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[SPARK-17853][STREAMING][KAFKA][DOC] make it clear that reusing group.id is bad #15442

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[SPARK-17853][STREAMING][KAFKA][DOC] make it clear that reusing group.id is bad #15442

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7 changes: 5 additions & 2 deletions docs/streaming-kafka-0-10-integration.md
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Expand Up @@ -27,7 +27,7 @@ For Scala/Java applications using SBT/Maven project definitions, link your strea
"bootstrap.servers" -> "localhost:9092,anotherhost:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"group.id" -> "example",
"group.id" -> "use_a_separate_group_id_for_each_stream",
"auto.offset.reset" -> "latest",
"enable.auto.commit" -> (false: java.lang.Boolean)
)
Expand All @@ -48,7 +48,7 @@ Each item in the stream is a [ConsumerRecord](http://kafka.apache.org/0100/javad
</div>

For possible kafkaParams, see [Kafka consumer config docs](http://kafka.apache.org/documentation.html#newconsumerconfigs).
Note that enable.auto.commit is disabled, for discussion see [Storing Offsets](streaming-kafka-0-10-integration.html#storing-offsets) below.
Note that the example sets enable.auto.commit to false, for discussion see [Storing Offsets](streaming-kafka-0-10-integration.html#storing-offsets) below.

### LocationStrategies
The new Kafka consumer API will pre-fetch messages into buffers. Therefore it is important for performance reasons that the Spark integration keep cached consumers on executors (rather than recreating them for each batch), and prefer to schedule partitions on the host locations that have the appropriate consumers.
Expand All @@ -57,6 +57,9 @@ In most cases, you should use `LocationStrategies.PreferConsistent` as shown abo

The cache for consumers has a default maximum size of 64. If you expect to be handling more than (64 * number of executors) Kafka partitions, you can change this setting via `spark.streaming.kafka.consumer.cache.maxCapacity`

The cache is keyed by topicpartition and group.id, so use a **separate** `group.id` for each call to `createDirectStream`.


### ConsumerStrategies
The new Kafka consumer API has a number of different ways to specify topics, some of which require considerable post-object-instantiation setup. `ConsumerStrategies` provides an abstraction that allows Spark to obtain properly configured consumers even after restart from checkpoint.

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