Ingestion

The ingestion configuration ('ingestionConfig') is a section of the table configuration that specifies how to ingest streaming data into Pinot.

ingestionConfig

Config key	Description
streamConfigMaps	See the streamConfigMaps section for details.
batchIngestionConfig	See the batchIngestionConfig section for details.
continueOnError	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 (real-time 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.
rowTimeValueCheck	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.
segmentTimeValueCheck	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.

streamConfigMaps

Config key	Description	Supported values
streamType	The streaming platform to ingest data from	kafka
stream.[streamType].consumer.type	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.
stream.[streamType].topic.name	Topic or data source to ingest data from	String
stream.[streamType].broker.list	List of brokers
stream.[streamType].decoder.class.name	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 - org.apache.pinot.plugin.inputformat.protobuf.KafkaConfluentSchemaRegistryProtoBufMessageDecoder
stream.[streamType].consumer.factory.class.name	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.kafka20.KafkaConsumerFactory - org.apache.pinot.plugin.stream.kafka30.KafkaConsumerFactory - org.apache.pinot.plugin.stream.kinesis.KinesisConsumerFactory - org.apache.pinot.plugin.stream.pulsar.PulsarConsumerFactory
stream.[streamType].consumer.prop.auto.offset.reset	Determines the offset from which to start the ingestion	- smallest - largest - timestamp in milliseconds
stream.[streamType].decoder.prop.format	Specifies the data format to ingest via a stream. The value of this property should match the format of the data in the stream.	- JSON
realtime.segment.flush.threshold.time	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).
realtime.segment.flush.threshold.rows	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.segment.size below. See note below this table for more information.	Default is 5,000,000
realtime.segment.flush.threshold.segment.rows	The maximum number of rows to consume before persisting the consuming segment. Added since release-1.2.0. See note below this table for more information.	Int
realtime.segment.flush.threshold.segment.size	Size the completed segments should be. 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.

The number of rows per segment is computed using the following formula: realtime.segment.flush.threshold.rows / maxPartitionsConsumedByServer For example, if you set realtime.segment.flush.threshold.rows = 1000 and each server consumes 10 partitions, the rows per segment is 1000/10 = 100.

Since release-1.2.0, we introduced realtime.segment.flush.threshold.segment.rows, which is directly used as the number of rows per segment.

Take the above example, if you set realtime.segment.flush.threshold.segment.rows = 1000 and each server consumes 10 partitions, the rows per segment is 1000.

Since this PR, streamConfigMaps could contain multiple maps pointing to multiple Kafka topics. This would allow creating one single Pinot table with data from multiple stream topics.

Example table config with ingestionConfig

{
  "tableName": "transcript",
  "tableType": "REALTIME",
  "segmentsConfig": {
    "timeColumnName": "timestamp",
    "timeType": "MILLISECONDS",
    "replication": "1"
  },
  "tenants": {},
  "tableIndexConfig": {
    "loadMode": "MMAP",
  },
  "ingestionConfig": {
    "streamIngestionConfig": {
      "streamConfigMaps": [{
        "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",
        "stream.kafka.broker.list": "localhost:9876",
        "realtime.segment.flush.threshold.segment.rows": "500000",
        "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
  }
}

batchIngestionConfig

Config key	Description	Supported values
segmentIngestionType	Can be either: APPEND (default): New data segments pushed periodically, to append to the existing data eg. daily or hourly. Time column is mandatory for this push type. REFRESH: Entire data is replaced every time during a data push. Refresh tables have no retention.	APPEND or REFRESH
segmentIngestionFrequency	The cadence at which segments are pushed, such as HOURLY or DAILY	HOURLY or DAILY

Example table config with batchIngestionConfig

{
  "tableName": "transcript",
  "tableType": "OFFLINE",
  "segmentsConfig": {
    "timeColumnName": "timestamp",
    "timeType": "MILLISECONDS",
    "replication": "1"
  },
  "tenants": {},
  "tableIndexConfig": {
    "loadMode": "MMAP",
  },
  "ingestionConfig": {
    "batchIngestionConfig": {
      "segmentIngestionType": "APPEND",
      "segmentIngestionFrequency": "HOURLY"
    }
  }
}