diff options
| -rw-r--r-- | docs/streaming-kafka-integration.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md index 64714f0b79..d6d5605948 100644 --- a/docs/streaming-kafka-integration.md +++ b/docs/streaming-kafka-integration.md @@ -29,7 +29,7 @@ Next, we discuss how to use this approach in your streaming application. [ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume]) You can also specify the key and value classes and their corresponding decoder classes using variations of `createStream`. See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$) - and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala). + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala). </div> <div data-lang="java" markdown="1"> import org.apache.spark.streaming.kafka.*; @@ -39,7 +39,7 @@ Next, we discuss how to use this approach in your streaming application. [ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume]); You can also specify the key and value classes and their corresponding decoder classes using variations of `createStream`. See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html) - and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java). + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java). </div> <div data-lang="python" markdown="1"> @@ -105,7 +105,7 @@ Next, we discuss how to use this approach in your streaming application. streamingContext, [map of Kafka parameters], [set of topics to consume]) See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$) - and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala). + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala). </div> <div data-lang="java" markdown="1"> import org.apache.spark.streaming.kafka.*; @@ -116,7 +116,7 @@ Next, we discuss how to use this approach in your streaming application. [map of Kafka parameters], [set of topics to consume]); See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html) - and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/scala-2.10/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java). + and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java). </div> </div> @@ -153,4 +153,4 @@ Next, we discuss how to use this approach in your streaming application. Another thing to note is that since this approach does not use Receivers, the standard receiver-related (that is, [configurations](configuration.html) of the form `spark.streaming.receiver.*` ) will not apply to the input DStreams created by this approach (will apply to other input DStreams though). Instead, use the [configurations](configuration.html) `spark.streaming.kafka.*`. An important one is `spark.streaming.kafka.maxRatePerPartition` which is the maximum rate at which each Kafka partition will be read by this direct API. -3. **Deploying:** Similar to the first approach, you can package `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` and its dependencies into the application JAR and the launch the application using `spark-submit`. Make sure `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` are marked as `provided` dependencies as those are already present in a Spark installation.
\ No newline at end of file +3. **Deploying:** Similar to the first approach, you can package `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` and its dependencies into the application JAR and the launch the application using `spark-submit`. Make sure `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` are marked as `provided` dependencies as those are already present in a Spark installation. |