Supervisor

Apache Druid uses supervisors to manage streaming ingestion from external streaming sources into Druid. Supervisors oversee the state of indexing tasks to coordinate handoffs, manage failures, and ensure that the scalability and replication requirements are maintained. They can also be used to perform automatic compaction after data has been ingested.

This topic uses the Apache Kafka term offset to refer to the identifier for records in a partition. If you are using Amazon Kinesis, the equivalent is sequence number.

Supervisor spec

Druid uses a JSON specification, often referred to as the supervisor spec, to define tasks used for streaming ingestion or auto-compaction. The supervisor spec specifies how Druid should consume, process, and index data from an external stream or Druid itself.

The following table outlines the high-level configuration options for a supervisor spec:

Property	Type	Description	Required
type	String	The supervisor type. For streaming ingestion, this can be either kafka, kinesis, or rabbit. For automatic compaction, set the type to autocompact.	Yes
spec	Object	The container object for the supervisor configuration. For automatic compaction, this is the same as the compaction configuration.	Yes
spec.dataSchema	Object	The schema for the indexing task to use during ingestion. See dataSchema for more information.	Yes
spec.ioConfig	Object	The I/O configuration object to define the connection and I/O-related settings for the supervisor and indexing tasks.	Yes
spec.tuningConfig	Object	The tuning configuration object to define performance-related settings for the supervisor and indexing tasks.	No
suspended	Boolean	Puts the supervisor in a suspended state	No

I/O configuration

The following table outlines the ioConfig configuration properties that apply to both Apache Kafka and Amazon Kinesis ingestion methods. For configuration properties specific to Kafka and Kinesis, see Kafka I/O configuration and Kinesis I/O configuration respectively.

Property	Type	Description	Required	Default
inputFormat	Object	The input format to define input data parsing.	Yes
autoScalerConfig	Object	Defines auto scaling behavior for ingestion tasks. See Task autoscaler for more information.	No	null
taskCount	Integer	The maximum number of reading tasks in a replica set. Multiply taskCount and replicas to measure the maximum number of reading tasks. The total number of tasks, reading and publishing, is higher than the maximum number of reading tasks. See Capacity planning for more details. When taskCount is greater than the number of Kafka partitions or Kinesis shards, the actual number of reading tasks is less than the taskCount value.	No	1
replicas	Integer	The number of replica sets, where 1 is a single set of tasks (no replication). Druid always assigns replicate tasks to different workers to provide resiliency against process failure.	No	1
taskDuration	ISO 8601 period	The length of time before tasks stop reading and begin publishing segments.	No	PT1H
startDelay	ISO 8601 period	The period to wait before the supervisor starts managing tasks.	No	PT5S
period	ISO 8601 period	Determines how often the supervisor executes its management logic. Note that the supervisor also runs in response to certain events, such as tasks succeeding, failing, and reaching their task duration. The period value specifies the maximum time between iterations.	No	PT30S
completionTimeout	ISO 8601 period	The length of time to wait before declaring a publishing task as failed and terminating it. If the value is too low, tasks may never publish. The publishing clock for a task begins roughly after taskDuration elapses.	No	PT30M
lateMessageRejectionStartDateTime	ISO 8601 date time	Configures tasks to reject messages with timestamps earlier than this date time. For example, if this property 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, such as a realtime and a nightly batch ingestion pipeline.	No
lateMessageRejectionPeriod	ISO 8601 period	Configures tasks to reject messages with timestamps earlier than this period before the task was created. For example, if this property is set to PT1H and the supervisor creates a task at 2016-01-01T12:00Z, Druid drops messages with timestamps earlier than 2016-01-01T11:00Z. 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, such as a streaming and a nightly batch ingestion pipeline. You can specify only one of the late message rejection properties.	No
earlyMessageRejectionPeriod	ISO 8601 period	Configures tasks to reject messages with timestamps later than this period after the task reached its task duration. For example, if this property is set to PT1H, the task duration is set to PT1H and the supervisor creates a task at 2016-01-01T12:00Z, Druid drops messages with timestamps later than 2016-01-01T14:00Z. Tasks sometimes run past their task duration, such as in cases of supervisor failover. Setting earlyMessageRejectionPeriod too low may cause Druid to drop messages unexpectedly whenever a task runs past its originally configured task duration.	No

Task autoscaler

You can optionally configure autoscaling behavior for ingestion tasks using the autoScalerConfig property of the ioConfig object.

