diff --git a/docs/streaming-kafka-0-10-integration.md b/docs/streaming-kafka-0-10-integration.md
index c1ef396907db790dc1ab7d712c5142fd90b72cac..b645d3c3a4b53ec1cd52780e4bebb1301ee02c84 100644
--- a/docs/streaming-kafka-0-10-integration.md
+++ b/docs/streaming-kafka-0-10-integration.md
@@ -17,69 +17,72 @@ For Scala/Java applications using SBT/Maven project definitions, link your strea
<div class="codetabs">
<div data-lang="scala" markdown="1">
- import org.apache.kafka.clients.consumer.ConsumerRecord
- import org.apache.kafka.common.serialization.StringDeserializer
- import org.apache.spark.streaming.kafka010._
- import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
- import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
-
- val kafkaParams = Map[String, Object](
- "bootstrap.servers" -> "localhost:9092,anotherhost:9092",
- "key.deserializer" -> classOf[StringDeserializer],
- "value.deserializer" -> classOf[StringDeserializer],
- "group.id" -> "use_a_separate_group_id_for_each_stream",
- "auto.offset.reset" -> "latest",
- "enable.auto.commit" -> (false: java.lang.Boolean)
- )
-
- val topics = Array("topicA", "topicB")
- val stream = KafkaUtils.createDirectStream[String, String](
- streamingContext,
- PreferConsistent,
- Subscribe[String, String](topics, kafkaParams)
- )
-
- stream.map(record => (record.key, record.value))
-
+{% highlight scala %}
+import org.apache.kafka.clients.consumer.ConsumerRecord
+import org.apache.kafka.common.serialization.StringDeserializer
+import org.apache.spark.streaming.kafka010._
+import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
+import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
+
+val kafkaParams = Map[String, Object](
+ "bootstrap.servers" -> "localhost:9092,anotherhost:9092",
+ "key.deserializer" -> classOf[StringDeserializer],
+ "value.deserializer" -> classOf[StringDeserializer],
+ "group.id" -> "use_a_separate_group_id_for_each_stream",
+ "auto.offset.reset" -> "latest",
+ "enable.auto.commit" -> (false: java.lang.Boolean)
+)
+
+val topics = Array("topicA", "topicB")
+val stream = KafkaUtils.createDirectStream[String, String](
+ streamingContext,
+ PreferConsistent,
+ Subscribe[String, String](topics, kafkaParams)
+)
+
+stream.map(record => (record.key, record.value))
+{% endhighlight %}
Each item in the stream is a [ConsumerRecord](http://kafka.apache.org/0100/javadoc/org/apache/kafka/clients/consumer/ConsumerRecord.html)
</div>
<div data-lang="java" markdown="1">
- import java.util.*;
- import org.apache.spark.SparkConf;
- import org.apache.spark.TaskContext;
- import org.apache.spark.api.java.*;
- import org.apache.spark.api.java.function.*;
- import org.apache.spark.streaming.api.java.*;
- import org.apache.spark.streaming.kafka010.*;
- import org.apache.kafka.clients.consumer.ConsumerRecord;
- import org.apache.kafka.common.TopicPartition;
- import org.apache.kafka.common.serialization.StringDeserializer;
- import scala.Tuple2;
-
- Map<String, Object> kafkaParams = new HashMap<>();
- kafkaParams.put("bootstrap.servers", "localhost:9092,anotherhost:9092");
- kafkaParams.put("key.deserializer", StringDeserializer.class);
- kafkaParams.put("value.deserializer", StringDeserializer.class);
- kafkaParams.put("group.id", "use_a_separate_group_id_for_each_stream");
- kafkaParams.put("auto.offset.reset", "latest");
- kafkaParams.put("enable.auto.commit", false);
-
