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kafka-ingestion Apache Kafka ingestion Apache Kafka

When you enable the Kafka indexing service, you can configure supervisors on the Overlord to manage the creation and lifetime of Kafka indexing tasks. These indexing tasks read events using Kafka's own partition and offset mechanism to guarantee exactly-once ingestion. The supervisor oversees the state of the indexing tasks to:

  • coordinate handoffs
  • manage failures
  • ensure that scalability and replication requirements are maintained.

To use the Kafka indexing service, load the druid-kafka-indexing-service core Apache Druid extension. See Including Extensions).

This topic covers the ingestion spec for Kafka. For a general ingestionSpec reference, see Ingestion specs. For a walk-through, check out the Loading from Apache Kafka tutorial.

Kafka support

The Kafka indexing service supports transactional topics introduced in Kafka 0.11.x by default. The consumer for Kafka indexing service is incompatible with older Kafka brokers. If you are using an older version, refer to the Kafka upgrade guide.

Additionally, you can set isolation.level to read_uncommitted in consumerProperties if either:

  • You don't need Druid to consume transactional topics.
  • You need Druid to consume older versions of Kafka. Make sure offsets are sequential, since there is no offset gap check in Druid anymore.

If your Kafka cluster enables consumer-group based ACLs, you can set group.id in consumerProperties to override the default auto generated group id.

Submitting a Supervisor Spec

To use the Kafka indexing service, load the druid-kafka-indexing-service extension on both the Overlord and the MiddleManagers. Druid starts a supervisor for a dataSource when you submit a supervisor spec. You can use the following endpoint:

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

Where the file supervisor-spec.json contains a Kafka supervisor spec:

{
  "type": "kafka",
  "spec": {
    "dataSchema": {
      "dataSource": "metrics-kafka",
      "timestampSpec": {
        "column": "timestamp",
        "format": "auto"
      },
      "dimensionsSpec": {
        "dimensions": [],
        "dimensionExclusions": [
          "timestamp",
          "value"
         ]
       },
      "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"
      }
    },
    "ioConfig": {
      "topic": "metrics",
      "inputFormat": {
        "type": "json"
      },
      "consumerProperties": {
        "bootstrap.servers": "localhost:9092"
      },
      "taskCount": 1,
      "replicas": 1,
      "taskDuration": "PT1H"
    },
    "tuningConfig": {
      "type": "kafka",
      "maxRowsPerSegment": 5000000
    }
  } 
}

Supervisor Configuration

Field Description Required
type Supervisor type. For Kafka streaming, set to kafka. yes
spec Container object for the supervisor configuration. yes
dataSchema Schema for the Kafka indexing task to use during ingestion. yes
ioConfig A KafkaSupervisorIOConfig object to define the Kafka connection and I/O-related settings for the supervisor and indexing task. See KafkaSupervisorIOConfig. yes
tuningConfig A KafkaSupervisorTuningConfig object to define performance-related settings for the supervisor and indexing tasks. See KafkaSupervisorTuningConfig. no

KafkaSupervisorIOConfig

Field Type Description Required
topic String The Kafka topic to read from. Must be a specific topic. Topic patterns are not supported. yes
inputFormat Object inputFormat to define input data parsing. See Specifying data format for details about specifying the input format. yes
consumerProperties Map<String, Object> A map of properties to pass to the Kafka consumer. See More on consumer properties. 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. "1" means a single set of tasks without replication. Druid always assigns replica tasks to different workers to provide resiliency against worker failure. no (default == 1)
taskCount Integer The maximum number of reading tasks in a replica set. The maximum number of reading tasks equals taskCount * replicas. Therefore, the total number of tasks, reading + publishing, is greater than this count. See Capacity Planning for more details. When taskCount > {numKafkaPartitions}, the actual number of reading tasks is less than the taskCount value. no (default == 1)
taskDuration ISO8601 Period The length of time before tasks stop reading and begin publishing segments. no (default == PT1H)
startDelay ISO8601 Period The period to wait before the supervisor starts managing tasks. no (default == PT5S)
period ISO8601 Period Frequency at which the supervisor executes its management logic. The supervisor also runs in response to certain events. For example task success, task failure, and tasks reaching their taskDuration. The period value specifies the maximum time between iterations. no (default == PT30S)
useEarliestOffset Boolean If a supervisor manages a dataSource for the first time, it obtains 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. Therefore Druid only uses useEarliestOffset 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 the value is too low, your tasks may never publish. The publishing clock for a task begins roughly after taskDuration elapses. no (default == PT30M)
lateMessageRejectionStartDateTime ISO8601 DateTime Configure tasks to reject messages with timestamps earlier than this date time; for example if this is set to 2016-01-01T11:00Z and the supervisor creates a task at 2016-01-01T12:00Z, Druid drops messages with timestamps earlier than 2016-01-01T11:00Z. This can 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)
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). Please note that only one of lateMessageRejectionPeriod or lateMessageRejectionStartDateTime can be specified. 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)
autoScalerConfig Object autoScalerConfig to specify how to auto scale the number of Kafka ingest tasks. ONLY supported for Kafka indexing as of now. See [Tasks Autoscaler Properties](#Task Autoscaler Properties) for details. no (default == null)

