# Structured Streaming + Kafka Integration Guide (Kafka broker version 0.10.0 or higher)

Structured Streaming integration for Kafka 0.10 to read data from and write data to Kafka.

For Scala/Java applications using SBT/Maven project definitions, link your application with the following artifact:

groupId = org.apache.spark
artifactId = spark-sql-kafka-0-10_2.11
version = 2.3.0


For Python applications, you need to add this above library and its dependencies when deploying your application. See the Deploying subsection below.

For experimenting on spark-shell, you need to add this above library and its dependencies too when invoking spark-shell. Also see the Deploying subsection below.

### Creating a Kafka Source for Batch Queries

If you have a use case that is better suited to batch processing, you can create a Dataset/DataFrame for a defined range of offsets.

Each row in the source has the following schema:

ColumnType
key binary
value binary
topic string
partition int
offset long
timestamp long
timestampType int

The following options must be set for the Kafka source for both batch and streaming queries.

Optionvaluemeaning
assign json string {"topicA":[0,1],"topicB":[2,4]} Specific TopicPartitions to consume. Only one of "assign", "subscribe" or "subscribePattern" options can be specified for Kafka source.
subscribe A comma-separated list of topics The topic list to subscribe. Only one of "assign", "subscribe" or "subscribePattern" options can be specified for Kafka source.
subscribePattern Java regex string The pattern used to subscribe to topic(s). Only one of "assign, "subscribe" or "subscribePattern" options can be specified for Kafka source.
kafka.bootstrap.servers A comma-separated list of host:port The Kafka "bootstrap.servers" configuration.

The following configurations are optional:

Optionvaluedefaultquery typemeaning
startingOffsets "earliest", "latest" (streaming only), or json string """ {"topicA":{"0":23,"1":-1},"topicB":{"0":-2}} """ "latest" for streaming, "earliest" for batch streaming and batch The start point when a query is started, either "earliest" which is from the earliest offsets, "latest" which is just from the latest offsets, or a json string specifying a starting offset for each TopicPartition. In the json, -2 as an offset can be used to refer to earliest, -1 to latest. Note: For batch queries, latest (either implicitly or by using -1 in json) is not allowed. For streaming queries, this only applies when a new query is started, and that resuming will always pick up from where the query left off. Newly discovered partitions during a query will start at earliest.
endingOffsets latest or json string {"topicA":{"0":23,"1":-1},"topicB":{"0":-1}} latest batch query The end point when a batch query is ended, either "latest" which is just referred to the latest, or a json string specifying an ending offset for each TopicPartition. In the json, -1 as an offset can be used to refer to latest, and -2 (earliest) as an offset is not allowed.
failOnDataLoss true or false true streaming query Whether to fail the query when it's possible that data is lost (e.g., topics are deleted, or offsets are out of range). This may be a false alarm. You can disable it when it doesn't work as you expected. Batch queries will always fail if it fails to read any data from the provided offsets due to lost data.
kafkaConsumer.pollTimeoutMs long 512 streaming and batch The timeout in milliseconds to poll data from Kafka in executors.
fetchOffset.numRetries int 3 streaming and batch Number of times to retry before giving up fetching Kafka offsets.
fetchOffset.retryIntervalMs long 10 streaming and batch milliseconds to wait before retrying to fetch Kafka offsets
maxOffsetsPerTrigger long none streaming and batch Rate limit on maximum number of offsets processed per trigger interval. The specified total number of offsets will be proportionally split across topicPartitions of different volume.

## Writing Data to Kafka

Here, we describe the support for writing Streaming Queries and Batch Queries to Apache Kafka. Take note that Apache Kafka only supports at least once write semantics. Consequently, when writing—either Streaming Queries or Batch Queries—to Kafka, some records may be duplicated; this can happen, for example, if Kafka needs to retry a message that was not acknowledged by a Broker, even though that Broker received and wrote the message record. Structured Streaming cannot prevent such duplicates from occurring due to these Kafka write semantics. However, if writing the query is successful, then you can assume that the query output was written at least once. A possible solution to remove duplicates when reading the written data could be to introduce a primary (unique) key that can be used to perform de-duplication when reading.

The Dataframe being written to Kafka should have the following columns in schema:

ColumnType
key (optional) string or binary
value (required) string or binary
topic (*optional) string

* The topic column is required if the “topic” configuration option is not specified.

The value column is the only required option. If a key column is not specified then a null valued key column will be automatically added (see Kafka semantics on how null valued key values are handled). If a topic column exists then its value is used as the topic when writing the given row to Kafka, unless the “topic” configuration option is set i.e., the “topic” configuration option overrides the topic column.

The following options must be set for the Kafka sink for both batch and streaming queries.

Optionvaluemeaning
kafka.bootstrap.servers A comma-separated list of host:port The Kafka "bootstrap.servers" configuration.

The following configurations are optional:

Optionvaluedefaultquery typemeaning
topic string none streaming and batch Sets the topic that all rows will be written to in Kafka. This option overrides any topic column that may exist in the data.

## Kafka Specific Configurations

Kafka’s own configurations can be set via DataStreamReader.option with kafka. prefix, e.g, stream.option("kafka.bootstrap.servers", "host:port"). For possible kafka parameters, see Kafka consumer config docs for parameters related to reading data, and Kafka producer config docs for parameters related to writing data.

Note that the following Kafka params cannot be set and the Kafka source or sink will throw an exception:

• group.id: Kafka source will create a unique group id for each query automatically.
• auto.offset.reset: Set the source option startingOffsets to specify where to start instead. Structured Streaming manages which offsets are consumed internally, rather than rely on the kafka Consumer 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.
• key.deserializer: Keys are always deserialized as byte arrays with ByteArrayDeserializer. Use DataFrame operations to explicitly deserialize the keys.
• value.deserializer: Values are always deserialized as byte arrays with ByteArrayDeserializer. Use DataFrame operations to explicitly deserialize the values.
• key.serializer: Keys are always serialized with ByteArraySerializer or StringSerializer. Use DataFrame operations to explicitly serialize the keys into either strings or byte arrays.
• value.serializer: values are always serialized with ByteArraySerializer or StringSerializer. Use DataFrame operations to explicitly serialize the values into either strings or byte arrays.
• enable.auto.commit: Kafka source doesn’t commit any offset.
• interceptor.classes: Kafka source always read keys and values as byte arrays. It’s not safe to use ConsumerInterceptor as it may break the query.

## Deploying

As with any Spark applications, spark-submit is used to launch your application. spark-sql-kafka-0-10_2.11 and its dependencies can be directly added to spark-submit using --packages, such as,

./bin/spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.3.0 ...


For experimenting on spark-shell, you can also use --packages to add spark-sql-kafka-0-10_2.11 and its dependencies directly,

./bin/spark-shell --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.3.0 ...


See Application Submission Guide for more details about submitting applications with external dependencies.