Skip to main content

Apache Kafka supervisor reference

This topic contains configuration reference information for the Apache Kafka supervisor for Apache Druid.

The following table outlines the high-level configuration options:

typeStringThe supervisor type. For Kafka streaming, set to kafka.Yes
specObjectThe container object for the supervisor configuration.Yes
ioConfigObjectThe I/O configuration object to define the Kafka connection and I/O-related settings for the supervisor and indexing task. See Supervisor I/O configuration.Yes
dataSchemaObjectThe schema for the Kafka indexing task to use during ingestion.Yes
tuningConfigObjectThe tuning configuration object to define performance-related settings for the supervisor and indexing tasks. See Supervisor tuning configuration.No

Supervisor I/O configuration

The following table outlines the configuration options for ioConfig:

topicStringThe Kafka topic to read from. Must be a specific topic. Druid does not support topic patterns.Yes
inputFormatObjectThe input format to define input data parsing. See Specifying data format for details about specifying the input format.Yes
consumerPropertiesString, ObjectA map of properties to pass to the Kafka consumer. See Consumer properties.Yes
pollTimeoutLongThe length of time to wait for the Kafka consumer to poll records, in milliseconds.No100
replicasIntegerThe 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.No1
taskCountIntegerThe maximum number of reading tasks in a replica set. The maximum number of reading tasks equals taskCount * replicas. The total number of tasks, reading and 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.No1
taskDurationISO 8601 periodThe length of time before tasks stop reading and begin publishing segments.NoPT1H
startDelayISO 8601 periodThe period to wait before the supervisor starts managing tasks.NoPT5S
periodISO 8601 periodDetermines 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.NoPT30S
useEarliestOffsetBooleanIf 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 start from where the previous segments ended. Druid only uses useEarliestOffset on the first run.Nofalse
completionTimeoutISO 8601 periodThe 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.NoPT30M
lateMessageRejectionStartDateTimeISO 8601 date timeConfigure 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
lateMessageRejectionPeriodISO 8601 periodConfigure 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 realtime and a nightly batch ingestion pipeline. Note that you can specify only one of the late message rejection properties.No
earlyMessageRejectionPeriodISO 8601 periodConfigure 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
autoScalerConfigObjectDefines auto scaling behavior for Kafka ingest tasks. See Task autoscaler properties.Nonull
idleConfigObjectDefines how and when the Kafka supervisor can become idle. See Idle supervisor configuration for more details.Nonull

Task autoscaler properties

The following table outlines the configuration options for autoScalerConfig:

enableTaskAutoScalerEnable or disable autoscaling. false or blank disables the autoScaler even when autoScalerConfig is not null.Nofalse
taskCountMaxMaximum 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
taskCountMinMinimum 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.Yes
minTriggerScaleActionFrequencyMillisMinimum time interval between two scale actions.No600000
autoScalerStrategyThe algorithm of autoScaler. Only supports lagBased. See Lag based autoscaler strategy related properties for details.NolagBased

The following table outlines the configuration options for autoScalerStrategy:

lagCollectionIntervalMillisThe time period during which Druid collects lag metric points.No30000
lagCollectionRangeMillisThe total time window of lag collection. Use with lagCollectionIntervalMillis to specify the intervals at which to collect lag metric points.No600000
scaleOutThresholdThe threshold of scale out action.No6000000
triggerScaleOutFractionThresholdEnables scale out action if triggerScaleOutFractionThreshold percent of lag points is higher than scaleOutThreshold.No0.3
scaleInThresholdThe threshold of scale in action.No1000000
triggerScaleInFractionThresholdEnables scale in action if triggerScaleInFractionThreshold percent of lag points is lower than scaleOutThreshold.No0.9
scaleActionStartDelayMillisThe number of milliseconds to delay after the supervisor starts before the first scale logic check.No300000
scaleActionPeriodMillisThe frequency in milliseconds to check if a scale action is triggered.No60000
scaleInStepThe number of tasks to reduce at once when scaling down.No1
scaleOutStepThe number of tasks to add at once when scaling out.No2

