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KafkaSourceProvider is the entry point (provider) to the built-in Kafka support in Spark Structured Streaming (and Spark SQL).

KafkaSourceProvider is a DataSourceRegister (Spark SQL) that registers itself under the kafka alias.

KafkaSourceProvider supports micro-batch stream processing (through MicroBatchStream) and uses a specialized KafkaMicroBatchReader.

Short Name (Alias)

shortName(): String

shortName is part of the DataSourceRegister abstraction.

shortName is kafka.

Kafka Consumer Properties on Executors

  specifiedKafkaParams: Map[String, String],
  uniqueGroupId: String): Map[String, Object]

kafkaParamsForExecutors sets the Kafka properties for the Kafka Consumers on executors.

kafkaParamsForExecutors creates a KafkaConfigUpdater for executor module (with the given specifiedKafkaParams).

kafkaParamsForExecutors sets (overrides) the following Kafka properties explicitly (in the KafkaConfigUpdater).

ConsumerConfig's Key Value Note
key.deserializer ByteArrayDeserializer
value.deserializer ByteArrayDeserializer
auto.offset.reset none [uniqueGroupId]-executor setIfUnset false
receive.buffer.bytes 65536 setIfUnset

In the end, kafkaParamsForExecutors requests the KafkaConfigUpdater to build a Kafka configuration.

kafkaParamsForExecutors is used when:

Unique Group ID for Batch Queries

  params: CaseInsensitiveMap[String]): String

batchUniqueGroupId takes GROUP_ID_PREFIX, if specified, or defaults to spark-kafka-relation prefix to build the following group ID:


batchUniqueGroupId is used when:

Unique Group ID for Streaming Queries

  params: CaseInsensitiveMap[String],
  metadataPath: String): String

streamingUniqueGroupId takes GROUP_ID_PREFIX, if specified, or defaults to spark-kafka-source prefix to build the following group ID:


streamingUniqueGroupId is used when:

Required Options

KafkaSourceProvider requires the following options (that you can set using option method of DataStreamReader or DataStreamWriter):


Refer to Kafka Data Source's Options for the supported configuration options.

Creating KafkaTable

  options: CaseInsensitiveStringMap): KafkaTable

getTable creates a KafkaTable with the value of includeheaders option (default: false).

getTable is part of the SimpleTableProvider abstraction (Spark SQL).

Creating Streaming Sink

  sqlContext: SQLContext,
  parameters: Map[String, String],
  partitionColumns: Seq[String],
  outputMode: OutputMode): Sink

createSink creates a KafkaSink for topic option (if defined) and Kafka Producer parameters.

createSink is part of the StreamSinkProvider abstraction.

Creating Streaming Source

  sqlContext: SQLContext,
  metadataPath: String,
  schema: Option[StructType],
  providerName: String,
  parameters: Map[String, String]): Source

createSource is part of the StreamSourceProvider abstraction.

createSource validates stream options.


Validating Options For Batch And Streaming Queries

  parameters: Map[String, String]): Unit


Parameters are case-insensitive, i.e. OptioN and option are equal.

validateGeneralOptions makes sure that exactly one topic subscription strategy is used in parameters and can be:

  • subscribe
  • subscribepattern
  • assign

validateGeneralOptions reports an IllegalArgumentException when there is no subscription strategy in use or there are more than one strategies used.

validateGeneralOptions makes sure that the value of subscription strategies meet the requirements:

  • assign strategy starts with { (the opening curly brace)
  • subscribe strategy has at least one topic (in a comma-separated list of topics)
  • subscribepattern strategy has the pattern defined

validateGeneralOptions makes sure that has not been specified and reports an IllegalArgumentException otherwise.

Kafka option '' is not supported as user-specified consumer groups are not used to track offsets.

validateGeneralOptions makes sure that auto.offset.reset has not been specified and reports an IllegalArgumentException otherwise.

Kafka option 'auto.offset.reset' is not supported.
Instead set the source option 'startingoffsets' to 'earliest' or 'latest' to specify where to start. Structured Streaming manages which offsets are consumed internally, rather than relying on the kafkaConsumer to do it. This will ensure that no data is missed when new topics/partitions are dynamically subscribed. Note that 'startingoffsets' only applies when a new Streaming query is started, and
that resuming will always pick up from where the query left off. See the docs for more details.

validateGeneralOptions makes sure that the following options have not been specified and reports an IllegalArgumentException otherwise:

  • kafka.key.deserializer
  • kafka.value.deserializer
  • kafka.interceptor.classes

In the end, validateGeneralOptions makes sure that kafka.bootstrap.servers option was specified and reports an IllegalArgumentException otherwise.