Task Autoscaler Properties

Note that Task AutoScaler is currently designated as experimental.

Property Description Required
enableTaskAutoScaler Enable or disable autoscaling. false or blank disables the autoScaler even when autoScalerConfig is not null no (default == false)
taskCountMax Maximum number of ingestion tasks. Set taskCountMax >= taskCountMin. If taskCountMax > {numKafkaPartitions}, Druid only scales reading tasks up to the {numKafkaPartitions}. In this case taskCountMax is ignored. yes
taskCountMin Minimum number of ingestion tasks. When you enable autoscaler, Druid ignores the value of taskCount in IOConfig and starts with the taskCountMin number of tasks. yes
minTriggerScaleActionFrequencyMillis Minimum time interval between two scale actions. no (default == 600000)
autoScalerStrategy The algorithm of autoScaler. Only supports lagBased. See [Lag Based AutoScaler Strategy Related Properties](#Lag Based AutoScaler Strategy Related Properties) for details. no (default == lagBased)

Lag Based AutoScaler Strategy Related Properties

Property Description Required
lagCollectionIntervalMillis Period of lag points collection. no (default == 30000)
lagCollectionRangeMillis The total time window of lag collection. Use with lagCollectionIntervalMillis，it means that in the recent lagCollectionRangeMillis, collect lag metric points every lagCollectionIntervalMillis. no (default == 600000)
scaleOutThreshold The threshold of scale out action no (default == 6000000)
triggerScaleOutFractionThreshold If triggerScaleOutFractionThreshold percent of lag points are higher than scaleOutThreshold, then do scale out action. no (default == 0.3)
scaleInThreshold The Threshold of scale in action no (default == 1000000)
triggerScaleInFractionThreshold If triggerScaleInFractionThreshold percent of lag points are lower than scaleOutThreshold, then do scale in action. no (default == 0.9)
scaleActionStartDelayMillis Number of milliseconds after supervisor starts when first check scale logic. no (default == 300000)
scaleActionPeriodMillis The frequency of checking whether to do scale action in millis no (default == 60000)
scaleInStep How many tasks to reduce at a time no (default == 1)
scaleOutStep How many tasks to add at a time no (default == 2)

The following example demonstrates supervisor spec with lagBased autoScaler enabled:

{
    "type": "kafka",
    "spec": {
      "dataSchema": {
        ...
      },
      "ioConfig": {
         "topic": "metrics",
         "inputFormat": {
             "type": "json"
         },
          "consumerProperties": {
             "bootstrap.servers": "localhost:9092"
          },
         "autoScalerConfig": {
             "enableTaskAutoScaler": true,
              "taskCountMax": 6,
              "taskCountMin": 2,
             "minTriggerScaleActionFrequencyMillis": 600000,
             "autoScalerStrategy": "lagBased",
              "lagCollectionIntervalMillis": 30000,
              "lagCollectionRangeMillis": 600000,
              "scaleOutThreshold": 6000000,
              "triggerScaleOutFractionThreshold": 0.3,
             "scaleInThreshold": 1000000,
             "triggerScaleInFractionThreshold": 0.9,
              "scaleActionStartDelayMillis": 300000,
              "scaleActionPeriodMillis": 60000,
              "scaleInStep": 1,
             "scaleOutStep": 2
         },
         "taskCount":1,
         "replicas":1,
         "taskDuration":"PT1H"
      },
     "tuningConfig":{
        ...
     }
    }
}

More on consumerProperties

This must contain a property bootstrap.servers with a list of Kafka brokers in the form: <BROKER_1>:<PORT_1>,<BROKER_2>:<PORT_2>,.... By default, isolation.level is set to read_committed. It should be set to read_uncommitted if you don't want Druid to consume only committed transactions or working with older versions of Kafka servers with no Transactions support.

