---
layout: global
title: 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.
## Linking
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_{{site.SCALA_BINARY_VERSION}}
version = {{site.SPARK_VERSION_SHORT}}
For Python applications, you need to add this above library and its dependencies when deploying your
application. See the [Deploying](#deploying) subsection below.
## Reading Data from Kafka
### Creating a Kafka Source for Streaming Queries
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
// Subscribe to 1 topic
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
// Subscribe to multiple topics
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1,topic2")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
// Subscribe to a pattern
val df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribePattern", "topic.*")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
// Subscribe to 1 topic
DataFrame<Row> df = spark
.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
// Subscribe to multiple topics
DataFrame<Row> df = spark
.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1,topic2")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
// Subscribe to a pattern
DataFrame<Row> df = spark
.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribePattern", "topic.*")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
# Subscribe to 1 topic
df = spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.option("subscribe", "topic1") \
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
# Subscribe to multiple topics
df = spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.option("subscribe", "topic1,topic2") \
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
# Subscribe to a pattern
df = spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.option("subscribePattern", "topic.*") \
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
{% endhighlight %}
</div>
</div>
### Creating a Kafka Source for Batch Queries
If you have a use case that is better suited to batch processing,
you can create an Dataset/DataFrame for a defined range of offsets.
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
// Subscribe to 1 topic defaults to the earliest and latest offsets
val df = spark
.read
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
// Subscribe to multiple topics, specifying explicit Kafka offsets
val df = spark
.read
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1,topic2")
.option("startingOffsets", """{"topic1":{"0":23,"1":-2},"topic2":{"0":-2}}""")
.option("endingOffsets", """{"topic1":{"0":50,"1":-1},"topic2":{"0":-1}}""")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
// Subscribe to a pattern, at the earliest and latest offsets
val df = spark
.read
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribePattern", "topic.*")
.option("startingOffsets", "earliest")
.option("endingOffsets", "latest")
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.as[(String, String)]
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
// Subscribe to 1 topic defaults to the earliest and latest offsets
DataFrame<Row> df = spark
.read()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1")
.load();
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)");
// Subscribe to multiple topics, specifying explicit Kafka offsets
DataFrame<Row> df = spark
.read()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribe", "topic1,topic2")
.option("startingOffsets", "{\"topic1\":{\"0\":23,\"1\":-2},\"topic2\":{\"0\":-2}}")
.option("endingOffsets", "{\"topic1\":{\"0\":50,\"1\":-1},\"topic2\":{\"0\":-1}}")
.load();
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)");
// Subscribe to a pattern, at the earliest and latest offsets
DataFrame<Row> df = spark
.read()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("subscribePattern", "topic.*")
.option("startingOffsets", "earliest")
.option("endingOffsets", "latest")
.load();
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)");
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
# Subscribe to 1 topic defaults to the earliest and latest offsets
df = spark \
.read \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.option("subscribe", "topic1") \
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
# Subscribe to multiple topics, specifying explicit Kafka offsets
df = spark \
.read \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.option("subscribe", "topic1,topic2") \
.option("startingOffsets", """{"topic1":{"0":23,"1":-2},"topic2":{"0":-2}}""") \
.option("endingOffsets", """{"topic1":{"0":50,"1":-1},"topic2":{"0":-1}}""") \
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
# Subscribe to a pattern, at the earliest and latest offsets
df = spark \
.read \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.option("subscribePattern", "topic.*") \
.option("startingOffsets", "earliest") \
.option("endingOffsets", "latest") \
.load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
{% endhighlight %}
</div>
</div>
Each row in the source has the following schema:
<table class="table">
<tr><th>Column</th><th>Type</th></tr>
<tr>
<td>key</td>
<td>binary</td>
</tr>
<tr>
<td>value</td>
<td>binary</td>
</tr>
<tr>
<td>topic</td>
<td>string</td>
</tr>
<tr>
<td>partition</td>
<td>int</td>
</tr>
<tr>
<td>offset</td>
<td>long</td>
</tr>
<tr>
<td>timestamp</td>
<td>long</td>
</tr>
<tr>
<td>timestampType</td>
<td>int</td>
</tr>
</table>
The following options must be set for the Kafka source
for both batch and streaming queries.
<table class="table">
<tr><th>Option</th><th>value</th><th>meaning</th></tr>
<tr>
<td>assign</td>
<td>json string {"topicA":[0,1],"topicB":[2,4]}</td>
<td>Specific TopicPartitions to consume.
Only one of "assign", "subscribe" or "subscribePattern"
options can be specified for Kafka source.</td>
</tr>
<tr>
<td>subscribe</td>
<td>A comma-separated list of topics</td>
<td>The topic list to subscribe.
Only one of "assign", "subscribe" or "subscribePattern"
options can be specified for Kafka source.</td>
</tr>
<tr>
<td>subscribePattern</td>
<td>Java regex string</td>
<td>The pattern used to subscribe to topic(s).
