Reformat streamingContext docs / config options

Author: velvia

doc/contexts.md

@@ -40,9 +40,11 @@ This can be done easily by extending the `SparkContextFactory` trait, like `SQLC
 
 If you wish to use the `SQLContext` or `HiveContext`, be sure to pull down the job-server-extras package.
 
-# StreamingContext
-job-server-extras provides a context to run Spark Streaming jobs, there are a couple of configurations you can change in job-server's .conf file
-streaming.batch_interval: the streaming batch in millis
-streaming.stopGracefully: if true, stops gracefully by waiting for the processing of all received data to be completed 
-streaming.stopSparkContext: if true, stops the SparkContext with the StreamingContext. The underlying SparkContext will be stopped regardless of whether the StreamingContext has been started.
+## StreamingContext
+
+`job-server-extras` provides a context to run Spark Streaming jobs. There are a couple of configurations you can change in job-server's .conf file:
+
+* `streaming.batch_interval`: the streaming batch in millis
+* `streaming.stopGracefully`: if true, stops gracefully by waiting for the processing of all received data to be completed 
+* `streaming.stopSparkContext`: if true, stops the SparkContext with the StreamingContext. The underlying SparkContext will be stopped regardless of whether the StreamingContext has been started.
 

job-server-extras/src/spark.jobserver/StreamingTestJob.scala

@@ -7,9 +7,6 @@ import org.apache.spark.streaming.StreamingContext
 
 import scala.collection.mutable
 
-/** :: TestObject ::
- * A Streaming job for testing, will
- */
 @VisibleForTesting
 object StreamingTestJob extends SparkStramingJob {
   def validate(ssc: StreamingContext, config: Config): SparkJobValidation = SparkJobValid

job-server/src/main/resources/application.conf

@@ -54,13 +54,18 @@ spark {
     # Determines the type of jobs that can run in a SparkContext
     context-factory = spark.jobserver.context.DefaultSparkContextFactory
 
-    # Default batch interval for Spark Streaming contexts in milliseconds
-    streaming.batch_interval = 1000
-    # if true, stops gracefully by waiting for the processing of all received data to be completed
-    streaming.stopGracefully = true
-    # if true, stops the SparkContext with the StreamingContext. The underlying SparkContext will be
-    # stopped regardless of whether the StreamingContext has been started.
-    streaming.stopSparkContext = true
+
+    streaming {
+      # Default batch interval for Spark Streaming contexts in milliseconds
+      batch_interval = 1000
+
+      # if true, stops gracefully by waiting for the processing of all received data to be completed
+      stopGracefully = true
+
+      # if true, stops the SparkContext with the StreamingContext. The underlying SparkContext will be
+      # stopped regardless of whether the StreamingContext has been started.
+      stopSparkContext = true
+    }
 
     # uris of jars to be loaded into the classpath for this context. Uris is a string list, or a string separated by commas ','
     # dependent-jar-uris = ["file:///some/path/present/in/each/mesos/slave/somepackage.jar"]