Table of Contents

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 using Tranquility. 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 Apache Druid (incubating) extension (see Including Extensions).

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 functionality. 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 MiddleManagers. 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 DataSchema. yes
ioConfig A KafkaSupervisorIOConfig to configure the supervisor and indexing tasks, see below. yes
tuningConfig A KafkaSupervisorTuningConfig to configure the supervisor and indexing tasks, see below. no


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). Normally user does not need to set this, but depending on the nature of data, if rows are short in terms of bytes, user may not want to store a million rows in memory and this value should be set. no (default == 1000000)
maxBytesInMemory Long The number of bytes to aggregate in heap memory before persisting. This is based on a rough estimate of memory usage and not actual usage. Normally this is computed internally and user does not need to set it. The maximum heap memory usage for indexing is maxBytesInMemory * (2 + maxPendingPersists). no (default == One-sixth of max JVM memory)
maxRowsPerSegment Integer The number of rows to aggregate into a segment; this number is post-aggregation rows. Handoff will happen either if maxRowsPerSegment or maxTotalRows is hit or every intermediateHandoffPeriod, whichever happens earlier. no (default == 5000000)
maxTotalRows Long The number of rows to aggregate across all segments; this number is post-aggregation rows. Handoff will happen either if maxRowsPerSegment or maxTotalRows is hit or every intermediateHandoffPeriod, whichever happens earlier. no (default == unlimited)
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 DEPRECATED. If true, exceptions encountered during parsing will be thrown and will halt ingestion; if false, unparseable rows and fields will be skipped. Setting reportParseExceptions to true will override existing configurations for maxParseExceptions and maxSavedParseExceptions, setting maxParseExceptions to 0 and limiting maxSavedParseExceptions to no more than 1. no (default == false)
handoffConditionTimeout Long Milliseconds to wait for segment handoff. It must be >= 0, where 0 means to wait forever. 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)
segmentWriteOutMediumFactory Object Segment write-out medium to use when creating segments. See below for more information. no (not specified by default, the value from druid.peon.defaultSegmentWriteOutMediumFactory.type is used)
intermediateHandoffPeriod ISO8601 Period How often the tasks should hand off segments. Handoff will happen either if maxRowsPerSegment or maxTotalRows is hit or every intermediateHandoffPeriod, whichever happens earlier. no (default == P2147483647D)
logParseExceptions Boolean If true, log an error message when a parsing exception occurs, containing information about the row where the error occurred. no, default == false
maxParseExceptions Integer The maximum number of parse exceptions that can occur before the task halts ingestion and fails. Overridden if reportParseExceptions is set. no, unlimited default
maxSavedParseExceptions Integer When a parse exception occurs, Druid can keep track of the most recent parse exceptions. "maxSavedParseExceptions" limits how many exception instances will be saved. These saved exceptions will be made available after the task finishes in the task completion report. Overridden if reportParseExceptions is set. no, default == 0


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
type String See Additional Peon Configuration: SegmentWriteOutMediumFactory for explanation and available options. yes


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>,.... For SSL connections, the keystore, truststore and key passwords can be provided as a Password Provider or String password. yes
pollTimeout Long The length of time to wait for the kafka consumer to poll records, in milliseconds no (default == 100)
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 process 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. 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 reached its taskDuration; for example if this is set to PT1H, the taskDuration is set to PT1H and the supervisor creates a task at 2016-01-01T12:00Z, messages with timestamps later than 2016-01-01T14:00Z will be dropped. Note: Tasks sometimes run past their task duration, for example, in cases of supervisor failover. Setting earlyMessageRejectionPeriod too low may cause messages to be dropped unexpectedly whenever a task runs past its originally configured task duration. no (default == none)


This section gives descriptions of how some supervisor APIs work specifically in Kafka Indexing Service. For all supervisor APIs, please check Supervisor APIs.

Getting 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.

Getting Supervisor Ingestion Stats Report

GET /druid/indexer/v1/supervisor/<supervisorId>/stats returns a snapshot of the current ingestion row counters for each task being managed by the supervisor, along with moving averages for the row counters.

See Task Reports: Row Stats for more information.

Updating Existing Supervisors

POST /druid/indexer/v1/supervisor can be used to update existing supervisor spec. 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.

Suspending and Resuming Supervisors

You can suspend and resume a supervisor using POST /druid/indexer/v1/supervisor/<supervisorId>/suspend and POST /druid/indexer/v1/supervisor/<supervisorId>/resume, respectively.

Note that the supervisor itself will still be operating and emitting logs and metrics, it will just ensure that no indexing tasks are running until the supervisor is resumed.

Resetting Supervisors

To reset a running supervisor, you can use 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.

Terminating Supervisors

POST /druid/indexer/v1/supervisor/<supervisorId>/terminate terminates a supervisor and causes all associated indexing tasks managed by this supervisor to immediately stop and begin publishing their segments. This supervisor will still exist in the metadata store and it's history may be retrieved with the supervisor history api, but will not be listed in the 'get supervisors' api response nor can it's configuration or status report be retrieved. The only way this supervisor can start again is by submitting a functioning supervisor spec to the create api.

Capacity Planning

Kafka indexing tasks run on MiddleManagers and are thus limited by the resources available in the MiddleManager 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 process (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>/terminate 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

Each Kafka Indexing Task puts events consumed from Kafka partitions assigned to it in a single segment for each segment granular interval until maxRowsPerSegment, maxTotalRows or intermediateHandoffPeriod limit is reached, at this point a new partition for this segment granularity is created for further events. Kafka Indexing Task also does incremental hand-offs which means that all the segments created by a task will not be held up till the task duration is over. As soon as maxRowsPerSegment, maxTotalRows or intermediateHandoffPeriod limit is hit, all the segments held by the task at that point in time will be handed-off and new set of segments will be created for further events. This means that the task can run for longer durations of time without accumulating old segments locally on Middle Manager processes and it is encouraged to do so.

Kafka Indexing Service may still produce some small segments. Lets say the task duration is 4 hours, segment granularity is set to an HOUR and Supervisor was started at 9:10 then after 4 hours at 13:10, new set of tasks will be started and events for the interval 13:00 - 14:00 may be split across previous and new set of tasks. If you see it becoming a problem then one can schedule 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). Details on how to optimize the segment size can be found on Segment size optimization. There is also ongoing work to support automatic segment compaction of sharded segments as well as compaction not requiring Hadoop (see here).