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| <h1 class="title">Structured Streaming + Kafka Integration Guide (Kafka broker version 0.10.0 or higher)</h1> |
| |
| |
| <p>Structured Streaming integration for Kafka 0.10 to poll data from Kafka.</p> |
| |
| <h3 id="linking">Linking</h3> |
| <p>For Scala/Java applications using SBT/Maven project definitions, link your application with the following artifact:</p> |
| |
| <pre><code>groupId = org.apache.spark |
| artifactId = spark-sql-kafka-0-10_2.11 |
| version = 2.0.2 |
| </code></pre> |
| |
| <p>For Python applications, you need to add this above library and its dependencies when deploying your |
| application. See the <a href="#deploying">Deploying</a> subsection below.</p> |
| |
| <h3 id="creating-a-kafka-source-stream">Creating a Kafka Source Stream</h3> |
| |
| <div class="codetabs"> |
| <div data-lang="scala"> |
| |
| <pre><code>// Subscribe to 1 topic |
| val ds1 = spark |
| .readStream |
| .format("kafka") |
| .option("kafka.bootstrap.servers", "host1:port1,host2:port2") |
| .option("subscribe", "topic1") |
| .load() |
| ds1.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") |
| .as[(String, String)] |
| |
| // Subscribe to multiple topics |
| val ds2 = spark |
| .readStream |
| .format("kafka") |
| .option("kafka.bootstrap.servers", "host1:port1,host2:port2") |
| .option("subscribe", "topic1,topic2") |
| .load() |
| ds2.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") |
| .as[(String, String)] |
| |
| // Subscribe to a pattern |
| val ds3 = spark |
| .readStream |
| .format("kafka") |
| .option("kafka.bootstrap.servers", "host1:port1,host2:port2") |
| .option("subscribePattern", "topic.*") |
| .load() |
| ds3.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") |
| .as[(String, String)] |
| </code></pre> |
| |
| </div> |
| <div data-lang="java"> |
| |
| <pre><code>// Subscribe to 1 topic |
| Dataset<Row> ds1 = spark |
| .readStream() |
| .format("kafka") |
| .option("kafka.bootstrap.servers", "host1:port1,host2:port2") |
| .option("subscribe", "topic1") |
| .load() |
| ds1.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") |
| |
| // Subscribe to multiple topics |
| Dataset<Row> ds2 = spark |
| .readStream() |
| .format("kafka") |
| .option("kafka.bootstrap.servers", "host1:port1,host2:port2") |
| .option("subscribe", "topic1,topic2") |
| .load() |
| ds2.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") |
| |
| // Subscribe to a pattern |
| Dataset<Row> ds3 = spark |
| .readStream() |
| .format("kafka") |
| .option("kafka.bootstrap.servers", "host1:port1,host2:port2") |
| .option("subscribePattern", "topic.*") |
| .load() |
| ds3.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") |
| </code></pre> |
| |
| </div> |
| <div data-lang="python"> |
| |
| <pre><code># Subscribe to 1 topic |
| ds1 = spark |
| .readStream() |
| .format("kafka") |
| .option("kafka.bootstrap.servers", "host1:port1,host2:port2") |
| .option("subscribe", "topic1") |
| .load() |
| ds1.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") |
| |
| # Subscribe to multiple topics |
| ds2 = spark |
| .readStream |
| .format("kafka") |
| .option("kafka.bootstrap.servers", "host1:port1,host2:port2") |
| .option("subscribe", "topic1,topic2") |
| .load() |
| ds2.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") |
| |
| # Subscribe to a pattern |
| ds3 = spark |
| .readStream() |
| .format("kafka") |
| .option("kafka.bootstrap.servers", "host1:port1,host2:port2") |
| .option("subscribePattern", "topic.*") |
| .load() |
| ds3.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)") |
| </code></pre> |
| |
| </div> |
| </div> |
| |
| <p>Each row in the source has the following schema:</p> |
| <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> |
| |
| <p>The following options must be set for the Kafka source.</p> |
| |
| <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> |
| |
| <p>The following configurations are optional:</p> |
| |
| <table class="table"> |
| <tr><th>Option</th><th>value</th><th>default</th><th>meaning</th></tr> |
| <tr> |
| <td>startingOffsets</td> |
| <td>earliest, latest, or json string |
| {"topicA":{"0":23,"1":-1},"topicB":{"0":-2}} |
| </td> |
| <td>latest</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: This only applies when a new Streaming 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>failOnDataLoss</td> |
| <td>true or false</td> |
| <td>true</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.</td> |
| </tr> |
| <tr> |
| <td>kafkaConsumer.pollTimeoutMs</td> |
| <td>long</td> |
| <td>512</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>Number of times to retry before giving up fatch Kafka latest offsets.</td> |
| </tr> |
| <tr> |
| <td>fetchOffset.retryIntervalMs</td> |
| <td>long</td> |
| <td>10</td> |
| <td>milliseconds to wait before retrying to fetch Kafka offsets</td> |
| </tr> |
| <tr> |
| <td>maxOffsetsPerTrigger</td> |
| <td>long</td> |
| <td>none</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> |
| |
| <p>Kafka’s own configurations can be set via <code>DataStreamReader.option</code> with <code>kafka.</code> prefix, e.g, |
| <code>stream.option("kafka.bootstrap.servers", "host:port")</code>. For possible kafkaParams, see |
| <a href="http://kafka.apache.org/documentation.html#newconsumerconfigs">Kafka consumer config docs</a>.</p> |
| |
| <p>Note that the following Kafka params cannot be set and the Kafka source will throw an exception: |
| - <strong>group.id</strong>: Kafka source will create a unique group id for each query automatically. |
| - <strong>auto.offset.reset</strong>: Set the source option <code>startingOffsets</code> 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 when new |
| topics/partitions are dynamically subscribed. Note that <code>startingOffsets</code> only applies when a new |
| Streaming query is started, and that resuming will always pick up from where the query left off. |
| - <strong>key.deserializer</strong>: Keys are always deserialized as byte arrays with ByteArrayDeserializer. Use |
| DataFrame operations to explicitly deserialize the keys. |
| - <strong>value.deserializer</strong>: Values are always deserialized as byte arrays with ByteArrayDeserializer. |
| Use DataFrame operations to explicitly deserialize the values. |
| - <strong>enable.auto.commit</strong>: Kafka source doesn’t commit any offset. |
| - <strong>interceptor.classes</strong>: Kafka source always read keys and values as byte arrays. It’s not safe to |
| use ConsumerInterceptor as it may break the query.</p> |
| |
| <h3 id="deploying">Deploying</h3> |
| |
| <p>As with any Spark applications, <code>spark-submit</code> is used to launch your application. <code>spark-sql-kafka-0-10_2.11</code> |
| and its dependencies can be directly added to <code>spark-submit</code> using <code>--packages</code>, such as,</p> |
| |
| <pre><code>./bin/spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.0.2 ... |
| </code></pre> |
| |
| <p>See <a href="submitting-applications.html">Application Submission Guide</a> for more details about submitting |
| applications with external dependencies.</p> |
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