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Posted to commits@spark.apache.org by sr...@apache.org on 2016/10/30 09:32:29 UTC
spark git commit: [SPARK-16312][FOLLOW-UP][STREAMING][KAFKA][DOC] Add
java code snippet for Kafka 0.10 integration doc
Repository: spark
Updated Branches:
refs/heads/master d2d438d1d -> 505b927cb
[SPARK-16312][FOLLOW-UP][STREAMING][KAFKA][DOC] Add java code snippet for Kafka 0.10 integration doc
## What changes were proposed in this pull request?
added java code snippet for Kafka 0.10 integration doc
## How was this patch tested?
SKIP_API=1 jekyll build
## Screenshot
Author: Liwei Lin <lw...@gmail.com>
Closes #15679 from lw-lin/kafka-010-examples.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/505b927c
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/505b927c
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/505b927c
Branch: refs/heads/master
Commit: 505b927cb7ff037adb797b9c3b9ecac3f885b7c8
Parents: d2d438d
Author: Liwei Lin <lw...@gmail.com>
Authored: Sun Oct 30 09:32:19 2016 +0000
Committer: Sean Owen <so...@cloudera.com>
Committed: Sun Oct 30 09:32:19 2016 +0000
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docs/streaming-kafka-0-10-integration.md | 133 +++++++++++++++++++++++---
1 file changed, 122 insertions(+), 11 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/505b927c/docs/streaming-kafka-0-10-integration.md
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diff --git a/docs/streaming-kafka-0-10-integration.md b/docs/streaming-kafka-0-10-integration.md
index de95ea9..c1ef396 100644
--- a/docs/streaming-kafka-0-10-integration.md
+++ b/docs/streaming-kafka-0-10-integration.md
@@ -8,9 +8,9 @@ The Spark Streaming integration for Kafka 0.10 is similar in design to the 0.8 [
### Linking
For Scala/Java applications using SBT/Maven project definitions, link your streaming application with the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
- groupId = org.apache.spark
- artifactId = spark-streaming-kafka-0-10_{{site.SCALA_BINARY_VERSION}}
- version = {{site.SPARK_VERSION_SHORT}}
+ groupId = org.apache.spark
+ artifactId = spark-streaming-kafka-0-10_{{site.SCALA_BINARY_VERSION}}
+ version = {{site.SPARK_VERSION_SHORT}}
### Creating a Direct Stream
Note that the namespace for the import includes the version, org.apache.spark.streaming.kafka010
@@ -44,6 +44,42 @@ For Scala/Java applications using SBT/Maven project definitions, link your strea
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());
+ }
+ })
</div>
</div>
@@ -85,6 +121,20 @@ If you have a use case that is better suited to batch processing, you can create
</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()
+ );
</div>
</div>
@@ -103,6 +153,20 @@ Note that you cannot use `PreferBrokers`, because without the stream there is no
}
</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());
+ }
+ });
+ }
+ });
</div>
</div>
@@ -120,15 +184,24 @@ 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 offsets = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
+ val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
// some time later, after outputs have completed
- stream.asInstanceOf[CanCommitOffsets].commitAsync(offsets)
+ stream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
}
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);
+ }
+ });
</div>
</div>
@@ -141,7 +214,7 @@ For data stores that support transactions, saving offsets in the same transactio
// 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")
+ new TopicPartition(resultSet.string("topic"), resultSet.int("partition")) -> resultSet.long("offset")
}.toMap
val stream = KafkaUtils.createDirectStream[String, String](
@@ -155,16 +228,46 @@ For data stores that support transactions, saving offsets in the same transactio
val results = yourCalculation(rdd)
- yourTransactionBlock {
- // update results
+ // begin your transaction
- // update offsets where the end of existing offsets matches the beginning of this batch of offsets
+ // update results
+ // update offsets where the end of existing offsets matches the beginning of this batch of offsets
+ // assert that offsets were updated correctly
- // assert that offsets were updated correctly
- }
+ // end your transaction
}
</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
+ }
+ });
</div>
</div>
@@ -185,6 +288,14 @@ The new Kafka consumer [supports SSL](http://kafka.apache.org/documentation.html
)
</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");
</div>
</div>
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