Merge pull request #367 from ankurdave/graphx

GraphX: Unifying Graphs and Tables

GraphX extends Spark's distributed fault-tolerant collections API and interactive console with a new graph API which leverages recent advances in graph systems (e.g., [GraphLab](http://graphlab.org)) to enable users to easily and interactively build, transform, and reason about graph structured data at scale. See http://amplab.github.io/graphx/.

Thanks to @jegonzal, @rxin, @ankurdave, @dcrankshaw, @jianpingjwang, @amatsukawa, @kellrott, and @adamnovak.

Tasks left:
- [x] Graph-level uncache
- [x] Uncache previous iterations in Pregel
- [x] ~~Uncache previous iterations in GraphLab~~ (postponed to post-release)
- [x] - Describe GC issue with GraphLab
- [ ] Write `docs/graphx-programming-guide.md`
- [x] - Mention future Bagel support in docs
- [ ] - Section on caching/uncaching in docs: As with Spark, cache something that is used more than once. In an iterative algorithm, try to cache and force (i.e., materialize) something every iteration, then uncache the cached things that depended on the newly materialized RDD but that won't be referenced again.
- [x] Undo modifications to core collections and instead copy them to org.apache.spark.graphx
- [x] Make Graph serializable to work around capture in Spark shell
- [x] Rename graph -> graphx in package name and subproject
- [x] Remove standalone PageRank
- [x] ~~Fix amplab/graphx#52 by checking `iter.hasNext`~~

1 files changed, 3 insertions, 1 deletions

diff --git a/docs/index.md b/docs/index.md
index 86d574daaa..debdb33108 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -5,7 +5,7 @@ title: Spark Overview
 
 Apache Spark is a fast and general-purpose cluster computing system.
 It provides high-level APIs in [Scala](scala-programming-guide.html), [Java](java-programming-guide.html), and [Python](python-programming-guide.html) that make parallel jobs easy to write, and an optimized engine that supports general computation graphs.
-It also supports a rich set of higher-level tools including [Shark](http://shark.cs.berkeley.edu) (Hive on Spark), [MLlib](mllib-guide.html) for machine learning, [Bagel](bagel-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).
+It also supports a rich set of higher-level tools including [Shark](http://shark.cs.berkeley.edu) (Hive on Spark), [MLlib](mllib-guide.html) for machine learning, [GraphX](graphx-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).
 
 # Downloading
 
@@ -78,6 +78,7 @@ For this version of Spark (0.8.1) Hadoop 2.2.x (or newer) users will have to bui
 * [Spark Streaming](streaming-programming-guide.html): using the alpha release of Spark Streaming
 * [MLlib (Machine Learning)](mllib-guide.html): Spark's built-in machine learning library
 * [Bagel (Pregel on Spark)](bagel-programming-guide.html): simple graph processing model
+* [GraphX (Graphs on Spark)](graphx-programming-guide.html): Spark's new API for graphs
 
 **API Docs:**
 
@@ -86,6 +87,7 @@ For this version of Spark (0.8.1) Hadoop 2.2.x (or newer) users will have to bui
 * [Spark Streaming for Java/Scala (Scaladoc)](api/streaming/index.html)
 * [MLlib (Machine Learning) for Java/Scala (Scaladoc)](api/mllib/index.html)
 * [Bagel (Pregel on Spark) for Scala (Scaladoc)](api/bagel/index.html)
+* [GraphX (Graphs on Spark) for Scala (Scaladoc)](api/graphx/index.html)
 
 
 **Deployment guides:**