- Collection<String> topics = Arrays.asList("topicA", "topicB");
-
- final JavaInputDStream<ConsumerRecord<String, String>> stream =
- KafkaUtils.createDirectStream(
- streamingContext,
- LocationStrategies.PreferConsistent(),
- ConsumerStrategies.<String, String>Subscribe(topics, kafkaParams)
- );
-
- stream.mapToPair(
- new PairFunction<ConsumerRecord<String, String>, String, String>() {
- @Override
- public Tuple2<String, String> call(ConsumerRecord<String, String> record) {
- return new Tuple2<>(record.key(), record.value());
- }
- })
+{% highlight java %}
+import java.util.*;
+import org.apache.spark.SparkConf;
+import org.apache.spark.TaskContext;
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.*;
+import org.apache.spark.streaming.api.java.*;
+import org.apache.spark.streaming.kafka010.*;
+import org.apache.kafka.clients.consumer.ConsumerRecord;
+import org.apache.kafka.common.TopicPartition;
+import org.apache.kafka.common.serialization.StringDeserializer;
+import scala.Tuple2;
+
+Map<String, Object> kafkaParams = new HashMap<>();
+kafkaParams.put("bootstrap.servers", "localhost:9092,anotherhost:9092");
+kafkaParams.put("key.deserializer", StringDeserializer.class);
+kafkaParams.put("value.deserializer", StringDeserializer.class);
+kafkaParams.put("group.id", "use_a_separate_group_id_for_each_stream");
+kafkaParams.put("auto.offset.reset", "latest");
+kafkaParams.put("enable.auto.commit", false);
+
+Collection<String> topics = Arrays.asList("topicA", "topicB");
+
+final JavaInputDStream<ConsumerRecord<String, String>> stream =
+ KafkaUtils.createDirectStream(
+ streamingContext,
+ LocationStrategies.PreferConsistent(),
+ ConsumerStrategies.<String, String>Subscribe(topics, kafkaParams)
+ );
+
+stream.mapToPair(
+ new PairFunction<ConsumerRecord<String, String>, String, String>() {
+ @Override
+ public Tuple2<String, String> call(ConsumerRecord<String, String> record) {
+ return new Tuple2<>(record.key(), record.value());
+ }
+ })
+{% endhighlight %}
</div>
</div>
@@ -109,32 +112,35 @@ If you have a use case that is better suited to batch processing, you can create
<div class="codetabs">
<div data-lang="scala" markdown="1">
- // Import dependencies and create kafka params as in Create Direct Stream above
-
- val offsetRanges = Array(
- // topic, partition, inclusive starting offset, exclusive ending offset
- OffsetRange("test", 0, 0, 100),
- OffsetRange("test", 1, 0, 100)
- )
+{% highlight scala %}
+// Import dependencies and create kafka params as in Create Direct Stream above
- val rdd = KafkaUtils.createRDD[String, String](sparkContext, kafkaParams, offsetRanges, PreferConsistent)
+val offsetRanges = Array(
+ // topic, partition, inclusive starting offset, exclusive ending offset
+ OffsetRange("test", 0, 0, 100),
+ OffsetRange("test", 1, 0, 100)
+)
+val rdd = KafkaUtils.createRDD[String, String](sparkContext, kafkaParams, offsetRanges, PreferConsistent)
+{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
- // Import dependencies and create kafka params as in Create Direct Stream above
-
- OffsetRange[] offsetRanges = {
- // topic, partition, inclusive starting offset, exclusive ending offset
- OffsetRange.create("test", 0, 0, 100),
- OffsetRange.create("test", 1, 0, 100)
- };
-
- JavaRDD<ConsumerRecord<String, String>> rdd = KafkaUtils.createRDD(
- sparkContext,
- kafkaParams,
- offsetRanges,
- LocationStrategies.PreferConsistent()
- );
