[SPARK-4806] Streaming doc update for 1.2

Important updates to the streaming programming guide
- Make the fault-tolerance properties easier to understand, with information about write ahead logs
- Update the information about deploying the spark streaming app with information about Driver HA
- Update Receiver guide to discuss reliable vs unreliable receivers.

Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Josh Rosen <joshrosen@databricks.com>
Author: Josh Rosen <rosenville@gmail.com>

Closes #3653 from tdas/streaming-doc-update-1.2 and squashes the following commits:

f53154a [Tathagata Das] Addressed Josh's comments.
ce299e4 [Tathagata Das] Minor update.
ca19078 [Tathagata Das] Minor change
f746951 [Tathagata Das] Mentioned performance problem with WAL
7787209 [Tathagata Das] Merge branch 'streaming-doc-update-1.2' of github.com:tdas/spark into streaming-doc-update-1.2
2184729 [Tathagata Das] Updated Kafka and Flume guides with reliability information.
2f3178c [Tathagata Das] Added more information about writing reliable receivers in the custom receiver guide.
91aa5aa [Tathagata Das] Improved API Docs menu
5707581 [Tathagata Das] Added Pythn API badge
b9c8c24 [Tathagata Das] Merge pull request #26 from JoshRosen/streaming-programming-guide
b8c8382 [Josh Rosen] minor fixes
a4ef126 [Josh Rosen] Restructure parts of the fault-tolerance section to read a bit nicer when skipping over the headings
65f66cd [Josh Rosen] Fix broken link to fault-tolerance semantics section.
f015397 [Josh Rosen] Minor grammar / pluralization fixes.
3019f3a [Josh Rosen] Fix minor Markdown formatting issues
aa8bb87 [Tathagata Das] Small update.
195852c [Tathagata Das] Updated based on Josh's comments, updated receiver reliability and deploying section, and also updated configuration.
17b99fb [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into streaming-doc-update-1.2
a0217c0 [Tathagata Das] Changed Deploying menu layout
67fcffc [Tathagata Das] Added cluster mode + supervise example to submitting application guide.
e45453b [Tathagata Das] Update streaming guide, added deploying section.
192c7a7 [Tathagata Das] Added more info about Python API, and rewrote the checkpointing section.

1 files changed, 10 insertions, 3 deletions

diff --git a/docs/streaming-flume-integration.md b/docs/streaming-flume-integration.md
index d57c3e0ef9..ac01dd3d80 100644
--- a/docs/streaming-flume-integration.md
+++ b/docs/streaming-flume-integration.md
@@ -66,9 +66,16 @@ configuring Flume agents.
 
 ## Approach 2 (Experimental): Pull-based Approach using a Custom Sink
 Instead of Flume pushing data directly to Spark Streaming, this approach runs a custom Flume sink that allows the following.
+
 - Flume pushes data into the sink, and the data stays buffered.
-- Spark Streaming uses transactions to pull data from the sink. Transactions succeed only after data is received and replicated by Spark Streaming.
-This ensures that better reliability and fault-tolerance than the previous approach. However, this requires configuring Flume to run a custom sink. Here are the configuration steps.
+- Spark Streaming uses a [reliable Flume receiver](streaming-programming-guide.html#receiver-reliability)
+  and transactions to pull data from the sink. Transactions succeed only after data is received and
+  replicated by Spark Streaming.
+
+This ensures stronger reliability and
+[fault-tolerance guarantees](streaming-programming-guide.html#fault-tolerance-semantics)
+than the previous approach. However, this requires configuring Flume to run a custom sink.
+Here are the configuration steps.
 
 #### General Requirements
 Choose a machine that will run the custom sink in a Flume agent. The rest of the Flume pipeline is configured to send data to that agent. Machines in the Spark cluster should have access to the chosen machine running the custom sink.
@@ -104,7 +111,7 @@ See the [Flume's documentation](https://flume.apache.org/documentation.html) for
 configuring Flume agents.
 
 #### Configuring Spark Streaming Application
-1. **Linking:** In your SBT/Maven projrect definition, link your streaming application against the `spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}` (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide).
+1. **Linking:** In your SBT/Maven project definition, link your streaming application against the `spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}` (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide).
 
 2. **Programming:** In the streaming application code, import `FlumeUtils` and create input DStream as follows.