Ingesting from multiple topics

To ingest data from multiple topics, you have to set topicPattern in the supervisor I/O configuration and not set topic. You can pass multiple topics as a regex pattern as the value for topicPattern in the I/O configuration. For example, to ingest data from clicks and impressions, set topicPattern to clicks|impressions in the I/O configuration. Similarly, you can use metrics-.* as the value for topicPattern if you want to ingest from all the topics that start with metrics-. If new topics are added to the cluster that match the regex, Druid automatically starts ingesting from those new topics. A topic name that only matches partially such as my-metrics-12 will not be included for ingestion. If you enable multi-topic ingestion for a datasource, downgrading to a version older than 28.0.0 will cause the ingestion for that datasource to fail.

When ingesting data from multiple topics, partitions are assigned based on the hashcode of the topic name and the id of the partition within that topic. The partition assignment might not be uniform across all the tasks. It's also assumed that partitions across individual topics have similar load. It is recommended that you have a higher number of partitions for a high load topic and a lower number of partitions for a low load topic. Assuming that you want to ingest from both high and low load topic in the same supervisor.

Idle supervisor configuration


Note that idle state transitioning is currently designated as experimental.

enabledIf true, the supervisor becomes idle if there is no data on input stream/topic for some time.No
inactiveAfterMillisThe supervisor becomes idle if all existing data has been read from input topic and no new data has been published for inactiveAfterMillis milliseconds.No

When the supervisor enters the idle state, no new tasks are launched subsequent to the completion of the currently executing tasks. This strategy may lead to reduced costs for cluster operators while using topics that get sporadic data.

The following example demonstrates supervisor spec with lagBased autoscaler and idle configuration 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
"idleConfig": {
"enabled": true,
"inactiveAfterMillis": 600000

Consumer properties

Consumer properties 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. If you use older versions of Kafka servers without transactions support or don't want Druid to consume only committed transactions, set isolation.level to read_uncommitted.

In some cases, you may need to fetch consumer properties at runtime. For example, when bootstrap.servers is not known upfront, or is not static. To enable SSL connections, you must provide passwords for keystore, truststore and key secretly. You can provide configurations at runtime with a dynamic config provider implementation like the environment variable config provider that comes with Druid. For more information, see Dynamic config provider.

For example, if you are using SASL and SSL with Kafka, set the following environment variables for the Druid user on the machines running the Overlord and the Peon services:

export KAFKA_JAAS_CONFIG=" required username='admin_user' password='admin_password';"
export SSL_KEY_PASSWORD=mysecretkeypassword
export SSL_KEYSTORE_PASSWORD=mysecretkeystorepassword
export SSL_TRUSTSTORE_PASSWORD=mysecrettruststorepassword
        "druid.dynamic.config.provider": {
"type": "environment",
"variables": {
"sasl.jaas.config": "KAFKA_JAAS_CONFIG",
"ssl.key.password": "SSL_KEY_PASSWORD",
"ssl.keystore.password": "SSL_KEYSTORE_PASSWORD",
"ssl.truststore.password": "SSL_TRUSTSTORE_PASSWORD"

Verify that you've changed the values for all configurations to match your own environment. You can use the environment variable config provider syntax in the Consumer properties field on the Connect tab in the Load Data UI in the web console. When connecting to Kafka, Druid replaces the environment variables with their corresponding values.

You can provide SSL connections with Password provider interface to define the keystore, truststore, and key, but this feature is deprecated.

Specifying data format

The 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.

