diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md
index 0f1e32212eb4..e0d3f4f69be8 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 d7eafff38f35..6550fcc0521c 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