Address more

Author: zsxwing

docs/structured-streaming-kafka-integration.md

@@ -190,7 +190,7 @@ The rest configurations are optional:
   as you expected.</td>
 </tr>
 <tr>
-  <td>kafka.consumer.poll.timeoutMs</td>
+  <td>kafkaConsumer.pollTimeoutMs</td>
   <td>long</td>
   <td>512</td>
   <td>The timeout in milliseconds to poll data from Kafka in executors.</td>

docs/structured-streaming-programming-guide.md

@@ -418,10 +418,15 @@ Since Spark 2.0, DataFrames and Datasets can represent static, bounded data, as
 Streaming DataFrames can be created through the `DataStreamReader` interface 
 ([Scala](api/scala/index.html#org.apache.spark.sql.streaming.DataStreamReader)/
 [Java](api/java/org/apache/spark/sql/streaming/DataStreamReader.html)/
-[Python](api/python/pyspark.sql.html#pyspark.sql.streaming.DataStreamReader) docs) returned by `SparkSession.readStream()`. Similar to the read interface for creating static DataFrame, you can specify the details of the source – data format, schema, options, etc. In Spark 2.0, there are a few built-in sources.
+[Python](api/python/pyspark.sql.html#pyspark.sql.streaming.DataStreamReader) docs) returned by `SparkSession.readStream()`. Similar to the read interface for creating static DataFrame, you can specify the details of the source – data format, schema, options, etc.
+
+#### Data Sources
+In Spark 2.0, there are a few built-in sources.
 
   - **File source** - Reads files written in a directory as a stream of data. Supported file formats are text, csv, json, parquet. See the docs of the DataStreamReader interface for a more up-to-date list, and supported options for each file format. Note that the files must be atomically placed in the given directory, which in most file systems, can be achieved by file move operations.
 
+  - **Kafka source** - Poll data from Kafka. It's compatible with Kafka broker versions 0.10.0 or higher. See the [Kafka Integration Guide](structured-streaming-kafka-integration.html) for more details.
+
   - **Socket source (for testing)** - Reads UTF8 text data from a socket connection. The listening server socket is at the driver. Note that this should be used only for testing as this does not provide end-to-end fault-tolerance guarantees. 
 
 Here are some examples.
@@ -1126,12 +1131,6 @@ aggDF \
 </div>
 </div>
 
-## Advanced Sources
-
-Structured Streaming supports the following advanced sources:
-
-- **Kafka:** Structured Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Kafka broker versions 0.10.0 or higher. See the [Kafka Integration Guide](structured-streaming-kafka-integration.html) for more details.
-
 # Where to go from here
 - Examples: See and run the 
 [Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/sql/streaming)/[Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/sql/streaming)/[Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python/sql/streaming) 

external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSource.scala

@@ -87,7 +87,7 @@ private[kafka010] case class KafkaSource(
 
   private val sc = sqlContext.sparkContext
 
-  private val pollTimeoutMs = sourceOptions.getOrElse("kafka.consumer.poll.timeoutMs", "512").toLong
+  private val pollTimeoutMs = sourceOptions.getOrElse("kafkaConsumer.pollTimeoutMs", "512").toLong
 
   private val maxOffsetFetchAttempts =
     sourceOptions.getOrElse("fetchOffset.numRetries", "3").toInt

external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaSourceProvider.scala

@@ -210,7 +210,7 @@ private[kafka010] class KafkaSourceProvider extends StreamSourceProvider
            |Kafka option '${ConsumerConfig.AUTO_OFFSET_RESET_CONFIG}' is not supported.
            |Instead set the source option '$STARTING_OFFSET_OPTION_KEY' to 'earliest' or 'latest' to
            |specify where to start. Structured Streaming manages which offsets are consumed
-           |internally, rather than relying on the kafka Consumer to do it. This will ensure that no
+           |internally, rather than relying on the kafkaConsumer to do it. This will ensure that no
            |data is missed when when new topics/partitions are dynamically subscribed. Note that
            |'$STARTING_OFFSET_OPTION_KEY' only applies when a new Streaming query is started, and
            |that resuming will always pick up from where the query left off. See the docs for more