The following table outlines the configuration properties for autoScalerConfig:

Property	Description	Required	Default
enableTaskAutoScaler	Enables the autoscaler. If not specified, Druid disables the autoscaler even when autoScalerConfig is not null.	No	false
taskCountMax	The maximum number of ingestion tasks. Must be greater than or equal to taskCountMin. If taskCountMax is greater than the number of Kafka partitions or Kinesis shards, Druid sets the maximum number of reading tasks to the number of Kafka partitions or Kinesis shards and ignores taskCountMax.	Yes
taskCountMin	The minimum number of ingestion tasks. When you enable the autoscaler, Druid ignores the value of taskCount in ioConfig and starts with the taskCountMin number of tasks to launch.	Yes
minTriggerScaleActionFrequencyMillis	The minimum time interval between two scale actions.	No	600000
autoScalerStrategy	The algorithm of autoscaler. Druid only supports the lagBased strategy. See Autoscaler strategy for more information.	No	lagBased

Autoscaler strategy

info

Unlike the Kafka indexing service, Kinesis reports lag metrics as the time difference in milliseconds between the current sequence number and the latest sequence number, rather than message count.

The following table outlines the configuration properties related to the lagBased autoscaler strategy:

Property	Description	Required	Default
lagCollectionIntervalMillis	The time period during which Druid collects lag metric points.	No	30000
lagCollectionRangeMillis	The total time window of lag collection. Use with lagCollectionIntervalMillis to specify the intervals at which to collect lag metric points.	No	600000
scaleOutThreshold	The threshold of scale out action.	No	6000000
triggerScaleOutFractionThreshold	Enables scale out action if triggerScaleOutFractionThreshold percent of lag points is higher than scaleOutThreshold.	No	0.3
scaleInThreshold	The threshold of scale in action.	No	1000000
triggerScaleInFractionThreshold	Enables scale in action if triggerScaleInFractionThreshold percent of lag points is lower than scaleOutThreshold.	No	0.9
scaleActionStartDelayMillis	The number of milliseconds to delay after the supervisor starts before the first scale logic check.	No	300000
scaleActionPeriodMillis	The frequency in milliseconds to check if a scale action is triggered.	No	60000
scaleInStep	The number of tasks to reduce at once when scaling down.	No	1
scaleOutStep	The number of tasks to add at once when scaling out.	No	2
lagAggregate	The aggregate function used to compute the lag metric for scaling decisions. Possible values are MAX, SUM and AVERAGE.	No	SUM

The following example shows a supervisor spec with lagBased autoscaler:

Click to view the example

{
  "type": "kinesis",
  "dataSchema": {
    "dataSource": "metrics-kinesis",
    "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": {
    "stream": "metrics",
    "autoScalerConfig": {
      "enableTaskAutoScaler": true,
      "taskCountMax": 6,
      "taskCountMin": 2,
      "minTriggerScaleActionFrequencyMillis": 600000,
      "autoScalerStrategy": "lagBased",
      "lagCollectionIntervalMillis": 30000,
      "lagCollectionRangeMillis": 600000,
      "scaleOutThreshold": 600000,
      "triggerScaleOutFractionThreshold": 0.3,
      "scaleInThreshold": 100000,
      "triggerScaleInFractionThreshold": 0.9,
      "scaleActionStartDelayMillis": 300000,
      "scaleActionPeriodMillis": 60000,
      "scaleInStep": 1,
      "scaleOutStep": 2
    },
    "inputFormat": {
      "type": "json"
    },
    "endpoint": "kinesis.us-east-1.amazonaws.com",
    "taskCount": 1,
    "replicas": 1,
    "taskDuration": "PT1H"
  },
  "tuningConfig": {
    "type": "kinesis",
    "maxRowsPerSegment": 5000000
  }
}

Tuning configuration

The tuningConfig object is optional. If you don't specify the tuningConfig object, Druid uses the default configuration settings.

The following table outlines the tuningConfig configuration properties that apply to both Kafka and Kinesis ingestion methods. For configuration properties specific to Kafka and Kinesis, see Kafka tuning configuration and Kinesis tuning configuration respectively.

