Version number to 1.0.0-SNAPSHOT
Since 0.9.0-incubating is done and out the door, we shouldn't be building 0.9.0-incubating-SNAPSHOT anymore.
@pwendell
Author: Mark Hamstra <markhamstra@gmail.com>
== Merge branch commits ==
commit 1b00a8a7c1a7f251b4bb3774b84b9e64758eaa71
Author: Mark Hamstra <markhamstra@gmail.com>
Date: Wed Feb 5 09:30:32 2014 -0800
Version number to 1.0.0-SNAPSHOT
Upgrade junit-interface plugin from 0.9 to 0.10.
I noticed that the JavaAPISuite tests didn't
appear to display any output locally or under
Jenkins, making it difficult to know whether they
were running. This change increases the verbosity
to more closely match the ScalaTest tests.
Remove Typesafe Config usage and conf files to fix nested property names
With Typesafe Config we had the subtle problem of no longer allowing
nested property names, which are used for a few of our properties:
http://apache-spark-developers-list.1001551.n3.nabble.com/Config-properties-broken-in-master-td208.html
This PR is for branch 0.9 but should be added into master too.
(cherry picked from commit 34e911ce9a)
Signed-off-by: Patrick Wendell <pwendell@gmail.com>
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`~~
Refactored the streaming project to separate external libraries like Twitter, Kafka, Flume, etc.
At a high level, these are the following changes.
1. All the external code was put in `SPARK_HOME/external/` as separate SBT projects and Maven modules. Their artifact names are `spark-streaming-twitter`, `spark-streaming-kafka`, etc. Both SparkBuild.scala and pom.xml files have been updated. References to external libraries and repositories have been removed from the settings of root and streaming projects/modules.
2. To avail the external functionality (say, creating a Twitter stream), the developer has to `import org.apache.spark.streaming.twitter._` . For Scala API, the developer has to call `TwitterUtils.createStream(streamingContext, ...)`. For the Java API, the developer has to call `TwitterUtils.createStream(javaStreamingContext, ...)`.
3. Each external project has its own scala and java unit tests. Note the unit tests of each external library use classes of the streaming unit tests (`TestSuiteBase`, `LocalJavaStreamingContext`, etc.). To enable this code sharing among test classes, `dependsOn(streaming % "compile->compile,test->test")` was used in the SparkBuild.scala . In the streaming/pom.xml, an additional `maven-jar-plugin` was necessary to capture this dependency (see comment inside the pom.xml for more information).
4. Jars of the external projects have been added to examples project but not to the assembly project.
5. In some files, imports have been rearrange to conform to the Spark coding guidelines.
Approximate distinct count
Added countApproxDistinct() to RDD and countApproxDistinctByKey() to PairRDDFunctions to approximately count distinct number of elements and distinct number of values per key, respectively. Both functions use HyperLogLog from stream-lib for counting. Both functions take a parameter that controls the trade-off between accuracy and memory consumption. Also added Scala docs and test suites for both methods.
Without this, in some cases, Ivy attempts to download the wrong file
and fails, stopping the whole build. See bug for more details.
(This is probably also the beginning of the slow death of our
recently prettified dependencies. Form follow function.)
This also includes:
-Change `isNewYarn` to `isNewHadoop`, since the protobuf-2.5 dependency is from Hadoop-2.2 itself.
-Regexp bugix
Credits to @alig for this patch.