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KafkaSourceProvider

KafkaSourceProvider is the entry point to the kafka data source.

KafkaSourceProvider is a SimpleTableProvider (and does not support custom table schema and partitioning).

Note

KafkaSourceProvider is also a StreamSourceProvider and a StreamSinkProvider to be used in Spark Structured Streaming.

Learn more on StreamSourceProvider and StreamSinkProvider in The Internals of Spark Structured Streaming online book.

DataSourceRegister

KafkaSourceProvider is a DataSourceRegister and registers itself as kafka format.

KafkaSourceProvider uses META-INF/services/org.apache.spark.sql.sources.DataSourceRegister file for the registration (available in the source code of Apache Spark).

KafkaTable

getTable(
  options: CaseInsensitiveStringMap): KafkaTable

getTable is part of the SimpleTableProvider abstraction.

getTable creates a KafkaTable with the includeHeaders flag based on includeHeaders option.

Creating Relation for Reading (RelationProvider)

createRelation(
  sqlContext: SQLContext,
  parameters: Map[String, String]): BaseRelation

createRelation is part of the RelationProvider abstraction.

createRelation starts by <> in the input parameters.

createRelation collects all kafka.-prefixed key options (in the input parameters) and creates a local specifiedKafkaParams with the keys without the kafka. prefix (e.g. kafka.whatever is simply whatever).

createRelation <> with the startingoffsets offset option key (in the given parameters) and EarliestOffsetRangeLimit as the default offsets.

createRelation makes sure that the KafkaOffsetRangeLimit is not EarliestOffsetRangeLimit or throws an AssertionError.

createRelation <>, but this time with the endingoffsets offset option key (in the given parameters) and LatestOffsetRangeLimit as the default offsets.

createRelation makes sure that the KafkaOffsetRangeLimit is not EarliestOffsetRangeLimit or throws a AssertionError.

In the end, createRelation creates a KafkaRelation with the <> (in the given parameters), <> option, and the starting and ending offsets.

Creating Relation for Writing (CreatableRelationProvider)

createRelation(
  sqlContext: SQLContext,
  mode: SaveMode,
  parameters: Map[String, String],
  df: DataFrame): BaseRelation

createRelation is part of the CreatableRelationProvider abstraction.

createRelation gets the topic option from the input parameters.

createRelation gets the <> from the input parameters.

createRelation then uses the KafkaWriter helper object to write the rows of the DataFrame to the Kafka topic.

In the end, createRelation creates a fake BaseRelation that simply throws an UnsupportedOperationException for all its methods.

createRelation supports Append and ErrorIfExists only. createRelation throws an AnalysisException for the other save modes:

Save mode [mode] not allowed for Kafka. Allowed save modes are [Append] and [ErrorIfExists] (default).

Kafka Configuration Properties for Driver

kafkaParamsForDriver(
  specifiedKafkaParams: Map[String, String]): ju.Map[String, Object]

kafkaParamsForDriver is a utility to define required Kafka configuration parameters for the driver.

kafkaParamsForDriver is used when:

  • KafkaBatch is requested to planInputPartitions
  • KafkaRelation is requested to buildScan
  • KafkaSourceProvider to createSource (for Spark Structured Streaming)
  • KafkaScan is requested to toMicroBatchStream and toContinuousStream (for Spark Structured Streaming)

auto.offset.reset

What to do when there is no initial offset in Kafka or if the current offset does not exist any more on the server (e.g. because that data has been deleted):

  • earliest - automatically reset the offset to the earliest offset

  • latest - automatically reset the offset to the latest offset

  • none - throw an exception to the Kafka consumer if no previous offset is found for the consumer's group

  • anything else - throw an exception to the Kafka consumer

Value: earliest

ConsumerConfig.AUTO_OFFSET_RESET_CONFIG

enable.auto.commit

If true the Kafka consumer's offset will be periodically committed in the background

Value: false

ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG

key.deserializer

Deserializer class for keys that implements the Kafka Deserializer interface.

Value: org.apache.kafka.common.deserialization.ByteArrayDeserializer

ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG

max.poll.records

The maximum number of records returned in a single call to Consumer.poll()

Value: 1

ConsumerConfig.MAX_POLL_RECORDS_CONFIG

receive.buffer.bytes

Only set if not set already

Value: 65536

ConsumerConfig.MAX_POLL_RECORDS_CONFIG

value.deserializer

Deserializer class for values that implements the Kafka Deserializer interface.