Property	Type	Description	Required	Default
type	String	The tuning type code for the ingestion method. One of kafka or kinesis.	Yes
maxRowsInMemory	Integer	The number of rows to accumulate before persisting. This number represents the post-aggregation rows. It is not equivalent to the number of input events, but the resulting number of aggregated rows. Druid uses maxRowsInMemory to manage the required JVM heap size. The maximum heap memory usage for indexing scales is maxRowsInMemory * (2 + maxPendingPersists). Normally, you don't need to set this, but depending on the nature of data, if rows are short in terms of bytes, you may not want to store a million rows in memory and this value should be set.	No	150000
maxBytesInMemory	Long	The number of bytes to accumulate in heap memory before persisting. The value is based on a rough estimate of memory usage and not actual usage. Normally, Druid computes the value internally. The maximum heap memory usage for indexing is maxBytesInMemory * (2 + maxPendingPersists).	No	One-sixth of max JVM memory
skipBytesInMemoryOverheadCheck	Boolean	The calculation of maxBytesInMemory takes into account overhead objects created during ingestion and each intermediate persist. To exclude the bytes of these overhead objects from the maxBytesInMemory check, set skipBytesInMemoryOverheadCheck to true.	No	false
maxRowsPerSegment	Integer	The number of rows to store in a segment. This number is post-aggregation rows. Handoff occurs when maxRowsPerSegment or maxTotalRows is reached or every intermediateHandoffPeriod, whichever happens first.	No	5000000
maxTotalRows	Long	The number of rows to aggregate across all segments; this number is post-aggregation rows. Handoff happens either if maxRowsPerSegment or maxTotalRows is reached or every intermediateHandoffPeriod, whichever happens earlier.	No	20000000
intermediateHandoffPeriod	ISO 8601 period	The period that determines how often tasks hand off segments. Handoff occurs if maxRowsPerSegment or maxTotalRows is reached or every intermediateHandoffPeriod, whichever happens first.	No	P2147483647D
intermediatePersistPeriod	ISO 8601 period	The period that determines the rate at which intermediate persists occur.	No	PT10M
maxPendingPersists	Integer	Maximum number of persists that can be pending but not started. If a new intermediate persist exceeds this limit, Druid blocks ingestion until the currently running persist finishes. One persist can be running concurrently with ingestion, and none can be queued up. The maximum heap memory usage for indexing scales is maxRowsInMemory * (2 + maxPendingPersists).	No	0
indexSpec	Object	Defines segment storage format options to use at indexing time. See IndexSpec for more information.	No
indexSpecForIntermediatePersists	Object	Defines segment storage format options to use at indexing time for intermediate persisted temporary segments. You can use indexSpecForIntermediatePersists 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.	No
reportParseExceptions	Boolean	DEPRECATED. If true, Druid throws exceptions encountered during parsing causing ingestion to halt. If false, Druid skips unparseable rows and fields. Setting reportParseExceptions to true overrides existing configurations for maxParseExceptions and maxSavedParseExceptions, setting maxParseExceptions to 0 and limiting maxSavedParseExceptions to not more than 1.	No	false
handoffConditionTimeout	Long	Number of milliseconds to wait for segment handoff. Set to a value >= 0, where 0 means to wait indefinitely.	No	900000 (15 minutes) for Kafka. 0 for Kinesis.
resetOffsetAutomatically	Boolean	Resets partitions when the sequence number is unavailable. If set to true, Druid resets partitions to the earliest or latest offset, based on the value of useEarliestSequenceNumber or useEarliestOffset (earliest if true, latest if false). If set to false, Druid surfaces the exception causing tasks to fail and ingestion to halt. If this occurs, manual intervention is required to correct the situation, potentially through resetting the supervisor.	No	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	min(10, taskCount)
chatRetries	Integer	The number of times Druid retries HTTP requests to indexing tasks before considering tasks unresponsive.	No	8
httpTimeout	ISO 8601 period	The period of time to wait for a HTTP response from an indexing task.	No	PT10S
shutdownTimeout	ISO 8601 period	The period of time to wait for the supervisor to attempt a graceful shutdown of tasks before exiting.	No	PT80S
offsetFetchPeriod	ISO 8601 period	Determines how often the supervisor queries the streaming source and the indexing tasks to fetch current offsets and calculate lag. If the user-specified value is below the minimum value of PT5S, the supervisor ignores the value and uses the minimum value instead.	No	PT30S
segmentWriteOutMediumFactory	Object	The segment write-out medium to use when creating segments. See Additional Peon configuration: SegmentWriteOutMediumFactory for explanation and available options.	No	If not specified, Druid uses the value from druid.peon.defaultSegmentWriteOutMediumFactory.type.
logParseExceptions	Boolean	If true, Druid logs an error message when a parsing exception occurs, containing information about the row where the error occurred.	No	false
maxParseExceptions	Integer	The maximum number of parse exceptions that can occur before the task halts ingestion and fails. Setting reportParseExceptions overrides this limit.	No	unlimited
maxSavedParseExceptions	Integer	When a parse exception occurs, Druid keeps track of the most recent parse exceptions. maxSavedParseExceptions limits the number of saved exception instances. These saved exceptions are available after the task finishes in the task completion report. Setting reportParseExceptions overrides this limit.	No	0

