Apache Pinot Docs

Stream ingestion

Apache Pinot lets users consume data from streams and push it directly into the database, in a process known as stream ingestion. Stream ingestion makes it possible to query data within seconds of publication.
Stream ingestion provides support for checkpoints for preventing data loss.
Setting up Stream ingestion involves the following steps:
  1. 1.
    Create schema configuration
  2. 2.
    Create table configuration
  3. 3.
    Create ingestion configuration
  4. 4.
    Upload table and schema spec
Let's take a look at each of the steps in more detail.
Let us assume the data to be ingested is in the following format:

Create schema configuration

Schema defines the fields along with their data types. The schema also defines whether fields serve as dimensions , metrics or timestamp. For more details on schema configuration, see creating a schema.
For our sample data, the schema configuration looks like this:
"schemaName": "transcript",
"dimensionFieldSpecs": [
"name": "studentID",
"dataType": "INT"
"name": "firstName",
"dataType": "STRING"
"name": "lastName",
"dataType": "STRING"
"name": "gender",
"dataType": "STRING"
"name": "subject",
"dataType": "STRING"
"metricFieldSpecs": [
"name": "score",
"dataType": "FLOAT"
"dateTimeFieldSpecs": [{
"name": "timestamp",
"dataType": "LONG",
"format" : "1:MILLISECONDS:EPOCH",
"granularity": "1:MILLISECONDS"

Create table configuration

The next step is to create a table where all the ingested data will flow and can be queried. For details about each table component, see the table reference.

Create ingestion configuration

The ingestion configuration (ingestionConfig) specifies how to ingest streaming data into Pinot. First, include a subsection for streamConfigMaps. Next, decide whether to skip table errors with _continueOnError and whether to validate time values with rowTimeValueCheck and _segmentTimeValueCheck. See details about these ingestionConfig configuration options the streamConfigMaps and Additional ingestion configs tables below:


Config key
Supported values
The streaming platform to ingest data from
Whether to use per partition low-level consumer or high-level stream consumer
- lowLevel: Consume data from each partition with offset management. - highLevel: Consume data without control over the partitions.
Topic or data source to ingest data from
List of brokers
Name of class to parse the data. The class should implement the org.apache.pinot.spi.stream.StreamMessageDecoder interface.
String. Available options: - org.apache.pinot.plugin.inputformat.json.JSONMessageDecoder - org.apache.pinot.plugin.inputformat.avro.KafkaAvroMessageDecoder - org.apache.pinot.plugin.inputformat.avro.SimpleAvroMessageDecoder - org.apache.pinot.plugin.inputformat.avro.confluent.KafkaConfluentSchemaRegistryAvroMessageDecoder - org.apache.pinot.plugin.inputformat.csv.CSVMessageDecoder - org.apache.pinot.plugin.inputformat.protobuf.ProtoBufMessageDecoder
Name of factory class to provide the appropriate implementation of low-level and high-level consumer, as well as the metadata
String. Available options: - org.apache.pinot.plugin.stream.kafka09.KafkaConsumerFactory - org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory - org.apache.pinot.plugin.stream.kinesis.KinesisConsumerFactory - org.apache.pinot.plugin.stream.pulsar.PulsarConsumerFactory
Determines the offset from which to start the ingestion
- smallest - largest - timestamp in milliseconds
Specifies the data format to ingest via a stream. The value of this property should match the format of the data in the stream.
Maximum elapsed time after which a consuming segment persist. Note that this time should be smaller than the Kafka retention period configured for the corresponding topic.
String, such 1d or 4h30m. Default is 6h (six hours).
The maximum number of rows to consume before persisting the consuming segment. If this value is set to 0, the configuration looks to realtime.segment.flush.threshold.rows below.
Default is 5,000,000
Desired size of the completed segments. This value is used when realtime.segment.flush.threshold.rows is set to 0.
String, such as 150M or 1.1G., etc. Default is 200M (200 megabytes). You can also specify additional configurations for the consumer directly into streamConfigMaps. For example, for Kafka streams, add any of the configs described in Kafka configuration page to pass them directly to the Kafka consumer.

