spark-sql-streaming-kafka-data-source.adoc

Kafka Data Source — Streaming Data Source for Apache Kafka

Kafka Data Source is the streaming data source for Apache Kafka in Spark Structured Streaming.

Kafka Data Source can be used as a streaming source and a streaming sink in micro-batch and continuous stream processing.

spark-sql-kafka-0-10 External Module

Kafka Data Source is part of the spark-sql-kafka-0-10 external module that is distributed with the official distribution of Apache Spark, but it is not included in the CLASSPATH by default.

You should define spark-sql-kafka-0-10 module as part of the build definition in your Spark project, e.g. as a libraryDependency in build.sbt for sbt:

libraryDependencies += "org.apache.spark" %% "spark-sql-kafka-0-10" % "{{ book.version }}"

For Spark environments like spark-submit (and "derivatives" like spark-shell), you should use --packages command-line option:

./bin/spark-shell --packages org.apache.spark:spark-sql-kafka-0-10_2.12:{{ book.version }}

Note	Replace the version of spark-sql-kafka-0-10 module (e.g. {{ book.version }} above) with one of the available versions found at The Central Repository’s Search that matches your version of Apache Spark.

Streaming Source

With spark-sql-kafka-0-10 module you can use kafka data source format for reading records from one or more Kafka topics as a streaming Dataset.

val records = spark
  .readStream
  .format("kafka")
  .option("subscribePattern", """topic-\d{2}""") // topics with two digits at the end
  .option("kafka.bootstrap.servers", ":9092")
  .load

Internally, the kafka data source format for reading is available through KafkaSourceProvider that is a MicroBatchReadSupport and ContinuousReadSupport for micro-batch and continuous stream processing, respectively.

Predefined (Fixed) Schema

Kafka Data Source uses a predefined (fixed) schema.

Table 1. Kafka Data Source’s Fixed Schema (in the positional order)

Name	Type
key	BinaryType
value	BinaryType
topic	StringType
partition	IntegerType
offset	LongType
timestamp	TimestampType
timestampType	IntegerType

scala> records.printSchema
root
 |-- key: binary (nullable = true)
 |-- value: binary (nullable = true)
 |-- topic: string (nullable = true)
 |-- partition: integer (nullable = true)
 |-- offset: long (nullable = true)
 |-- timestamp: timestamp (nullable = true)
 |-- timestampType: integer (nullable = true)

Internally, the fixed schema is defined as part of the DataSourceReader contract through MicroBatchReader and ContinuousReader extension contracts for micro-batch and continuous stream processing, respectively.

Tip	Read up on DataSourceReader in The Internals of Spark SQL book.

Tip	Use Column.cast operator to cast BinaryType to a StringType (for key and value columns). scala> :type records org.apache.spark.sql.DataFrame val values = records .select($"value" cast "string") // deserializing values scala> values.printSchema root |-- value: string (nullable = true)

Streaming Sink

With spark-sql-kafka-0-10 module you can use kafka data source format for writing the result of executing a streaming query (a streaming Dataset) to one or more Kafka topics.

val sq = records
  .writeStream
  .format("kafka")
  .option("kafka.bootstrap.servers", ":9092")
  .option("topic", "kafka2console-output")
  .option("checkpointLocation", "checkpointLocation-kafka2console")
  .start

Internally, the kafka data source format for writing is available through KafkaSourceProvider that is a StreamWriteSupport.

Micro-Batch Stream Processing

Kafka Data Source supports Micro-Batch Stream Processing (i.e. Trigger.Once and Trigger.ProcessingTime triggers) via KafkaMicroBatchReader.

import org.apache.spark.sql.streaming.Trigger
import scala.concurrent.duration._
val sq = spark
  .readStream
  .format("kafka")
  .option("subscribepattern", "kafka2console.*")
  .option("kafka.bootstrap.servers", ":9092")
  .load
  .withColumn("value", $"value" cast "string") // deserializing values
  .writeStream
  .format("console")
  .option("truncate", false) // format-specific option
  .option("checkpointLocation", "checkpointLocation-kafka2console") // generic query option
  .trigger(Trigger.ProcessingTime(30.seconds))
  .queryName("kafka2console-microbatch")
  .start

// In the end, stop the streaming query
sq.stop

Kafka Data Source can assign a single task per Kafka partition (using KafkaOffsetRangeCalculator in Micro-Batch Stream Processing).

Kafka Data Source can reuse a Kafka consumer (using KafkaMicroBatchReader in Micro-Batch Stream Processing).

Continuous Stream Processing

Kafka Data Source supports Continuous Stream Processing (i.e. Trigger.Continuous trigger) via KafkaContinuousReader.

import org.apache.spark.sql.streaming.Trigger
import scala.concurrent.duration._
val sq = spark
  .readStream
  .format("kafka")
  .option("subscribepattern", "kafka2console.*")
  .option("kafka.bootstrap.servers", ":9092")
  .load
  .withColumn("value", $"value" cast "string") // convert bytes to string for display purposes
  .writeStream
  .format("console")
  .option("truncate", false) // format-specific option
  .option("checkpointLocation", "checkpointLocation-kafka2console") // generic query option
  .queryName("kafka2console-continuous")
  .trigger(Trigger.Continuous(10.seconds))
  .start

// In the end, stop the streaming query
sq.stop

Configuration Options

Note	Options with kafka. prefix (e.g. kafka.bootstrap.servers) are considered configuration properties for the Kafka consumers used on the driver and executors.

