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. 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 extension (see Including Extensions). Please note that the Kafka indexing service is currently designated as an experimental feature and is subject to the usual experimental caveats.
The Kafka indexing service requires that the druid-kafka-indexing-service extension be loaded on both the overlord and the middle managers. 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" } }
| Field | Description | Required |
|---|---|---|
type | The supervisor type, this should always be kafka. | yes |
dataSchema | The schema that will be used by the Kafka indexing task during ingestion, see Ingestion Spec DataSchema. | yes |
tuningConfig | A KafkaSupervisorTuningConfig to configure the supervisor and indexing tasks, see below. | no |
ioConfig | A KafkaSupervisorIOConfig to configure the supervisor and indexing tasks, see below. | yes |
The tuningConfig is optional and default parameters will be used if no tuningConfig is specified.
| Field | Type | Description | Required |
|---|---|---|---|
type | String | The indexing task type, this should always be kafka. | yes |
maxRowsInMemory | Integer | The number of rows to aggregate before persisting. This number is the post-aggregation rows, so it is not equivalent to the number of input events, but the number of aggregated rows that those events result in. This is used to manage the required JVM heap size. Maximum heap memory usage for indexing scales with maxRowsInMemory * (2 + maxPendingPersists). Normally user does not need to set this, but depending on the nature of data, if rows are short in terms of bytes, user may not want to store a million rows in memory and this value should be set. | no (default == 1000000) |
maxBytesInMemory | Long | The number of bytes to aggregate in heap memory before persisting. This is based on a rough estimate of memory usage and not actual usage. Normally this is computed internally and user does not need to set it. The maximum heap memory usage for indexing is maxBytesInMemory * (2 + maxPendingPersists). | no (default == One-sixth of max JVM memory) |
maxRowsPerSegment | Integer | The number of rows to aggregate into a segment; this number is post-aggregation rows. Handoff will happen either if maxRowsPerSegment or maxTotalRows is hit or every intermediateHandoffPeriod, whichever happens earlier. | no (default == 5000000) |
maxTotalRows | Long | The number of rows to aggregate across all segments; this number is post-aggregation rows. Handoff will happen either if maxRowsPerSegment or maxTotalRows is hit or every intermediateHandoffPeriod, whichever happens earlier. | no (default == unlimited) |
intermediatePersistPeriod | ISO8601 Period | The period that determines the rate at which intermediate persists occur. | no (default == PT10M) |
maxPendingPersists | Integer | Maximum number of persists that can be pending but not started. If this limit would be exceeded by a new intermediate persist, ingestion will block until the currently-running persist finishes. Maximum heap memory usage for indexing scales with maxRowsInMemory * (2 + maxPendingPersists). | no (default == 0, meaning one persist can be running concurrently with ingestion, and none can be queued up) |
indexSpec | Object | Tune how data is indexed, see ‘IndexSpec’ below for more details. | no |
reportParseExceptions | 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. | false | no |
handoffConditionTimeout | Long | Milliseconds to wait for segment handoff. It must be >= 0, where 0 means to wait forever. | no (default == 0) |
resetOffsetAutomatically | Boolean | Whether to reset the consumer offset if the next offset that it is trying to fetch is less than the earliest available offset for that particular partition. The consumer offset will be reset to either the earliest or latest offset depending on useEarliestOffset property of KafkaSupervisorIOConfig (see below). This situation typically occurs when messages in Kafka are no longer available for consumption and therefore won't be ingested into Druid. If set to false then ingestion for that particular partition will halt and manual intervention is required to correct the situation, please see Reset Supervisor API below. | no (default == false) |
workerThreads | Integer | The number of threads that will be used by the supervisor for asynchronous operations. | no (default == min(10, taskCount)) |
chatThreads | Integer | The number of threads that will be used for communicating with indexing tasks. | no (default == min(10, taskCount * replicas)) |
chatRetries | Integer | The number of times HTTP requests to indexing tasks will be retried before considering tasks unresponsive. | no (default == 8) |
httpTimeout | ISO8601 Period | How long to wait for a HTTP response from an indexing task. | no (default == PT10S) |
shutdownTimeout | ISO8601 Period | How long to wait for the supervisor to attempt a graceful shutdown of tasks before exiting. | no (default == PT80S) |
offsetFetchPeriod | ISO8601 Period | How often the supervisor queries Kafka and the indexing tasks to fetch current offsets and calculate lag. | no (default == PT30S, min == PT5S) |
segmentWriteOutMediumFactory | String | Segment write-out medium to use when creating segments. See Additional Peon Configuration: SegmentWriteOutMediumFactory for explanation and available options. | no (not specified by default, the value from druid.peon.defaultSegmentWriteOutMediumFactory is used) |