Additional ingestion configurations

Config key
Set to true to skip any row indexing error and move on to the next row. Otherwise, an error evaluating a transform or filter function may block ingestion (realtime or offline), and result in data loss or corruption. Consider your use case to determine if it's preferable to set this option to false, and fail the ingestion if an error occurs to maintain data integrity.
Set to true to validate the time column values ingested during segment upload. Validates each row of data in a segment matches the specified time format, and falls within a valid time range (1971-2071). If the value doesn't meet both criteria, Pinot replaces the value with null. This option ensures that the time values are strictly increasing and that there are no duplicates or gaps in the data.
Set to true to validate the time range of the segment falls between 1971 and 2071. This option ensures data segments stored in the system are correct and consistent

Example table config with ingestionConfig

For our sample data and schema, the table config will look like this:
"tableName": "transcript",
"tableType": "REALTIME",
"segmentsConfig": {
"timeColumnName": "timestamp",
"timeType": "MILLISECONDS",
"schemaName": "transcript",
"replicasPerPartition": "1"
"tenants": {},
"tableIndexConfig": {
"loadMode": "MMAP",
"metadata": {
"customConfigs": {}
"ingestionConfig": {
"streamIngestionConfig": {
"streamConfigMaps": [
"realtime.segment.flush.threshold.rows": "0",
"stream.kafka.decoder.prop.format": "JSON",
"key.serializer": "org.apache.kafka.common.serialization.ByteArraySerializer",
"stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
"streamType": "kafka",
"value.serializer": "org.apache.kafka.common.serialization.ByteArraySerializer",
"stream.kafka.consumer.type": "LOWLEVEL",
"realtime.segment.flush.threshold.segment.rows": "50000",
"stream.kafka.broker.list": "localhost:9876",
"realtime.segment.flush.threshold.time": "3600000",
"stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
"stream.kafka.consumer.prop.auto.offset.reset": "smallest",
"stream.kafka.topic.name": "transcript-topic"
"transformConfigs": [],
"continueOnError": true,
"rowTimeValueCheck": true,
"segmentTimeValueCheck": false
"isDimTable": false

Upload schema and table config

Now that we have our table and schema configurations, let's upload them to the Pinot cluster. As soon as the configs are uploaded, pinot will start ingesting available records from the topic.
Launcher Script
docker run \
--network=pinot-demo \
-v /tmp/pinot-quick-start:/tmp/pinot-quick-start \
--name pinot-streaming-table-creation \
apachepinot/pinot:latest AddTable \
-schemaFile /tmp/pinot-quick-start/transcript-schema.json \
-tableConfigFile /tmp/pinot-quick-start/transcript-table-realtime.json \
-controllerHost pinot-quickstart \
-controllerPort 9000 \
bin/pinot-admin.sh AddTable \
-schemaFile /path/to/transcript-schema.json \
-tableConfigFile /path/to/transcript-table-realtime.json \

Tuning stream config

Throttling stream consumption

There are some scenarios where the message rate in the input stream has a bursty nature which can lead to long GC pauses on the Pinot servers or affect the ingestion rate of other realtime tables on the same server. In such scenarios, you should throttle the consumption rate during stream ingestion.
Stream consumption throttling can be tuned using the stream config topic.consumption.rate.limit which indicates the upper bound on the message rate for the entire topic.
Here is the sample configuration on how to configure the consumption throttling:
"tableName": "transcript",
"tableType": "REALTIME",
"ingestionConfig": {
"streamConfigMaps": {
"streamType": "kafka",
"stream.kafka.consumer.type": "lowlevel",
"stream.kafka.topic.name": "transcript-topic",
"topic.consumption.rate.limit": 1000
Some things to keep in mind while tuning this config are:
  • Since this configuration applied to the entire topic, internally, this rate is divided by the number of partitions in the topic and applied to each partition's consumer.
  • In case of multi-tenant deployment (where you have more than 1 table in the same server instance), you need to make sure that the rate limit on one table doesn't step on/starve the rate limiting of another table. So, when there is more than 1 table on the same server (which is most likely to happen), you may need to re-tune the throttling threshold for all the streaming tables.
Once throttling is enabled for a table, you can verify by searching for a log that looks similar to:
A consumption rate limiter is set up for topic <topic_name> in table <tableName> with rate limit: <rate_limit> (topic rate limit: <topic_rate_limit>, partition count: <partition_count>)
In addition, you can monitor the consumption rate utilization with the metric COSUMPTION_QUOTA_UTILIZATION.
Note that any configuration change for topic.consumption.rate.limit in the stream config will NOT take effect immediately. The new configuration will be picked up from the next consuming segment. In order to enforce the new configuration, you need to trigger forceCommit APIs. Please refer to Pause Stream Ingestion for more details.
$ curl -X POST {controllerHost}/tables/{tableName}/forceCommit

Custom Ingestion Support

We are working on support for other ingestion platforms, but you can also write your own ingestion plugin if it is not supported out of the box. For a walkthrough, see Stream Ingestion Plugin.