Druid supports the following input formats:

  • csv
  • tsv
  • json
  • kafka
  • avro_stream
  • avro_ocf
  • protobuf

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

Supervisor tuning configuration

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

typeStringThe indexing task type. This should always be kafka.Yes
maxRowsInMemoryIntegerThe number of rows to aggregate 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 do not 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.No150000
maxBytesInMemoryLongThe 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. The maximum heap memory usage for indexing is maxBytesInMemory * (2 + maxPendingPersists).NoOne-sixth of max JVM memory
skipBytesInMemoryOverheadCheckBooleanThe 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.Nofalse
maxRowsPerSegmentIntegerThe 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.No5000000
maxTotalRowsLongThe 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.No20000000
intermediateHandoffPeriodISO 8601 periodThe period that determines how often tasks hand off segments. Handoff occurs if maxRowsPerSegment or maxTotalRows is reached or every intermediateHandoffPeriod, whichever happens first.NoP2147483647D
intermediatePersistPeriodISO 8601 periodThe period that determines the rate at which intermediate persists occur.NoPT10M
maxPendingPersistsIntegerMaximum 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).No0
indexSpecObjectDefines how Druid indexes the data. See IndexSpec for more information.No
indexSpecForIntermediatePersistsObjectDefines 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. See IndexSpec for possible values.NoSame as indexSpec
reportParseExceptionsBooleanDEPRECATED. 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.Nofalse
handoffConditionTimeoutLongNumber of milliseconds to wait for segment handoff. Set to a value >= 0, where 0 means to wait indefinitely.No900000 (15 minutes)
resetOffsetAutomaticallyBooleanControls behavior when Druid needs to read Kafka messages that are no longer available, when offsetOutOfRangeException is encountered.
If false, the exception bubbles up causing 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). Note that this can lead to dropping data (if useEarliestSequenceNumber is false) or duplicating data (if useEarliestSequenceNumber is true) without your knowledge. Druid logs messages indicating that a reset has occurred without interrupting ingestion. This mode is useful for non-production situations since it enables Druid 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.
workerThreadsIntegerThe number of threads that the supervisor uses to handle requests/responses for worker tasks, along with any other internal asynchronous operation.Nomin(10, taskCount)
chatAsyncBooleanIf true, use asynchronous communication with indexing tasks, and ignore the chatThreads parameter. If false, use synchronous communication in a thread pool of size chatThreads.Notrue
chatThreadsIntegerThe number of threads to use for communicating with indexing tasks. Ignored if chatAsync is true.Nomin(10, taskCount * replicas)
chatRetriesIntegerThe number of times HTTP requests to indexing tasks are retried before considering tasks unresponsive.No8
httpTimeoutISO 8601 periodThe period of time to wait for a HTTP response from an indexing task.NoPT10S
shutdownTimeoutISO 8601 periodThe period of time to wait for the supervisor to attempt a graceful shutdown of tasks before exiting.NoPT80S
offsetFetchPeriodISO 8601 periodDetermines 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 of PT5S, the supervisor ignores the value and uses the minimum value instead.NoPT30S
segmentWriteOutMediumFactoryObjectThe segment write-out medium to use when creating segments. See Additional Peon configuration: SegmentWriteOutMediumFactory for explanation and available options.NoIf not specified, Druid uses the value from druid.peon.defaultSegmentWriteOutMediumFactory.type.
logParseExceptionsBooleanIf true, Druid logs an error message when a parsing exception occurs, containing information about the row where the error occurred.Nofalse
maxParseExceptionsIntegerThe maximum number of parse exceptions that can occur before the task halts ingestion and fails. Overridden if reportParseExceptions is set.Nounlimited
maxSavedParseExceptionsIntegerWhen 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. Overridden if reportParseExceptions is set.No0


The following table outlines the configuration options for indexSpec:

bitmapObjectCompression format for bitmap indexes. Druid supports roaring and concise bitmap types.NoRoaring
dimensionCompressionStringCompression format for dimension columns. Choose from LZ4, LZF, ZSTD or uncompressed.NoLZ4
metricCompressionStringCompression format for primitive type metric columns. Choose from LZ4, LZF, ZSTD, uncompressed or none.NoLZ4
longEncodingStringEncoding 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.Nolongs