Option 'kafka.bootstrap.servers' must be specified for configuring Kafka consumer

validateGeneralOptions is used when KafkaSourceProvider validates options for streaming and batch queries.

Creating ConsumerStrategy

  caseInsensitiveParams: Map[String, String]): ConsumerStrategy

strategy converts a key (in caseInsensitiveParams) to a ConsumerStrategy.

Key ConsumerStrategy
assign AssignStrategy
subscribe SubscribeStrategy
subscribepattern SubscribePatternStrategy

strategy is used when...FIXME


AssignStrategy with Kafka TopicPartitions

strategy uses JsonUtils.partitions method to parse a JSON with topic names and partitions, e.g.


The topic names and partitions are mapped directly to Kafka's TopicPartition objects.


SubscribeStrategy with topic names

strategy extracts topic names from a comma-separated string, e.g.



SubscribePatternStrategy with topic subscription regex pattern (that uses a Java java.util.regex.Pattern for the pattern), e.g.


Name and Schema of Streaming Source

  sqlContext: SQLContext,
  schema: Option[StructType],
  providerName: String,
  parameters: Map[String, String]): (String, StructType)

sourceSchema gives the short name (i.e. kafka) and the fixed schema.

Internally, sourceSchema validates Kafka options and makes sure that the optional input schema is indeed undefined.

When the input schema is defined, sourceSchema reports a IllegalArgumentException.

Kafka source has a fixed schema and cannot be set with a custom one

sourceSchema is part of the StreamSourceProvider abstraction.

Validating Kafka Options for Streaming Queries

  caseInsensitiveParams: Map[String, String]): Unit

validateStreamOptions makes sure that endingoffsets option is not used. Otherwise, validateStreamOptions reports a IllegalArgumentException.

ending offset not valid in streaming queries

validateStreamOptions validates the general options.

validateStreamOptions is used when KafkaSourceProvider is requested for the schema for Kafka source and to create a KafkaSource.

Converting Configuration Options to KafkaOffsetRangeLimit

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

getKafkaOffsetRangeLimit finds the given offsetOptionKey in the params and does the following conversion:

getKafkaOffsetRangeLimit is used when:

Creating Fake BaseRelation

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


createRelation is part of the RelationProvider abstraction (Spark SQL).

Validating Configuration Options for Batch Processing

  caseInsensitiveParams: Map[String, String]): Unit


validateBatchOptions is used when:


  caseInsensitiveParams: Map[String, String]): Boolean

failOnDataLoss looks up the failOnDataLoss configuration property (in the given caseInsensitiveParams) or defaults to true.

failOnDataLoss is used when:



  specifiedKafkaParams: Map[String, String]): Map[String, Object]


kafkaParamsForDriver is used when:

Kafka Producer Parameters

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

kafkaParamsForProducer converts the given params.

kafkaParamsForProducer creates a KafkaConfigUpdater for executor module (with the converted params) and defines the two serializer-specific options to use ByteArraySerializer:

  • key.serializer
  • value.serializer

In the end, kafkaParamsForProducer requests the KafkaConfigUpdater to build a Kafka configuration (Map[String, Object]).

kafkaParamsForProducer ensures that neither kafka.key.serializer nor kafka.value.serializer are specified or throws an IllegalArgumentException.

Kafka option 'key.serializer' is not supported as keys are serialized with ByteArraySerializer.
Kafka option 'value.serializer' is not supported as values are serialized with ByteArraySerializer.

kafkaParamsForProducer is used when:


  parameters: Map[String, String]): Map[String, String]

convertToSpecifiedParams finds kafka.-prefixed keys in the given parameters to drop the kafka. prefix and create a new parameters with a Kafka-specific configuration.

convertToSpecifiedParams is used when:


Enable ALL logging level for org.apache.spark.sql.kafka010.KafkaSourceProvider logger to see what happens inside.

Add the following line to conf/

Refer to Logging.