[SPARK-12894][DOCUMENT] Add deploy instructions for Python in Kinesis integration doc

This PR added instructions to get Kinesis assembly jar for Python users in the Kinesis integration page like Kafka doc.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #10822 from zsxwing/kinesis-doc.

1 files changed, 12 insertions, 2 deletions

diff --git a/docs/streaming-kinesis-integration.md b/docs/streaming-kinesis-integration.md
index 07194b0a6b..5f5e2b9087 100644
--- a/docs/streaming-kinesis-integration.md
+++ b/docs/streaming-kinesis-integration.md
@@ -15,12 +15,13 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m
 
 #### Configuring Spark Streaming Application
 
-1. **Linking:** In your SBT/Maven project definition, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
+1. **Linking:** For Scala/Java applications using SBT/Maven project definitions, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
 
 		groupId = org.apache.spark
 		artifactId = spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}
 		version = {{site.SPARK_VERSION_SHORT}}
 
+	For Python applications, you will have to add this above library and its dependencies when deploying your application. See the *Deploying* subsection below.
 	**Note that by linking to this library, you will include [ASL](https://aws.amazon.com/asl/)-licensed code in your application.**
 
 2. **Programming:** In the streaming application code, import `KinesisUtils` and create the input DStream of byte array as follows:
@@ -116,7 +117,16 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m
 
 	In other versions of the API, you can also specify the AWS access key and secret key directly.
 
-3. **Deploying:** Package `spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).
+3. **Deploying:** As with any Spark applications, `spark-submit` is used to launch your application. However, the details are slightly different for Scala/Java applications and Python applications.
+
+	For Scala and Java applications, if you are using SBT or Maven for project management, then package `spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}` and its dependencies into the application JAR. Make sure `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` are marked as `provided` dependencies as those are already present in a Spark installation. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).
+
+	For Python applications which lack SBT/Maven project management, `spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}` and its dependencies can be directly added to `spark-submit` using `--packages` (see [Application Submission Guide](submitting-applications.html)). That is,
+
+	    ./bin/spark-submit --packages org.apache.spark:spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}} ...
+
+	Alternatively, you can also download the JAR of the Maven artifact `spark-streaming-kinesis-asl-assembly` from the
+	[Maven repository](http://search.maven.org/#search|ga|1|a%3A%22spark-streaming-kinesis-asl-assembly_{{site.SCALA_BINARY_VERSION}}%22%20AND%20v%3A%22{{site.SPARK_VERSION_SHORT}}%22) and add it to `spark-submit` with `--jars`.
 
 	*Points to remember at runtime:*