Value: org.apache.kafka.common.serialization.ByteArrayDeserializer

ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG

Tip

Enable ALL logging level for org.apache.spark.sql.kafka010.KafkaSourceProvider.ConfigUpdater logger to see updates of Kafka configuration parameters.

Add the following line to conf/log4j.properties:

log4j.logger.org.apache.spark.sql.kafka010.KafkaSourceProvider.ConfigUpdater=ALL

Refer to Logging.

kafkaParamsForExecutors

kafkaParamsForExecutors(
  specifiedKafkaParams: Map[String, String],
  uniqueGroupId: String): ju.Map[String, Object]

kafkaParamsForExecutors...FIXME

kafkaParamsForExecutors is used when:

  • KafkaBatch is requested to planInputPartitions
  • KafkaRelation is requested to buildScan
  • KafkaSourceProvider to createSource (for Spark Structured Streaming)
  • KafkaScan is requested to toMicroBatchStream and toContinuousStream (for Spark Structured Streaming)

kafkaParamsForProducer

kafkaParamsForProducer(
  params: CaseInsensitiveMap[String]): ju.Map[String, Object]

kafkaParamsForProducer...FIXME

kafkaParamsForProducer is used when:

Validating Kafka Options for Batch Queries

validateBatchOptions(
  params: CaseInsensitiveMap[String]): Unit

validateBatchOptions <> for the startingoffsets option in the input caseInsensitiveParams and with EarliestOffsetRangeLimit as the default KafkaOffsetRangeLimit.

validateBatchOptions then matches the returned KafkaOffsetRangeLimit as follows:

  • EarliestOffsetRangeLimit is acceptable and validateBatchOptions simply does nothing

  • LatestOffsetRangeLimit is not acceptable and validateBatchOptions throws an IllegalArgumentException:

    starting offset can't be latest for batch queries on Kafka
    
  • SpecificOffsetRangeLimit is acceptable unless one of the offsets is -1L for which validateBatchOptions throws an IllegalArgumentException:

    startingOffsets for [tp] can't be latest for batch queries on Kafka
    

validateBatchOptions is used when:

Getting Desired KafkaOffsetRangeLimit (for Offset Option)

getKafkaOffsetRangeLimit(
  params: Map[String, String],
  offsetOptionKey: String,
  defaultOffsets: KafkaOffsetRangeLimit): KafkaOffsetRangeLimit

getKafkaOffsetRangeLimit tries to find the given offsetOptionKey in the input params and converts the value found to a KafkaOffsetRangeLimit as follows:

When the input offsetOptionKey was not found, getKafkaOffsetRangeLimit returns the input defaultOffsets.

getKafkaOffsetRangeLimit is used when:

  • KafkaSourceProvider is requested for a relation for reading and createSource (Spark Structured Streaming)
  • KafkaScan is requested for a Batch, toMicroBatchStream (Spark Structured Streaming) and toContinuousStream (Spark Structured Streaming)

ConsumerStrategy

strategy(
  params: CaseInsensitiveMap[String]): ConsumerStrategy

strategy finds one of the strategy options: subscribe, subscribepattern and assign.

For assign, strategy uses the JsonUtils helper object to deserialize TopicPartitions from JSON (e.g. {"topicA":[0,1],"topicB":[0,1]}) and returns a new AssignStrategy.

For subscribe, strategy splits the value by , (comma) and returns a new SubscribeStrategy.

For subscribepattern, strategy returns a new SubscribePatternStrategy

strategy is used when:

  • KafkaSourceProvider is requested for a relation for reading and createSource (Spark Structured Streaming)
  • KafkaScan is requested for a Batch, toMicroBatchStream (Spark Structured Streaming) and toContinuousStream (Spark Structured Streaming)

failOnDataLoss Option

failOnDataLoss(
  params: CaseInsensitiveMap[String]): Boolean

failOnDataLoss is the value of failOnDataLoss key in the given case-insensitive parameters (options) if available or true.

  • KafkaSourceProvider is requested for a relation for reading (and createSource for Spark Structured Streaming)
  • KafkaScan is requested for a Batch (and toMicroBatchStream and toContinuousStream for Spark Structured Streaming)

Last update: 2020-11-13