de00bc63db
1. Changed the lock structure of BlockManager by replacing the 337 coarse-grained locks to use BlockInfo objects as per-block fine-grained locks. 2. Changed the MemoryStore lock structure by making the block putting threads lock on a different object (not the memory store) thus making sure putting threads minimally blocks to the getting treads. 3. Added spark.storage.ThreadingTest to stress test the BlockManager using 5 block producer and 5 block consumer threads. |
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|---|---|---|
| bagel/src | ||
| bin | ||
| conf | ||
| core/src | ||
| docs | ||
| ec2 | ||
| examples/src/main | ||
| project | ||
| repl | ||
| sbt | ||
| .gitignore | ||
| kmeans_data.txt | ||
| LICENSE | ||
| lr_data.txt | ||
| README.md | ||
| run | ||
| run.cmd | ||
| run2.cmd | ||
| spark-executor | ||
| spark-shell | ||
| spark-shell.cmd | ||
Spark
Lightning-Fast Cluster Computing - http://www.spark-project.org/
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project webpage at http://spark-project.org/documentation.html. This README file only contains basic setup instructions.
Building
Spark requires Scala 2.9.2. The project is built using Simple Build Tool (SBT), which is packaged with it. To build Spark and its example programs, run:
sbt/sbt package
To run Spark, you will need to have Scala's bin directory in your PATH, or
you will need to set the SCALA_HOME environment variable to point to where
you've installed Scala. Scala must be accessible through one of these
methods on your cluster's worker nodes as well as its master.
To run one of the examples, use ./run <class> <params>. For example:
./run spark.examples.SparkLR local[2]
will run the Logistic Regression example locally on 2 CPUs.
Each of the example programs prints usage help if no params are given.
All of the Spark samples take a <host> parameter that is the cluster URL
to connect to. This can be a mesos:// or spark:// URL, or "local" to run
locally with one thread, or "local[N]" to run locally with N threads.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported
storage systems. Because the HDFS API has changed in different versions of
Hadoop, you must build Spark against the same version that your cluster runs.
You can change the version by setting the HADOOP_VERSION variable at the top
of project/SparkBuild.scala, then rebuilding Spark.
Configuration
Please refer to the "Configuration" guide in the online documentation for a
full overview on how to configure Spark. At the minimum, you will need to
create a conf/spark-env.sh script (copy conf/spark-env.sh.template) and
set the following two variables:
-
SCALA_HOME: Location where Scala is installed. -
MESOS_NATIVE_LIBRARY: Your Mesos library (only needed if you want to run on Mesos). For example, this might be/usr/local/lib/libmesos.soon Linux.
Contributing to Spark
Contributions via GitHub pull requests are gladly accepted from their original author. Along with any pull requests, please state that the contribution is your original work and that you license the work to the project under the project's open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project's open source license and warrant that you have the legal authority to do so.