Pause Stream Ingestion

There are some scenarios in which you may want to pause the realtime ingestion while your table is available for queries. For example if there is a problem with the stream ingestion, while you are troubleshooting the issue, you still want the queries to be executed on the already ingested data. For these scenarios, you can first issue a Pause request to a Controller host. After troubleshooting with the stream is done, you can issue another request to Controller to resume the consumption.
$ curl -X POST {controllerHost}/tables/{tableName}/pauseConsumption
$ curl -X POST {controllerHost}/tables/{tableName}/resumeConsumption
When a Pause request is issued, Controller instructs the realtime servers hosting your table to commit their consuming segments immediately. However, the commit process may take some time to complete. Please note that Pause and Resume requests are async. OK response means that instructions for pausing or resuming has been successfully sent to the realtime server. If you want to know if the consumptions actually stopped or resumed, you can issue a pause status request.
$ curl -X POST {controllerHost}/tables/{tableName}/pauseStatus
It's worth noting that consuming segments on realtime servers are stored in volatile memory, and their resources are allocated when the consuming segments are first created. These resources cannot be altered if consumption parameters are changed midway through consumption. It may therefore take hours before these changes take effect. Furthermore, if the parameters are changed in an incompatible way (for example, changing the underlying stream with a completely new set of offsets, or changing the stream endpoint from which to consume messages, etc.), it will result in the table getting into an error state.
Pause and resume feature comes to the rescue here. When a Pause request is issued by the operator, consuming segments are committed without starting new mutables ones. Instead, new mutable segments are started only when the Resume request is issued. This mechanism provides the operators as well as developers with more flexibility. It also enables Pinot to be more resilient to the operational and functional constraints imposed by underlying streams.
There is another feature called "Force Commit" which utilizes the primitives of pause and resume feature. When the operator issues a force commit request, the current mutable segments will be committed and new ones started right away. Operators can now use this feature for all compatible table config parameter changes to take effect immediately.
$ curl -X POST {controllerHost}/tables/{tableName}/forceCommit
(v 0.12.0+) Once submitted, the forceCommit API returns a jobId that can be used to get the current progress of the forceCommit operation. A sample response and status API call:
$ curl -X POST {controllerHost}/tables/{tableName}/forceCommit
"forceCommitJobId": "6757284f-b75b-45ce-91d8-a277bdbc06ae",
"forceCommitStatus": "SUCCESS",
"jobMetaZKWriteStatus": "SUCCESS"
$ curl -X GET {controllerHost}/tables/forceCommitStatus/6757284f-b75b-45ce-91d8-a277bdbc06ae
"jobId": "6757284f-b75b-45ce-91d8-a277bdbc06ae",
"segmentsForceCommitted": "[\"airlineStats__0__0__20230119T0700Z\",\"airlineStats__1__0__20230119T0700Z\",\"airlineStats__2__0__20230119T0700Z\"]",
"submissionTimeMs": "1674111682977",
"numberOfSegmentsYetToBeCommitted": 0,
"jobType": "FORCE_COMMIT",
"segmentsYetToBeCommitted": [],
"tableName": "airlineStats_REALTIME"
For incompatible parameter changes, an option is added to the resume request to handle the case of a completely new set of offsets. Operators can now follow a three-step process: First, issue a Pause request. Second, change the consumption parameters. Finally, issue the Resume request with the appropriate option. These steps will preserve the old data and allow the new data to be consumed immediately. All through the operation, queries will continue to be served.
$ curl -X POST {controllerHost}/tables/{tableName}/resumeConsumption?resumeFrom=smallest
$ curl -X POST {controllerHost}/tables/{tableName}/resumeConsumption?resumeFrom=largest