There are few cases that require fetching few/all of consumer properties at runtime e.g. when bootstrap.servers is not known upfront or not static, to enable SSL connections users might have to provide passwords for keystore, truststore and key secretly. For such consumer properties, user can implement a DynamicConfigProvider to supply them at runtime, by adding druid.dynamic.config.provider={"type": "<registered_dynamic_config_provider_name>", ...} in consumerProperties map.

Note: SSL connections may also be supplied using the deprecated Password Provider interface to define the keystore, truststore, and key. This functionality might be removed in a future release.

Specifying data format

Kafka indexing service supports both inputFormat and parser to specify the data format. Use the inputFormat to specify the data format for Kafka indexing service unless you need a format only supported by the legacy parser.

Supported inputFormats include:

  • csv
  • delimited
  • json
  • avro_stream
  • protobuf

For more information, see Data formats. You can also read thrift formats using parser.

KafkaSupervisorTuningConfig

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 for more information. no
indexSpecForIntermediatePersists Defines segment storage format options to be used at indexing time for intermediate persisted temporary segments. This can be used to disable dimension/metric compression on intermediate segments to reduce memory required for final merging. However, disabling compression on intermediate segments might increase page cache use while they are used before getting merged into final segment published, see IndexSpec for possible values. no (default = same as indexSpec)
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 Controls behavior when Druid needs to read Kafka messages that are no longer available (i.e. when OffsetOutOfRangeException is encountered).

If false, the exception will bubble up, which will cause your tasks to fail and ingestion to halt. If this occurs, manual intervention is required to correct the situation; potentially using the Reset Supervisor API. This mode is useful for production, since it will make you aware of issues with ingestion.

If true, Druid will automatically reset to the earlier or latest offset available in Kafka, based on the value of the useEarliestOffset property (earliest if true, latest if false). Please note that this can lead to data being DROPPED (if useEarliestOffset is false) or DUPLICATED (if useEarliestOffset is true) without your knowledge. Messages will be logged indicating that a reset has occurred, but ingestion will continue. This mode is useful for non-production situations, since it will make Druid attempt to recover from problems automatically, even if they lead to quiet dropping or duplicating of data.

This feature behaves similarly to the Kafka auto.offset.reset consumer property.		 no (default == false)
workerThreads Integer The number of threads that the supervisor uses to handle requests/responses for worker tasks, along with any other internal asynchronous operation. 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. If the user-specified value is below the minimum value (PT5S), the supervisor ignores the value and uses the minimum value instead. 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

IndexSpec

Field Type Description Required
bitmap Object Compression format for bitmap indexes. Should be a JSON object. See Bitmap types below for options. no (defaults to Roaring)
dimensionCompression String Compression format for dimension columns. Choose from LZ4, LZF, or uncompressed. no (default == LZ4)
metricCompression String Compression format for primitive type 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 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)

For Concise bitmaps:

Field Type Description Required
type String Must be concise. yes

SegmentWriteOutMediumFactory

Field Type Description Required
type String See Additional Peon Configuration: SegmentWriteOutMediumFactory for explanation and available options. yes

Operations

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.

The status report also contains the supervisor's state and a list of recently thrown exceptions (reported as recentErrors, whose max size can be controlled using the druid.supervisor.maxStoredExceptionEvents configuration). There are two fields related to the supervisor's state - state and detailedState. The state field will always be one of a small number of generic states that are applicable to any type of supervisor, while the detailedState field will contain a more descriptive, implementation-specific state that may provide more insight into the supervisor's activities than the generic state field.

The list of possible state values are: [PENDING, RUNNING, SUSPENDED, STOPPING, UNHEALTHY_SUPERVISOR, UNHEALTHY_TASKS]

The list of detailedState values and their corresponding state mapping is as follows:

