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| -rw-r--r-- | docs/streaming-kafka-integration.md | 2 | ||||
| -rw-r--r-- | docs/streaming-programming-guide.md | 4 |
2 files changed, 3 insertions, 3 deletions
diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md index 0f1e32212e..e0d3f4f69b 100644 --- a/docs/streaming-kafka-integration.md +++ b/docs/streaming-kafka-integration.md @@ -111,7 +111,7 @@ Next, we discuss how to use this approach in your streaming application. <div data-lang="java" markdown="1"> import org.apache.spark.streaming.kafka.*; - JavaPairReceiverInputDStream<String, String> directKafkaStream = + JavaPairInputDStream<String, String> directKafkaStream = KafkaUtils.createDirectStream(streamingContext, [key class], [value class], [key decoder class], [value decoder class], [map of Kafka parameters], [set of topics to consume]); diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index d7eafff38f..6550fcc052 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -145,8 +145,8 @@ import org.apache.spark.streaming.api.java.*; import scala.Tuple2; // Create a local StreamingContext with two working thread and batch interval of 1 second -SparkConf conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount") -JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(1)) +SparkConf conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount"); +JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(1)); {% endhighlight %} Using this context, we can create a DStream that represents streaming data from a TCP |