Table 2. Kafka Data Source’s Options (Case-Insensitive)

Option	Description
assign	Topic subscription strategy that accepts a JSON with topic names and partitions, e.g. {"topicA":[0,1],"topicB":[0,1]} Note Exactly one topic subscription strategy is allowed (that KafkaSourceProvider validates before creating KafkaSource).
failOnDataLoss	Flag to control whether…​FIXME Default: true Used when KafkaSourceProvider is requested for failOnDataLoss configuration property
kafka.bootstrap.servers	(required) bootstrap.servers configuration property of the Kafka consumers used on the driver and executors Default: (empty)
kafkaConsumer.pollTimeoutMs	The time (in milliseconds) spent waiting in Consumer.poll if data is not available in the buffer. Default: spark.network.timeout or 120s Used when…​FIXME
maxOffsetsPerTrigger	Number of records to fetch per trigger (to limit the number of records to fetch). Default: (undefined) Unless defined, KafkaSource requests KafkaOffsetReader for the latest offsets.
minPartitions	Minimum number of partitions per executor (given Kafka partitions) Default: (undefined) Must be undefined (default) or greater than 0 When undefined (default) or smaller than the number of TopicPartitions with records to consume from, KafkaMicroBatchReader uses KafkaOffsetRangeCalculator to find the preferred executor for every TopicPartition (and the available executors).
startingOffsets	Starting offsets Default: latest Possible values: latest earliest JSON with topics, partitions and their starting offsets, e.g. {"topicA":{"part":offset,"p1":-1},"topicB":{"0":-2}} Tip Use Scala’s tripple quotes for the JSON for topics, partitions and offsets. option( "startingOffsets", """{"topic1":{"0":5,"4":-1},"topic2":{"0":-2}}""")
subscribe	Topic subscription strategy that accepts topic names as a comma-separated string, e.g. topic1,topic2,topic3 Note Exactly one topic subscription strategy is allowed (that KafkaSourceProvider validates before creating KafkaSource).
subscribepattern	Topic subscription strategy that uses Java’s java.util.regex.Pattern for the topic subscription regex pattern of topics to subscribe to, e.g. topic\d Tip Use Scala’s tripple quotes for the regular expression for topic subscription regex pattern. option("subscribepattern", """topic\d""") Note Exactly one topic subscription strategy is allowed (that KafkaSourceProvider validates before creating KafkaSource).
topic	Optional topic name to use for writing a streaming query Default: (empty) Unless defined, Kafka data source uses the topic names as defined in the topic field in the incoming data.

Logical Query Plan for Reading

When DataStreamReader is requested to load a dataset with kafka data source format, it creates a DataFrame with a StreamingRelationV2 leaf logical operator.

scala> records.explain(extended = true)
== Parsed Logical Plan ==
StreamingRelationV2 org.apache.spark.sql.kafka010.KafkaSourceProvider@1a366d0, kafka, Map(maxOffsetsPerTrigger -> 1, startingOffsets -> latest, subscribepattern -> topic\d, kafka.bootstrap.servers -> :9092), [key#7, value#8, topic#9, partition#10, offset#11L, timestamp#12, timestampType#13], StreamingRelation DataSource(org.apache.spark.sql.SparkSession@39b3de87,kafka,List(),None,List(),None,Map(maxOffsetsPerTrigger -> 1, startingOffsets -> latest, subscribepattern -> topic\d, kafka.bootstrap.servers -> :9092),None), kafka, [key#0, value#1, topic#2, partition#3, offset#4L, timestamp#5, timestampType#6]
...

Logical Query Plan for Writing

When DataStreamWriter is requested to start a streaming query with kafka data source format for writing, it requests the StreamingQueryManager to create a streaming query that in turn creates (a StreamingQueryWrapper with) a ContinuousExecution or a MicroBatchExecution for continuous and micro-batch stream processing, respectively.

scala> sq.explain(extended = true)
== Parsed Logical Plan ==
WriteToDataSourceV2 org.apache.spark.sql.execution.streaming.sources.MicroBatchWriter@bf98b73
+- Project [key#28 AS key#7, value#29 AS value#8, topic#30 AS topic#9, partition#31 AS partition#10, offset#32L AS offset#11L, timestamp#33 AS timestamp#12, timestampType#34 AS timestampType#13]
   +- Streaming RelationV2 kafka[key#28, value#29, topic#30, partition#31, offset#32L, timestamp#33, timestampType#34] (Options: [subscribepattern=kafka2console.*,kafka.bootstrap.servers=:9092])