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Removed reference to incubation in Spark user docs.

Author: Reynold Xin <rxin@apache.org>

Closes #2 from rxin/docs and squashes the following commits:

08bbd5f [Reynold Xin] Removed reference to incubation in Spark user docs.
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commit 40e080a68a8fd025435e9ff84fa9280b4aba4dcf 1 parent c42557b
@rxin rxin authored pwendell committed
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2  docs/README.md
@@ -1,6 +1,6 @@
Welcome to the Spark documentation!
-This readme will walk you through navigating and building the Spark documentation, which is included here with the Spark source code. You can also find documentation specific to release versions of Spark at http://spark.incubator.apache.org/documentation.html.
+This readme will walk you through navigating and building the Spark documentation, which is included here with the Spark source code. You can also find documentation specific to release versions of Spark at http://spark.apache.org/documentation.html.
Read on to learn more about viewing documentation in plain text (i.e., markdown) or building the documentation yourself. Why build it yourself? So that you have the docs that corresponds to whichever version of Spark you currently have checked out of revision control.
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4 docs/_config.yml
@@ -3,10 +3,10 @@ markdown: kramdown
# These allow the documentation to be updated with nerw releases
# of Spark, Scala, and Mesos.
-SPARK_VERSION: 1.0.0-incubating-SNAPSHOT
+SPARK_VERSION: 1.0.0-SNAPSHOT
SPARK_VERSION_SHORT: 1.0.0
SCALA_BINARY_VERSION: "2.10"
SCALA_VERSION: "2.10.3"
MESOS_VERSION: 0.13.0
SPARK_ISSUE_TRACKER_URL: https://spark-project.atlassian.net
-SPARK_GITHUB_URL: https://github.com/apache/incubator-spark
+SPARK_GITHUB_URL: https://github.com/apache/spark
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10 docs/_layouts/global.html
@@ -159,16 +159,6 @@ <h1 class="title">{{ page.title }}</h1>
<hr>-->
- <footer>
- <hr>
- <p style="text-align: center; veritcal-align: middle; color: #999;">
- Apache Spark is an effort undergoing incubation at the Apache Software Foundation.
- <a href="http://incubator.apache.org">
- <img style="margin-left: 20px;" src="img/incubator-logo.png" />
- </a>
- </p>
- </footer>
-
</div> <!-- /container -->
<script src="js/vendor/jquery-1.8.0.min.js"></script>
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2  docs/bagel-programming-guide.md
@@ -108,7 +108,7 @@ _Example_
## Operations
-Here are the actions and types in the Bagel API. See [Bagel.scala](https://github.com/apache/incubator-spark/blob/master/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala) for details.
+Here are the actions and types in the Bagel API. See [Bagel.scala](https://github.com/apache/spark/blob/master/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala) for details.
### Actions
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12 docs/index.md
@@ -9,7 +9,7 @@ It also supports a rich set of higher-level tools including [Shark](http://shark
# Downloading
-Get Spark by visiting the [downloads page](http://spark.incubator.apache.org/downloads.html) of the Apache Spark site. This documentation is for Spark version {{site.SPARK_VERSION}}.
+Get Spark by visiting the [downloads page](http://spark.apache.org/downloads.html) of the Apache Spark site. This documentation is for Spark version {{site.SPARK_VERSION}}.
Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you need to run it is to have `java` to installed on your system `PATH`, or the `JAVA_HOME` environment variable pointing to a Java installation.
@@ -96,7 +96,7 @@ For this version of Spark (0.8.1) Hadoop 2.2.x (or newer) users will have to bui
* [Amazon EC2](ec2-scripts.html): scripts that let you launch a cluster on EC2 in about 5 minutes
* [Standalone Deploy Mode](spark-standalone.html): launch a standalone cluster quickly without a third-party cluster manager
* [Mesos](running-on-mesos.html): deploy a private cluster using
- [Apache Mesos](http://incubator.apache.org/mesos)
+ [Apache Mesos](http://mesos.apache.org)
* [YARN](running-on-yarn.html): deploy Spark on top of Hadoop NextGen (YARN)
**Other documents:**
@@ -110,20 +110,20 @@ For this version of Spark (0.8.1) Hadoop 2.2.x (or newer) users will have to bui
**External resources:**
-* [Spark Homepage](http://spark.incubator.apache.org)
+* [Spark Homepage](http://spark.apache.org)
* [Shark](http://shark.cs.berkeley.edu): Apache Hive over Spark
-* [Mailing Lists](http://spark.incubator.apache.org/mailing-lists.html): ask questions about Spark here
+* [Mailing Lists](http://spark.apache.org/mailing-lists.html): ask questions about Spark here
* [AMP Camps](http://ampcamp.berkeley.edu/): a series of training camps at UC Berkeley that featured talks and