Only one of "assign, "subscribe" or "subscribePattern"
options can be specified for Kafka source.</td>
</tr>
<tr>
<td>kafka.bootstrap.servers</td>
<td>A comma-separated list of host:port</td>
<td>The Kafka "bootstrap.servers" configuration.</td>
</tr>
</table>
The following configurations are optional:
<table class="table">
<tr><th>Option</th><th>value</th><th>default</th><th>query type</th><th>meaning</th></tr>
<tr>
<td>startingOffsets</td>
<td>"earliest", "latest" (streaming only), or json string
""" {"topicA":{"0":23,"1":-1},"topicB":{"0":-2}} """
</td>
<td>"latest" for streaming, "earliest" for batch</td>
<td>streaming and batch</td>
<td>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.</td>
</tr>
<tr>
<td>endingOffsets</td>
<td>latest or json string
{"topicA":{"0":23,"1":-1},"topicB":{"0":-1}}
</td>
<td>latest</td>
<td>batch query</td>
<td>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.</td>
</tr>
<tr>
<td>failOnDataLoss</td>
<td>true or false</td>
<td>true</td>
<td>streaming query</td>
<td>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.</td>
</tr>
<tr>
<td>kafkaConsumer.pollTimeoutMs</td>
<td>long</td>
<td>512</td>
<td>streaming and batch</td>
<td>The timeout in milliseconds to poll data from Kafka in executors.</td>
</tr>
<tr>
<td>fetchOffset.numRetries</td>
<td>int</td>
<td>3</td>
<td>streaming and batch</td>
<td>Number of times to retry before giving up fetching Kafka offsets.</td>
</tr>
<tr>
<td>fetchOffset.retryIntervalMs</td>
<td>long</td>
<td>10</td>
<td>streaming and batch</td>
<td>milliseconds to wait before retrying to fetch Kafka offsets</td>
</tr>
<tr>
<td>maxOffsetsPerTrigger</td>
<td>long</td>
<td>none</td>
<td>streaming and batch</td>
<td>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.</td>
</tr>
</table>
## 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:
<table class="table">
<tr><th>Column</th><th>Type</th></tr>
<tr>
<td>key (optional)</td>
<td>string or binary</td>
</tr>
<tr>
<td>value (required)</td>
<td>string or binary</td>
</tr>
<tr>
<td>topic (*optional)</td>
<td>string</td>
</tr>
</table>
\* The topic column is required if the "topic" configuration option is not specified.<br>
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.
<table class="table">
<tr><th>Option</th><th>value</th><th>meaning</th></tr>
<tr>
<td>kafka.bootstrap.servers</td>
<td>A comma-separated list of host:port</td>
<td>The Kafka "bootstrap.servers" configuration.</td>
</tr>
</table>
The following configurations are optional:
<table class="table">
<tr><th>Option</th><th>value</th><th>default</th><th>query type</th><th>meaning</th></tr>
<tr>
<td>topic</td>
<td>string</td>
<td>none</td>
<td>streaming and batch</td>
<td>Sets the topic that all rows will be written to in Kafka. This option overrides any
topic column that may exist in the data.</td>
</tr>
</table>
### Creating a Kafka Sink for Streaming Queries
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
// Write key-value data from a DataFrame to a specific Kafka topic specified in an option
val ds = df
.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.writeStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("topic", "topic1")
.start()
// Write key-value data from a DataFrame to Kafka using a topic specified in the data
val ds = df
.selectExpr("topic", "CAST(key AS STRING)", "CAST(value AS STRING)")
.writeStream
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.start()
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
// Write key-value data from a DataFrame to a specific Kafka topic specified in an option
StreamingQuery ds = df
.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.writeStream()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("topic", "topic1")
.start()
// Write key-value data from a DataFrame to Kafka using a topic specified in the data
StreamingQuery ds = df
.selectExpr("topic", "CAST(key AS STRING)", "CAST(value AS STRING)")
.writeStream()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.start()
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
# Write key-value data from a DataFrame to a specific Kafka topic specified in an option
ds = df \
.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") \
.writeStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.option("topic", "topic1") \
.start()
# Write key-value data from a DataFrame to Kafka using a topic specified in the data
ds = df \
.selectExpr("topic", "CAST(key AS STRING)", "CAST(value AS STRING)") \
.writeStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.start()
{% endhighlight %}
</div>
</div>
### Writing the output of Batch Queries to Kafka
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
// Write key-value data from a DataFrame to a specific Kafka topic specified in an option
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.write
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("topic", "topic1")
.save()
// Write key-value data from a DataFrame to Kafka using a topic specified in the data
df.selectExpr("topic", "CAST(key AS STRING)", "CAST(value AS STRING)")
.write
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.save()
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
// Write key-value data from a DataFrame to a specific Kafka topic specified in an option
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
.write()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.option("topic", "topic1")
.save()
// Write key-value data from a DataFrame to Kafka using a topic specified in the data
df.selectExpr("topic", "CAST(key AS STRING)", "CAST(value AS STRING)")
.write()
.format("kafka")
.option("kafka.bootstrap.servers", "host1:port1,host2:port2")
.save()
{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
# Write key-value data from a DataFrame to a specific Kafka topic specified in an option
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") \
.write \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.option("topic", "topic1") \
.save()
# Write key-value data from a DataFrame to Kafka using a topic specified in the data
df.selectExpr("topic", "CAST(key AS STRING)", "CAST(value AS STRING)") \
.write \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.save()
{% endhighlight %}
</div>
</div>
## 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](http://kafka.apache.org/documentation.html#newconsumerconfigs) for
parameters related to reading data, and [Kafka producer config docs](http://kafka.apache.org/documentation/#producerconfigs)
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 oeprations 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_{{site.SCALA_BINARY_VERSION}}`
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_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}} ...
See [Application Submission Guide](submitting-applications.html) for more details about submitting
applications with external dependencies.