Detailed State Corresponding State Description
UNHEALTHY_SUPERVISOR UNHEALTHY_SUPERVISOR The supervisor has encountered errors on the past druid.supervisor.unhealthinessThreshold iterations
UNHEALTHY_TASKS UNHEALTHY_TASKS The last druid.supervisor.taskUnhealthinessThreshold tasks have all failed
UNABLE_TO_CONNECT_TO_STREAM UNHEALTHY_SUPERVISOR The supervisor is encountering connectivity issues with Kafka and has not successfully connected in the past
LOST_CONTACT_WITH_STREAM UNHEALTHY_SUPERVISOR The supervisor is encountering connectivity issues with Kafka but has successfully connected in the past
PENDING (first iteration only) PENDING The supervisor has been initialized and hasn't started connecting to the stream
CONNECTING_TO_STREAM (first iteration only) RUNNING The supervisor is trying to connect to the stream and update partition data
DISCOVERING_INITIAL_TASKS (first iteration only) RUNNING The supervisor is discovering already-running tasks
CREATING_TASKS (first iteration only) RUNNING The supervisor is creating tasks and discovering state
RUNNING RUNNING The supervisor has started tasks and is waiting for taskDuration to elapse
SUSPENDED SUSPENDED The supervisor has been suspended
STOPPING STOPPING The supervisor is stopping

On each iteration of the supervisor's run loop, the supervisor completes the following tasks in sequence:

  1. Fetch the list of partitions from Kafka and determine the starting offset for each partition (either based on the last processed offset if continuing, or starting from the beginning or ending of the stream if this is a new topic).
  2. Discover any running indexing tasks that are writing to the supervisor's datasource and adopt them if they match the supervisor's configuration, else signal them to stop.
  3. Send a status request to each supervised task to update our view of the state of the tasks under our supervision.
  4. Handle tasks that have exceeded taskDuration and should transition from the reading to publishing state.
  5. Handle tasks that have finished publishing and signal redundant replica tasks to stop.
  6. Handle tasks that have failed and clean up the supervisor's internal state.
  7. Compare the list of healthy tasks to the requested taskCount and replicas configurations and create additional tasks if required.

The detailedState field will show additional values (those marked with "first iteration only") the first time the supervisor executes this run loop after startup or after resuming from a suspension. This is intended to surface initialization-type issues, where the supervisor is unable to reach a stable state (perhaps because it can't connect to Kafka, it can't read from the Kafka topic, or it can't communicate with existing tasks). Once the supervisor is stable - that is, once it has completed a full execution without encountering any issues - detailedState will show a RUNNING state until it is stopped, suspended, or hits a task failure threshold and transitions to an unhealthy state.

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.

Supervisor Health Check

GET /druid/indexer/v1/supervisor/<supervisorId>/health returns 200 OK if the supervisor is healthy and 503 Service Unavailable if it is unhealthy. Healthiness is determined by the supervisor's state (as returned by the /status endpoint) and the druid.supervisor.* Overlord configuration thresholds.

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

The POST /druid/indexer/v1/supervisor/<supervisorId>/reset operation clears stored offsets, causing the supervisor to start reading offsets from either the earliest or latest offsets in Kafka (depending on the value of useEarliestOffset). After clearing stored offsets, the supervisor kills and recreates any active tasks, so that tasks begin reading from valid offsets.

Use care when using this operation! Resetting the supervisor may cause Kafka messages to be skipped or read twice, resulting in missing or duplicate data.

The reason for using this operation is to recover from a state in which the supervisor ceases operating due to missing offsets. 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 operation enables you to recover from this condition.

Note that the supervisor must be running for this endpoint to be available.

Terminating Supervisors

The POST /druid/indexer/v1/supervisor/<supervisorId>/terminate operation 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 signaled 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 Kafka partitions and Druid segments

Druid assigns each Kafka indexing task Kafka partitions. A task writes the events it consumes from Kafka into a single segment for the segment granularity interval until it reaches one of the following: maxRowsPerSegment, maxTotalRows or intermediateHandoffPeriod limit. At this point, the task creates a new partition for this segment granularity to contain subsequent events.

The Kafka Indexing Task also does incremental hand-offs. Therefore segments become available as they are ready and you do not have to wait for all segments until the end of the task duration. When the task reaches one of maxRowsPerSegment, maxTotalRows, or intermediateHandoffPeriod, it hands off all the segments and creates a new new set of segments will be created for further events. This allows the task to run for longer durations without accumulating old segments locally on Middle Manager processes.

The Kafka Indexing Service may still produce some small segments. For example, consider the following scenario:

  • Task duration is 4 hours
  • Segment granularity is set to an HOUR
  • The supervisor was started at 9:10 After 4 hours at 13:10, Druid starts a new set of tasks. The events for the interval 13:00 - 14:00 may be split across existing tasks and the new set of tasks which could result in small segments. To merge them together into new segments of an ideal size (in the range of ~500-700 MB per segment), you can schedule re-indexing tasks, optionally with a different segment granularity.

For more detail, see 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).