Start a supervisor

Druid starts a new supervisor when you submit a supervisor spec. You can submit the supervisor spec in the Druid web console data loader or with the Supervisor API.

The following screenshot shows the Supervisors view of the web console for a cluster with two supervisors:

Once started, the supervisor persists in the configured metadata database. There can only be one supervisor per datasource. Submitting a second supervisor spec for the same datasource overwrites the previous one.

When an Overlord gains leadership, either by being started or as a result of another Overlord failing, it spawns a supervisor for each supervisor spec in the metadata database. The supervisor then discovers running indexing tasks and attempts to adopt them if they are compatible with the supervisor's configuration. If they are not compatible, the tasks are terminated and the supervisor creates a new set of tasks. This way, the supervisor ingestion tasks persist across Overlord restarts and failovers.

Schema and configuration changes

To make schema or configuration changes, you must submit a new supervisor spec. The Overlord initiates a graceful shutdown of the existing supervisor. The running supervisor signals its tasks to stop reading and begin publishing, exiting itself. Druid then uses the new configuration to create a new supervisor. Druid submits the updated schema while retaining existing publishing tasks. It also starts new tasks at the previous task offsets. This way, configuration changes can be applied without requiring any pause in ingestion.

Status report

The supervisor status report contains the state of the supervisor tasks and an array of recently thrown exceptions reported as recentErrors. You can control the maximum size of the exceptions using the druid.supervisor.maxStoredExceptionEvents configuration.

To view the supervisor status in the web console, navigate to the Supervisors view and click the supervisor ID to open the Supervisor dialog. Click Status in the left navigation pane to display the status:

The following example shows the status of a supervisor with the name social_media:

Click to view the example

{
  "dataSource": "social_media",
  "stream": "social_media",
  "partitions": 1,
  "replicas": 1,
  "durationSeconds": 3600,
  "activeTasks": [
    {
      "id": "index_kafka_social_media_8ff3096f21fe448_jajnddno",
      "startingOffsets": {
        "0": 0
      },
      "startTime": "2024-01-30T21:21:41.696Z",
      "remainingSeconds": 479,
      "type": "ACTIVE",
      "currentOffsets": {
        "0": 50000
      },
      "lag": {
        "0": 0
      }
    }
  ],
  "publishingTasks": [],
  "latestOffsets": {
    "0": 50000
  },
  "minimumLag": {
    "0": 0
  },
  "aggregateLag": 0,
  "offsetsLastUpdated": "2024-01-30T22:13:19.335Z",
  "suspended": false,
  "healthy": true,
  "state": "RUNNING",
  "detailedState": "RUNNING",
  "recentErrors": []
}

The status report contains two properties that correspond to the state of the supervisor: state and detailedState. The state property contains a small number of generic states that apply to any type of supervisor. The detailedState property contains a more descriptive, implementation-specific state that may provide more insight into the supervisor's activities.

Possible state values are PENDING, RUNNING, SUSPENDED, STOPPING, UNHEALTHY_SUPERVISOR, and UNHEALTHY_TASKS.

The following table lists detailedState values and their corresponding state mapping:

