Kafka Data Source¶

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

Kafka Data Source provides a streaming source and a streaming sink for micro-batch and continuous stream processing.

Kafka Data Source supports options.

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 on 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" % "3.3.1"

spark-submit¶

For Spark environments like spark-submit (and "derivatives" like spark-shell), you should use --packages command-line option to "install" the package.

./bin/spark-shell \
  --packages org.apache.spark:spark-sql-kafka-0-10_2.13:3.3.1

Note

Replace the version of spark-sql-kafka-0-10 module (e.g. 3.3.1 above) with one of the available versions found at The Central Repository's Search that matches your version of Apache Spark.

Streaming Source¶

Kafka data source can load streaming data (reading records) from one or more Kafka topics.

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

Kafka data source supports many options for reading.

Kafka data source for reading is available through KafkaSourceProvider that is a MicroBatchStream and ContinuousReadSupport for micro-batch and continuous stream processing, respectively.

Predefined (Fixed) Schema¶

Kafka Data Source uses a predefined (fixed) schema.

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)

Column.cast Operator¶

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¶

Kafka data source can write streaming data (the result of executing a streaming query) 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.

Micro-Batch Stream Processing¶

Kafka Data Source supports Micro-Batch Stream Processing.

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 in Micro-Batch Stream Processing).

Continuous Stream Processing¶

Kafka Data Source supports Continuous Stream Processing.

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

Trigger.AvailableNow¶

Kafka data source supports Trigger.AvailableNow mode (as KafkaMicroBatchStream is a SupportsTriggerAvailableNow).

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.MicroBatchWrite@42363db7
+- 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])

Demo: Streaming Aggregation with Kafka Data Source¶

Check out Demo: Streaming Aggregation with Kafka Data Source.

Use the following to publish events to Kafka.

// 1st streaming batch
$ cat /tmp/1
1,1,1
15,2,1

$ kafkacat -P -b localhost:9092 -t topic1 -l /tmp/1

// Alternatively (and slower due to JVM bootup)
$ cat /tmp/1 | ./bin/kafka-console-producer.sh --topic topic1 --broker-list localhost:9092