druid/docs/development/extensions-core/kafka-ingestion.md

id	title	sidebar_label
kafka-ingestion	Apache Kafka ingestion	Apache Kafka

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.

Tutorial

This page contains reference documentation for Apache Kafka-based ingestion. For a walk-through instead, check out the Loading from Apache Kafka tutorial.

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": [
            "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"
    }
  },
  "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 dataSchema for details.	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

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 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

IndexSpec

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)

SegmentWriteOutMediumFactory

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

KafkaSupervisorIOConfig

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<String, Object>	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)

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 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

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).