intermediateHandoffPeriod | ISO8601 Period | How often the tasks should hand off segments. Handoff will happen either if maxRowsPerSegment or maxTotalRows is hit or every intermediateHandoffPeriod, whichever happens earlier. | no (default == P2147483647D) |
logParseExceptions | Boolean | If true, log an error message when a parsing exception occurs, containing information about the row where the error occurred. | no, default == false |
maxParseExceptions | Integer | The maximum number of parse exceptions that can occur before the task halts ingestion and fails. Overridden if reportParseExceptions is set. | no, unlimited default |
maxSavedParseExceptions | Integer | When a parse exception occurs, Druid can keep track of the most recent parse exceptions. “maxSavedParseExceptions” limits how many exception instances will be saved. These saved exceptions will be made available after the task finishes in the task completion report. Overridden if reportParseExceptions is set. | no, default == 0 |
| Field | Type | Description | Required |
|---|---|---|---|
| bitmap | Object | Compression format for bitmap indexes. Should be a JSON object; see below for options. | no (defaults to Concise) |
| dimensionCompression | String | Compression format for dimension columns. Choose from LZ4, LZF, or uncompressed. | no (default == LZ4) |
| metricCompression | String | Compression format for metric columns. Choose from LZ4, LZF, uncompressed, or none. | no (default == LZ4) |
| longEncoding | String | Encoding format for metric and dimension columns with type long. Choose from auto or longs. auto encodes the values using offset or lookup table depending on column cardinality, and store them with variable size. longs stores the value as is with 8 bytes each. | no (default == longs) |
For Concise bitmaps:
| Field | Type | Description | Required |
|---|---|---|---|
type | String | Must be concise. | yes |
For Roaring bitmaps:
| Field | Type | Description | Required |
|---|---|---|---|
type | String | Must be roaring. | yes |
compressRunOnSerialization | Boolean | Use a run-length encoding where it is estimated as more space efficient. | no (default == true) |
| Field | Type | Description | Required |
|---|---|---|---|
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 |
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 node 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. | no (default == none) |
skipOffsetGaps | Boolean | Whether or not to allow gaps of missing offsets in the Kafka stream. This is required for compatibility with implementations such as MapR Streams which does not guarantee consecutive offsets. If this is false, an exception will be thrown if offsets are not consecutive. | no (default == false) |
The following endpoints are available on the Overlord:
POST /druid/indexer/v1/supervisor
Use Content-Type: application/json and provide a supervisor spec in the request body.
Calling this endpoint when there is already an existing supervisor for the same dataSource will cause:
Seamless schema migrations can thus be achieved by simply submitting the new schema using this endpoint.
POST /druid/indexer/v1/supervisor/<supervisorId>/suspend
Suspend indexing tasks associated with a supervisor. 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. Responds with updated SupervisorSpec.
POST /druid/indexer/v1/supervisor/suspendAll
Suspend all supervisors at once.
POST /druid/indexer/v1/supervisor/<supervisorId>/resume
Resume indexing tasks for a supervisor. Responds with updated SupervisorSpec.
POST /druid/indexer/v1/supervisor/resumeAll
Resume all supervisors at once.
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.
POST /druid/indexer/v1/supervisor/<supervisorId>/terminate
Terminate a supervisor and cause 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.
POST /druid/indexer/v1/supervisor/terminateAll
Terminate all supervisors at once.
Deprecated: use the equivalent ‘terminate’ instead
POST /druid/indexer/v1/supervisor/<supervisorId>/shutdown
GET /druid/indexer/v1/supervisor
Returns a list of strings of the currently active supervisor ids.
GET /druid/indexer/v1/supervisor?full
Returns a list of objects of the currently active supervisors.
| Field | Type | Description |
|---|---|---|
id | String | supervisor unique identifier |
spec | SupervisorSpec | json specification of supervisor (See Supervisor Configuration for details) |
GET /druid/indexer/v1/supervisor/<supervisorId>
Returns the current spec for the supervisor with the provided ID.
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.
GET /druid/indexer/v1/supervisor/history
Returns an audit history of specs for all supervisors (current and past).
GET /druid/indexer/v1/supervisor/<supervisorId>/history
Returns an audit history of specs for the supervisor with the provided ID.
Kafka indexing tasks run on middle managers and are thus 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 (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 node (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.
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>/shutdown 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 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.
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 nodes 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).