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Diffstat (limited to 'docs/streaming-programming-guide.md')
| -rw-r--r-- | docs/streaming-programming-guide.md | 42 |
1 files changed, 20 insertions, 22 deletions
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 972b660262..b95f818d0f 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -136,7 +136,7 @@ The complete code can be found in the Spark Streaming example <div data-lang="java" markdown="1"> First, we create a -[JavaStreamingContext](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html) object, +[JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html) object, which is the main entry point for all streaming functionality. Besides Spark's configuration, we specify that any DStream would be processed in 1 second batches. @@ -215,7 +215,7 @@ jssc.awaitTermination(); // Wait for the computation to terminate {% endhighlight %} The complete code can be found in the Spark Streaming example -[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java). +[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/index.html?org/apache/spark/examples/streaming/JavaNetworkWordCount.java). <br> </div> @@ -360,12 +360,12 @@ new JavaStreamingContext(master, appName, batchInterval, [sparkHome], [jars]) </div> </div> -The `master` parameter is a standard [Spark cluster URL](scala-programming-guide.html#master-urls) +The `master` parameter is a standard [Spark cluster URL](programming-guide.html#master-urls) and can be "local" for local testing. The `appName` is a name of your program, which will be shown on your cluster's web UI. The `batchInterval` is the size of the batches, as explained earlier. Finally, the last two parameters are needed to deploy your code to a cluster if running in distributed mode, as described in the - [Spark programming guide](scala-programming-guide.html#deploying-code-on-a-cluster). + [Spark programming guide](programming-guide.html#deploying-code-on-a-cluster). Additionally, the underlying SparkContext can be accessed as `ssc.sparkContext`. @@ -813,10 +813,8 @@ output operators are defined: The complete list of DStream operations is available in the API documentation. For the Scala API, see [DStream](api/scala/index.html#org.apache.spark.streaming.dstream.DStream) and [PairDStreamFunctions](api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions). -For the Java API, see [JavaDStream](api/scala/index.html#org.apache.spark.streaming.api.java.dstream.DStream) -and [JavaPairDStream](api/scala/index.html#org.apache.spark.streaming.api.java.JavaPairDStream). -Specifically for the Java API, see [Spark's Java programming guide](java-programming-guide.html) -for more information. +For the Java API, see [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html) +and [JavaPairDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairDStream.html). ## Persistence Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is, @@ -833,7 +831,7 @@ default persistence level is set to replicate the data to two nodes for fault-to Note that, unlike RDDs, the default persistence level of DStreams keeps the data serialized in memory. This is further discussed in the [Performance Tuning](#memory-tuning) section. More information on different persistence levels can be found in -[Spark Programming Guide](scala-programming-guide.html#rdd-persistence). +[Spark Programming Guide](programming-guide.html#rdd-persistence). ## RDD Checkpointing A _stateful operation_ is one which operates over multiple batches of data. This includes all @@ -878,7 +876,7 @@ sending the data to two destinations (i.e., the earlier and upgraded application - The existing application is shutdown gracefully (see [`StreamingContext.stop(...)`](api/scala/index.html#org.apache.spark.streaming.StreamingContext) -or [`JavaStreamingContext.stop(...)`](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html) +or [`JavaStreamingContext.stop(...)`](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html) for graceful shutdown options) which ensure data that have been received is completely processed before shutdown. Then the upgraded application can be started, which will start processing from the same point where the earlier @@ -1313,10 +1311,10 @@ This section elaborates the steps required to migrate your existing code to 1.0. `FlumeUtils.createStream`, etc.) now returns [InputDStream](api/scala/index.html#org.apache.spark.streaming.dstream.InputDStream) / [ReceiverInputDStream](api/scala/index.html#org.apache.spark.streaming.dstream.ReceiverInputDStream) -(instead of DStream) for Scala, and [JavaInputDStream](api/java/org/apache/spark/streaming/api/java/JavaInputDStream.html) / -[JavaPairInputDStream](api/java/org/apache/spark/streaming/api/java/JavaPairInputDStream.html) / -[JavaReceiverInputDStream](api/java/org/apache/spark/streaming/api/java/JavaReceiverInputDStream.html) / -[JavaPairReceiverInputDStream](api/java/org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.html) +(instead of DStream) for Scala, and [JavaInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaInputDStream.html) / +[JavaPairInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairInputDStream.html) / +[JavaReceiverInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaReceiverInputDStream.html) / +[JavaPairReceiverInputDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.html) (instead of JavaDStream) for Java. This ensures that functionality specific to input streams can be added to these classes in the future without breaking binary compatibility. Note that your existing Spark Streaming applications should not require any change @@ -1367,14 +1365,14 @@ package and renamed for better clarity. [ZeroMQUtils](api/scala/index.html#org.apache.spark.streaming.zeromq.ZeroMQUtils$), and [MQTTUtils](api/scala/index.html#org.apache.spark.streaming.mqtt.MQTTUtils$) - Java docs - * [JavaStreamingContext](api/java/org/apache/spark/streaming/api/java/JavaStreamingContext.html), - [JavaDStream](api/java/org/apache/spark/streaming/api/java/JavaDStream.html) and - [PairJavaDStream](api/java/org/apache/spark/streaming/api/java/PairJavaDStream.html) - * [KafkaUtils](api/java/org/apache/spark/streaming/kafka/KafkaUtils.html), - [FlumeUtils](api/java/org/apache/spark/streaming/flume/FlumeUtils.html), - [TwitterUtils](api/java/org/apache/spark/streaming/twitter/TwitterUtils.html), - [ZeroMQUtils](api/java/org/apache/spark/streaming/zeromq/ZeroMQUtils.html), and - [MQTTUtils](api/java/org/apache/spark/streaming/mqtt/MQTTUtils.html) + * [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html), + [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html) and + [PairJavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/PairJavaDStream.html) + * [KafkaUtils](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html), + [FlumeUtils](api/java/index.html?org/apache/spark/streaming/flume/FlumeUtils.html), + [TwitterUtils](api/java/index.html?org/apache/spark/streaming/twitter/TwitterUtils.html), + [ZeroMQUtils](api/java/index.html?org/apache/spark/streaming/zeromq/ZeroMQUtils.html), and + [MQTTUtils](api/java/index.html?org/apache/spark/streaming/mqtt/MQTTUtils.html) * More examples in [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming) and [Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming) |