+{% highlight java %}
+// Import dependencies and create kafka params as in Create Direct Stream above
+
+OffsetRange[] offsetRanges = {
+ // topic, partition, inclusive starting offset, exclusive ending offset
+ OffsetRange.create("test", 0, 0, 100),
+ OffsetRange.create("test", 1, 0, 100)
+};
+
+JavaRDD<ConsumerRecord<String, String>> rdd = KafkaUtils.createRDD(
+ sparkContext,
+ kafkaParams,
+ offsetRanges,
+ LocationStrategies.PreferConsistent()
+);
+{% endhighlight %}
</div>
</div>
@@ -144,29 +150,33 @@ Note that you cannot use `PreferBrokers`, because without the stream there is no
<div class="codetabs">
<div data-lang="scala" markdown="1">
- stream.foreachRDD { rdd =>
- val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
- rdd.foreachPartition { iter =>
- val o: OffsetRange = offsetRanges(TaskContext.get.partitionId)
- println(s"${o.topic} ${o.partition} ${o.fromOffset} ${o.untilOffset}")
- }
- }
+{% highlight scala %}
+stream.foreachRDD { rdd =>
+ val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
+ rdd.foreachPartition { iter =>
+ val o: OffsetRange = offsetRanges(TaskContext.get.partitionId)
+ println(s"${o.topic} ${o.partition} ${o.fromOffset} ${o.untilOffset}")
+ }
+}
+{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
- stream.foreachRDD(new VoidFunction<JavaRDD<ConsumerRecord<String, String>>>() {
- @Override
- public void call(JavaRDD<ConsumerRecord<String, String>> rdd) {
- final OffsetRange[] offsetRanges = ((HasOffsetRanges) rdd.rdd()).offsetRanges();
- rdd.foreachPartition(new VoidFunction<Iterator<ConsumerRecord<String, String>>>() {
- @Override
- public void call(Iterator<ConsumerRecord<String, String>> consumerRecords) {
- OffsetRange o = offsetRanges[TaskContext.get().partitionId()];
- System.out.println(
- o.topic() + " " + o.partition() + " " + o.fromOffset() + " " + o.untilOffset());
- }
- });
- }
- });
+{% highlight java %}
+stream.foreachRDD(new VoidFunction<JavaRDD<ConsumerRecord<String, String>>>() {
+ @Override
+ public void call(JavaRDD<ConsumerRecord<String, String>> rdd) {
+ final OffsetRange[] offsetRanges = ((HasOffsetRanges) rdd.rdd()).offsetRanges();
+ rdd.foreachPartition(new VoidFunction<Iterator<ConsumerRecord<String, String>>>() {
+ @Override
+ public void call(Iterator<ConsumerRecord<String, String>> consumerRecords) {
+ OffsetRange o = offsetRanges[TaskContext.get().partitionId()];
+ System.out.println(
+ o.topic() + " " + o.partition() + " " + o.fromOffset() + " " + o.untilOffset());
+ }
+ });
+ }
+});
+{% endhighlight %}
</div>
</div>
@@ -183,25 +193,28 @@ Kafka has an offset commit API that stores offsets in a special Kafka topic. By
<div class="codetabs">
<div data-lang="scala" markdown="1">
- stream.foreachRDD { rdd =>
- val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
-
- // some time later, after outputs have completed
- stream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
- }
-
+{% highlight scala %}
+stream.foreachRDD { rdd =>
+ val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
+
+ // some time later, after outputs have completed
+ stream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
+}
+{% endhighlight %}
As with HasOffsetRanges, the cast to CanCommitOffsets will only succeed if called on the result of createDirectStream, not after transformations. The commitAsync call is threadsafe, but must occur after outputs if you want meaningful semantics.
