafka offsets in order to provide exactly-once ingestion guarantees across tasks. Subsequent tasks must start reading from where the previous task completed in order for the generated segments to be accepted. If the messages at the expected starting offsets are no longer available in Kafka (typically because the message retention period has elapsed or the topic was removed and re-created) the supervisor will refuse to start and in-flight tasks will fail.

This endpoint can be used to clear the stored offsets which will cause the supervisor to start reading from either the earliest or latest offsets in Kafka (depending on the value of useEarliestOffset). The supervisor must be running for this endpoint to be available. After the stored offsets are cleared, the supervisor will automatically kill and re-create any active tasks so that tasks begin reading from valid offsets.

Note that since the stored offsets are necessary to guarantee exactly-once ingestion, resetting them with this endpoint may cause some Kafka messages to be skipped or to be read twice.

Capacity Planning

Kafka indexing tasks run on middle managers and are thus limited by the resources available in the middle manager cluster. In particular, you should make sure that you have sufficient worker capacity (configured using the druid.worker.capacity property) to handle the configuration in the supervisor spec. Note that worker capacity is shared across all types of indexing tasks, so you should plan your worker capacity to handle your total indexing load (e.g. batch processing, realtime tasks, merging tasks, etc.). If your workers run out of capacity, Kafka indexing tasks will queue and wait for the next available worker. This may cause queries to return partial results but will not result in data loss (assuming the tasks run before Kafka purges those offsets).

A running task will normally be in one of two states: reading or publishing. A task will remain in reading state for taskDuration, at which point it will transition to publishing state. A task will remain in publishing state for as long as it takes to generate segments, push segments to deep storage, and have them be loaded and served by a historical node (or until completionTimeout elapses).

The number of reading tasks is controlled by replicas and taskCount. In general, there will be replicas * taskCount reading tasks, the exception being if taskCount > {numKafkaPartitions} in which case {numKafkaPartitions} tasks will be used instead. When taskDuration elapses, these tasks will transition to publishing state and replicas * taskCount new reading tasks will be created. Therefore to allow for reading tasks and publishing tasks to run concurrently, there should be a minimum capacity of:

workerCapacity = 2 * replicas * taskCount

This value is for the ideal situation in which there is at most one set of tasks publishing while another set is reading. In some circumstances, it is possible to have multiple sets of tasks publishing simultaneously. This would happen if the time-to-publish (generate segment, push to deep storage, loaded on historical) > taskDuration. This is a valid scenario (correctness-wise) but requires additional worker capacity to support. In general, it is a good idea to have taskDuration be large enough that the previous set of tasks finishes publishing before the current set begins.

Supervisor Persistence

When a supervisor spec is submitted via the POST /druid/indexer/v1/supervisor endpoint, it is persisted in the configured metadata database. There can only be a single supervisor per dataSource, and submitting a second spec for the same dataSource will overwrite the previous one.

When an overlord gains leadership, either by being started or as a result of another overlord failing, it will spawn a supervisor for each supervisor spec in the metadata database. The supervisor will then discover running Kafka indexing tasks and will attempt to adopt them if they are compatible with the supervisor's configuration. If they are not compatible because they have a different ingestion spec or partition allocation, the tasks will be killed and the supervisor will create a new set of tasks. In this way, the supervisors are persistent across overlord restarts and fail-overs.

A supervisor is stopped via the POST /druid/indexer/v1/supervisor/<supervisorId>/shutdown endpoint. This places a tombstone marker in the database (to prevent the supervisor from being reloaded on a restart) and then gracefully shuts down the currently running supervisor. When a supervisor is shut down in this way, it will instruct its managed tasks to stop reading and begin publishing their segments immediately. The call to the shutdown endpoint will return after all tasks have been signalled to stop but before the tasks finish publishing their segments.

Schema/Configuration Changes

Schema and configuration changes are handled by submitting the new supervisor spec via the same POST /druid/indexer/v1/supervisor endpoint used to initially create the supervisor. The overlord will initiate a graceful shutdown of the existing supervisor which will cause the tasks being managed by that supervisor to stop reading and begin publishing their segments. A new supervisor will then be started which will create a new set of tasks that will start reading from the offsets where the previous now-publishing tasks left off, but using the updated schema. In this way, configuration changes can be applied without requiring any pause in ingestion.

Deployment Notes

On the Subject of Segments

The Kafka indexing service may generate a significantly large number of segments which over time will cause query performance issues if not properly managed. One important characteristic to understand is that the Kafka indexing task will generate a Druid partition in each segment granularity interval for each partition in the Kafka topic. As an example, if you are ingesting realtime data and your segment granularity is 15 minutes with 10 partitions in the Kafka topic, you would generate a minimum of 40 segments an hour. This is a limitation imposed by the Kafka architecture which guarantees delivery order within a partition but not across partitions. Therefore as a consumer of Kafka, in order to generate segments deterministically (and be able to provide exactly-once ingestion semantics) partitions need to be handled separately.

Compounding this, if your taskDuration was also set to 15 minutes, you would actually generate 80 segments an hour since any given 15 minute interval would be handled by two tasks. For an example of this behavior, let's say we started the supervisor at 9:05 with a 15 minute segment granularity. The first task would create a segment for 9:00-9:15 and a segment for 9:15-9:30 before stopping at 9:20. A second task would be created at 9:20 which would create another segment for 9:15-9:30 and a segment for 9:30-9:45 before stopping at 9:35. Hence, if taskDuration and segmentGranularity are the same duration, you will get two tasks generating a segment for each segment granularity interval.

Understanding this behavior is the first step to managing the number of segments produced. Some recommendations for keeping the number of segments low are:

  • Keep the number of Kafka partitions to the minimum required to sustain the required throughput for your event streams.
  • Increase segment granularity and task duration so that more events are written into the same segment. One consideration here is that segments are only handed off to historical nodes after the task duration has elapsed. Since workers tend to be configured with less query-serving resources than historical nodes, query performance may suffer if tasks run excessively long without handing off segments.

In many production installations which have been ingesting events for a long period of time, these suggestions alone will not be sufficient to keep the number of segments at an optimal level. It is recommended that scheduled re-indexing tasks be run to merge segments together into new segments of an ideal size (in the range of ~500-700 MB per segment). Currently, the recommended way of doing this is by running periodic Hadoop batch ingestion jobs and using a dataSource inputSpec to read from the segments generated by the Kafka indexing tasks. Details on how to do this can be found under 'Updating Existing Data'. Note that the Merge Task and Append Task described here will not work as they require unsharded segments while Kafka indexing tasks always generated sharded segments.

There is ongoing work to support automatic segment compaction of sharded segments as well as compaction not requiring Hadoop (see here and here for related PRs).