exercises about Spark, Shark, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/agenda-2012),
[slides](http://ampcamp.berkeley.edu/agenda-2012) and [exercises](http://ampcamp.berkeley.edu/exercises-2012) are
available online for free.
-* [Code Examples](http://spark.incubator.apache.org/examples.html): more are also available in the [examples subfolder](https://github.com/apache/incubator-spark/tree/master/examples/src/main/scala/) of Spark
+* [Code Examples](http://spark.apache.org/examples.html): more are also available in the [examples subfolder](https://github.com/apache/spark/tree/master/examples/src/main/scala/) of Spark
* [Paper Describing Spark](http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf)
* [Paper Describing Spark Streaming](http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf)
# Community
-To get help using Spark or keep up with Spark development, sign up for the [user mailing list](http://spark.incubator.apache.org/mailing-lists.html).
+To get help using Spark or keep up with Spark development, sign up for the [user mailing list](http://spark.apache.org/mailing-lists.html).
If you're in the San Francisco Bay Area, there's a regular [Spark meetup](http://www.meetup.com/spark-users/) every few weeks. Come by to meet the developers and other users.
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2  docs/java-programming-guide.md
@@ -189,7 +189,7 @@ We hope to generate documentation with Java-style syntax in the future.
# Where to Go from Here
Spark includes several sample programs using the Java API in
-[`examples/src/main/java`](https://github.com/apache/incubator-spark/tree/master/examples/src/main/java/org/apache/spark/examples). You can run them by passing the class name to the
+[`examples/src/main/java`](https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples). You can run them by passing the class name to the
`bin/run-example` script included in Spark; for example:
./bin/run-example org.apache.spark.examples.JavaWordCount
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2  docs/scala-programming-guide.md
@@ -365,7 +365,7 @@ res2: Int = 10
# Where to Go from Here
-You can see some [example Spark programs](http://spark.incubator.apache.org/examples.html) on the Spark website.
+You can see some [example Spark programs](http://spark.apache.org/examples.html) on the Spark website.
In addition, Spark includes several samples in `examples/src/main/scala`. Some of them have both Spark versions and local (non-parallel) versions, allowing you to see what had to be changed to make the program run on a cluster. You can run them using by passing the class name to the `bin/run-example` script included in Spark; for example:
./bin/run-example org.apache.spark.examples.SparkPi
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4 docs/spark-debugger.md
@@ -2,7 +2,7 @@
layout: global
title: The Spark Debugger
---
-**Summary:** The Spark debugger provides replay debugging for deterministic (logic) errors in Spark programs. It's currently in development, but you can try it out in the [arthur branch](https://github.com/apache/incubator-spark/tree/arthur).
+**Summary:** The Spark debugger provides replay debugging for deterministic (logic) errors in Spark programs. It's currently in development, but you can try it out in the [arthur branch](https://github.com/apache/spark/tree/arthur).
## Introduction
@@ -19,7 +19,7 @@ For deterministic errors, debugging a Spark program is now as easy as debugging
## Approach
-As your Spark program runs, the slaves report key events back to the master -- for example, RDD creations, RDD contents, and uncaught exceptions. (A full list of event types is in [EventLogging.scala](https://github.com/apache/incubator-spark/blob/arthur/core/src/main/scala/spark/EventLogging.scala).) The master logs those events, and you can load the event log into the debugger after your program is done running.
+As your Spark program runs, the slaves report key events back to the master -- for example, RDD creations, RDD contents, and uncaught exceptions. (A full list of event types is in [EventLogging.scala](https://github.com/apache/spark/blob/arthur/core/src/main/scala/spark/EventLogging.scala).) The master logs those events, and you can load the event log into the debugger after your program is done running.
_A note on nondeterminism:_ For fault recovery, Spark requires RDD transformations (for example, the function passed to `RDD.map`) to be deterministic. The Spark debugger also relies on this property, and it can also warn you if your transformation is nondeterministic. This works by checksumming the contents of each RDD and comparing the checksums from the original execution to the checksums after recomputing the RDD in the debugger.
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