</div>
<div data-lang="java" markdown="1">
- stream.foreachRDD(new VoidFunction<JavaRDD<ConsumerRecord<String, String>>>() {
- @Override
- public void call(JavaRDD<ConsumerRecord<String, String>> rdd) {
- OffsetRange[] offsetRanges = ((HasOffsetRanges) rdd.rdd()).offsetRanges();
-
- // some time later, after outputs have completed
- ((CanCommitOffsets) stream.inputDStream()).commitAsync(offsetRanges);
- }
- });
+{% highlight java %}
+stream.foreachRDD(new VoidFunction<JavaRDD<ConsumerRecord<String, String>>>() {
+ @Override
+ public void call(JavaRDD<ConsumerRecord<String, String>> rdd) {
+ OffsetRange[] offsetRanges = ((HasOffsetRanges) rdd.rdd()).offsetRanges();
+
+ // some time later, after outputs have completed
+ ((CanCommitOffsets) stream.inputDStream()).commitAsync(offsetRanges);
+ }
+});
+{% endhighlight %}
</div>
</div>
@@ -210,64 +223,68 @@ For data stores that support transactions, saving offsets in the same transactio
<div class="codetabs">
<div data-lang="scala" markdown="1">
- // The details depend on your data store, but the general idea looks like this
+{% highlight scala %}
+// The details depend on your data store, but the general idea looks like this
- // begin from the the offsets committed to the database
- val fromOffsets = selectOffsetsFromYourDatabase.map { resultSet =>
- new TopicPartition(resultSet.string("topic"), resultSet.int("partition")) -> resultSet.long("offset")
- }.toMap
+// begin from the the offsets committed to the database
+val fromOffsets = selectOffsetsFromYourDatabase.map { resultSet =>
+ new TopicPartition(resultSet.string("topic"), resultSet.int("partition")) -> resultSet.long("offset")
+}.toMap
- val stream = KafkaUtils.createDirectStream[String, String](
- streamingContext,
- PreferConsistent,
- Assign[String, String](fromOffsets.keys.toList, kafkaParams, fromOffsets)
- )
+val stream = KafkaUtils.createDirectStream[String, String](
+ streamingContext,
+ PreferConsistent,
+ Assign[String, String](fromOffsets.keys.toList, kafkaParams, fromOffsets)
+)
- stream.foreachRDD { rdd =>
- val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
+stream.foreachRDD { rdd =>
+ val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
- val results = yourCalculation(rdd)
+ val results = yourCalculation(rdd)
- // begin your transaction
+ // begin your transaction
- // update results
- // update offsets where the end of existing offsets matches the beginning of this batch of offsets
- // assert that offsets were updated correctly
+ // update results
+ // update offsets where the end of existing offsets matches the beginning of this batch of offsets
+ // assert that offsets were updated correctly
- // end your transaction
- }
+ // end your transaction
+}
+{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
- // The details depend on your data store, but the general idea looks like this
-
- // begin from the the offsets committed to the database
- Map<TopicPartition, Long> fromOffsets = new HashMap<>();
- for (resultSet : selectOffsetsFromYourDatabase)
- fromOffsets.put(new TopicPartition(resultSet.string("topic"), resultSet.int("partition")), resultSet.long("offset"));
- }
-
- JavaInputDStream<ConsumerRecord<String, String>> stream = KafkaUtils.createDirectStream(
- streamingContext,
- LocationStrategies.PreferConsistent(),
- ConsumerStrategies.<String, String>Assign(fromOffsets.keySet(), kafkaParams, fromOffsets)
- );
-
- stream.foreachRDD(new VoidFunction<JavaRDD<ConsumerRecord<String, String>>>() {
- @Override
- public void call(JavaRDD<ConsumerRecord<String, String>> rdd) {
- OffsetRange[] offsetRanges = ((HasOffsetRanges) rdd.rdd()).offsetRanges();
-
- Object results = yourCalculation(rdd);
-
- // begin your transaction
-
- // update results
- // update offsets where the end of existing offsets matches the beginning of this batch of offsets
- // assert that offsets were updated correctly
-
- // end your transaction
- }
- });
+{% highlight java %}
+// The details depend on your data store, but the general idea looks like this
+
+// begin from the the offsets committed to the database
+Map<TopicPartition, Long> fromOffsets = new HashMap<>();
+for (resultSet : selectOffsetsFromYourDatabase)
+ fromOffsets.put(new TopicPartition(resultSet.string("topic"), resultSet.int("partition")), resultSet.long("offset"));
+}
+
+JavaInputDStream<ConsumerRecord<String, String>> stream = KafkaUtils.createDirectStream(