Handling partition changes in Streams

If a Pinot table is configured to consume using a Low Level (partition-based) stream type, then it is possible that the partitions of the table change over time. In Kafka, for example, the number of partitions may increase. In Kinesis, the number of partitions may increase or decrease -- some partitions could be merged to create a new one, or existing partitions split to create new ones.
Pinot runs a periodic task called RealtimeSegmentValidationManager that monitors such changes and starts consumption on new partitions (or stops consumptions from old ones) as necessary. Since this is a periodic task that is run on the controller, it may take some time for Pinot to recognize new partitions and start consuming from them. This may delay the data in new partitions appearing in the results that pinot returns.
If it is desired to recognize the new partitions sooner, then you can manually trigger the periodic task so as to recognize such data immediately.

Inferring Ingestion Status of Realtime Tables

Often, it is important to understand the rate of ingestion of data into your realtime table. This is commonly done by looking at the consumption "lag" of the consumer. The lag itself can be observed in many dimensions. Pinot supports observing consumption lag along the offset dimension and time dimension, whenever applicable (as it depends on the specifics of the connector).
The ingestion status of a connector can be observed by querying either the /consumingSegmentsInfo API or the table's /debug API, as shown below:
# GET /tables/{tableName}/consumingSegmentsInfo
curl -X GET "http://<controller_url:controller_admin_port>/tables/meetupRsvp/consumingSegmentsInfo" -H "accept: application/json"
# GET /debug/tables/{tableName}
curl -X GET "http://localhost:9000/debug/tables/meetupRsvp?type=REALTIME&verbosity=1" -H "accept: application/json"
A sample response from a Kafka based realtime table is shown below. The ingestion status is displayed for each of the CONSUMING segments in the table.
"_segmentToConsumingInfoMap": {
"meetupRsvp__0__0__20221019T0639Z": [
"serverName": "Server_192.168.0.103_7000",
"consumerState": "CONSUMING",
"lastConsumedTimestamp": 1666161593904,
"partitionToOffsetMap": { // <<-- Deprecated. See currentOffsetsMap for same info
"0": "6"
"partitionOffsetInfo": {
"currentOffsetsMap": {
"0": "6" // <-- Current consumer position
"latestUpstreamOffsetMap": {
"0": "6" // <-- Upstream latest position
"recordsLagMap": {
"0": "0" // <-- Lag, in terms of #records behind latest
"recordsAvailabilityLagMap": {
"0": "2" // <-- Lag, in terms of time
Current consuming offset position per partition
(Wherever applicable) Latest offset found in the upstream topic partition
(Whenever applicable) Defines how far behind the current record's offset / pointer is from upstream latest record. This is calculated as the difference between the latestUpstreamOffset and currentOffset for the partition when the lag computation request is made.
(Whenever applicable) Defines how soon after record ingestion was the record consumed by Pinot. This is calculated as the difference between the time the record was consumed and the time at which the record was ingested upstream.

Monitoring Realtime Ingestion

Realtime ingestion includes 3 stages of message processing: Decode, Transform and Index.
In each of these stages, a failure can happen which may or may not result in an ingestion failure. The following metrics are available to investigation ingestion issues:
  1. 1.
    Decode stage -> error here is recorded as INVALID_REALTIME_ROWS_DROPPED
  2. 2.
    Transform stage -> possible errors here are:
    1. 1.
      When a message gets dropped due to the FILTER transform, it is recorded as REALTIME_ROWS_FILTERED
    2. 2.
      When the transform pipeline sets the $INCOMPLETE_RECORD_KEY$ key in the message, it is recorded as INCOMPLETE_REALTIME_ROWS_CONSUMED , only when continueOnError configuration is enabled. If the continueOnError is not enabled, the ingestion fails.
  3. 3.
    Index stage -> When there is failure at this stage, the ingestion typically stops and marks the partition as ERROR.
There is yet another metric called ROWS_WITH_ERROR which is the sum of all error counts in the 3 stages above.
Furthermore, the metric REALTIME_CONSUMPTION_EXCEPTIONS gets incremented whenever there is a transient/permanent stream exception seen during consumption.
These metrics can be used to understand why ingestion failed for a particular table partition before diving into the server logs.