detailedState	state	Description
UNHEALTHY_SUPERVISOR	UNHEALTHY_SUPERVISOR	The supervisor encountered errors on previous druid.supervisor.unhealthinessThreshold iterations.
UNHEALTHY_TASKS	UNHEALTHY_TASKS	The last druid.supervisor.taskUnhealthinessThreshold tasks all failed.
UNABLE_TO_CONNECT_TO_STREAM	UNHEALTHY_SUPERVISOR	The supervisor is encountering connectivity issues with the stream and hasn't successfully connected in the past.
LOST_CONTACT_WITH_STREAM	UNHEALTHY_SUPERVISOR	The supervisor is encountering connectivity issues with the stream but has successfully connected in the past.
PENDING (first iteration only)	PENDING	The supervisor has been initialized but 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.
IDLE	IDLE	The supervisor is not creating tasks since the input stream has not received any new data and all the existing data is read.
SUSPENDED	SUSPENDED	The supervisor is 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. Retrieve the list of partitions and determine the starting offset for each partition. If continuing, Druid uses the last processed offset. For new streams, Druid starts from either the beginning or end of the stream, depending on the useEarliestOffset property.
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 the view of the state of the tasks under supervision.
4. Handle tasks that have exceeded taskDuration and should transition from reading to publishing.
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 property shows additional values (marked with "first iteration only" in the preceding table) 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. For example, if the supervisor can't connect to the stream, if it's unable to read from the stream, or if 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 failure threshold and transitions to an unhealthy state.

info

For the Kafka indexing service, Druid may report the consumer lag per partition as a negative value if the supervisor hasn't received the latest offset response from Kafka. The aggregate lag value is always >= 0.

SUPERVISORS system table

Druid exposes system information through special system schemas. You can query the sys.supervisors table to retrieve information about the supervisor internals. The following example shows how to retrieve supervisor tasks information filtered by health status:

SELECT * FROM sys.supervisors WHERE healthy=0;

For more information on the supervisors system table, see SUPERVISORS table.

Manage a supervisor

You can manage a supervisor from the web console or with the Supervisor API. In the web console, navigate to the Supervisors view and click the ellipsis in the Actions column. Select the desired action from the menu that appears.

The supervisor must be running for some of these actions to be available.

Suspend

Suspend pauses a running supervisor. The suspended supervisor continues to emit logs and metrics. Indexing tasks remain suspended until you resume the supervisor. For information on how to suspend a supervisor by API, see Supervisors: Suspend a running supervisor.

Set offsets

info

Perform this action with caution as it may result in skipped messages and lead to data loss or duplicate data.

Set offsets resets the offsets for supervisor partitions. This action clears the stored offsets and instructs the supervisor to resume reading data from the specified offsets. If there are no stored offsets, Druid saves the specified offsets in the metadata store. Set offsets terminates and recreates active tasks for the specified partitions to begin reading from the reset offsets. For partitions not specified in this operation, the supervisor resumes from the last stored offset.

For information on how to reset offsets by API, see Supervisors: Reset offsets for a supervisor.

Hard reset

info

Perform this action with caution as it may result in skipped messages and lead to data loss or duplicate data.

Hard reset clears supervisor metadata, causing the supervisor to resume data reading from either the earliest or latest available position, depending on the useEarliestOffset setting. Hard reset terminates and recreates active tasks, so that tasks begin reading from valid positions.

Use this action to recover from a stopped state due to missing offsets.

For information on how to reset a supervisor by API, see Supervisors: Reset a supervisor.

Terminate

Terminate stops a supervisor and its indexing tasks, triggering the publishing of their segments. When you terminate a supervisor, Druid places a tombstone marker in the metadata store to prevent reloading on restart. The terminated supervisor still exists in the metadata store and its history can be retrieved.

For information on how to terminate a supervisor by API, see Supervisors: Terminate a supervisor.

Capacity planning

Indexing tasks run on Middle Managers and are 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, such as batch processing, streaming tasks, and merging tasks. If your workers run out of capacity, indexing tasks 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 the stream purges those sequence numbers.

A running task can be in one of two states: reading or publishing. A task remains in reading state for the period defined in taskDuration, at which point it transitions to publishing state. A task remains in publishing state for as long as it takes to generate segments, push segments to deep storage, and have them loaded and served by a Historical service or until completionTimeout elapses.

The number of reading tasks is controlled by replicas and taskCount. In general, there are replicas * taskCount reading tasks. An exception occurs if taskCount is over the number of shards in Kinesis or partitions in Kafka, in which case Druid uses the number of shards or partitions. When taskDuration elapses, these tasks transition to publishing state and replicas * taskCount new reading tasks are created. 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, load on Historical) is greater than taskDuration. This is a valid and correct scenario 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.

Learn more

See the following topics for more information:

• Supervisor API for how to manage and monitor supervisors using the API.
• Apache Kafka ingestion to learn about ingesting data from an Apache Kafka stream.
• Amazon Kinesis ingestion to learn about ingesting data from an Amazon Kinesis stream.