+ streamingContext,
+ LocationStrategies.PreferConsistent(),
+ ConsumerStrategies.<String, String>Assign(fromOffsets.keySet(), kafkaParams, fromOffsets)
+);
+
+stream.foreachRDD(new VoidFunction<JavaRDD<ConsumerRecord<String, String>>>() {
+ @Override
+ public void call(JavaRDD<ConsumerRecord<String, String>> rdd) {
+ OffsetRange[] offsetRanges = ((HasOffsetRanges) rdd.rdd()).offsetRanges();
+
+ Object results = yourCalculation(rdd);
+
+ // begin your transaction
+
+ // update results
+ // update offsets where the end of existing offsets matches the beginning of this batch of offsets
+ // assert that offsets were updated correctly
+
+ // end your transaction
+ }
+});
+{% endhighlight %}
</div>
</div>
@@ -277,25 +294,29 @@ The new Kafka consumer [supports SSL](http://kafka.apache.org/documentation.html
<div class="codetabs">
<div data-lang="scala" markdown="1">
- val kafkaParams = Map[String, Object](
- // the usual params, make sure to change the port in bootstrap.servers if 9092 is not TLS
- "security.protocol" -> "SSL",
- "ssl.truststore.location" -> "/some-directory/kafka.client.truststore.jks",
- "ssl.truststore.password" -> "test1234",
- "ssl.keystore.location" -> "/some-directory/kafka.client.keystore.jks",
- "ssl.keystore.password" -> "test1234",
- "ssl.key.password" -> "test1234"
- )
+{% highlight scala %}
+val kafkaParams = Map[String, Object](
+ // the usual params, make sure to change the port in bootstrap.servers if 9092 is not TLS
+ "security.protocol" -> "SSL",
+ "ssl.truststore.location" -> "/some-directory/kafka.client.truststore.jks",
+ "ssl.truststore.password" -> "test1234",
+ "ssl.keystore.location" -> "/some-directory/kafka.client.keystore.jks",
+ "ssl.keystore.password" -> "test1234",
+ "ssl.key.password" -> "test1234"
+)
+{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
- Map<String, Object> kafkaParams = new HashMap<String, Object>();
- // the usual params, make sure to change the port in bootstrap.servers if 9092 is not TLS
- kafkaParams.put("security.protocol", "SSL");
- kafkaParams.put("ssl.truststore.location", "/some-directory/kafka.client.truststore.jks");
- kafkaParams.put("ssl.truststore.password", "test1234");
- kafkaParams.put("ssl.keystore.location", "/some-directory/kafka.client.keystore.jks");
- kafkaParams.put("ssl.keystore.password", "test1234");
- kafkaParams.put("ssl.key.password", "test1234");
+{% highlight java %}
+Map<String, Object> kafkaParams = new HashMap<String, Object>();
+// the usual params, make sure to change the port in bootstrap.servers if 9092 is not TLS
+kafkaParams.put("security.protocol", "SSL");
+kafkaParams.put("ssl.truststore.location", "/some-directory/kafka.client.truststore.jks");
+kafkaParams.put("ssl.truststore.password", "test1234");
+kafkaParams.put("ssl.keystore.location", "/some-directory/kafka.client.keystore.jks");
+kafkaParams.put("ssl.keystore.password", "test1234");
+kafkaParams.put("ssl.key.password", "test1234");
+{% endhighlight %}
</div>
</div>
diff --git a/docs/structured-streaming-kafka-integration.md b/docs/structured-streaming-kafka-integration.md
index a6c3b3a9024d81c1fd0d66df6f086e3a3f5aa68e..c4c9fb3f7d3db84b39d7b82f2744d2a03fc191a1 100644
--- a/docs/structured-streaming-kafka-integration.md
+++ b/docs/structured-streaming-kafka-integration.md
@@ -19,97 +19,103 @@ application. See the [Deploying](#deploying) subsection below.
<div class="codetabs">
<div data-lang="scala" markdown="1">
+{% highlight scala %}
- // 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 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 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)]
+// 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)]
+{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
+{% highlight java %}
- // 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 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 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)")
+// 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)")
+{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
+{% highlight python %}
- # 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 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 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)")
+# 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)")
+{% endhighlight %}
</div>
</div>