893d6fd704
ksakellis I stumbled on your JIRA for this yesterday; I know it's assigned to you but I'd already done this for my own uses a while ago so thought I could help save you the work of doing it! Hopefully this doesn't duplicate any work you've already done. Here's a screenshot of what the UI looks like: ![image](https://cloud.githubusercontent.com/assets/1108612/6135352/c03e7276-b11c-11e4-8f11-c6aefe1f35b9.png) Based on a discussion with pwendell, I put the data read remotely in as an additional metric rather than showing it in brackets as you'd suggested, Kostas. The assumption here is that the average user doesn't care about the differentiation between local / remote data, so it's better not to pollute the UI. I also added data about the local read time, which I've found very helpful for debugging, but I didn't put it in the UI because I think it's probably something not a ton of people will need to use. With this change, the total read time and total write time shown in the UI will be equal, fixing a long-term source of user confusion: ![image](https://cloud.githubusercontent.com/assets/1108612/6135399/25f14490-b11d-11e4-8086-20be5f4002e6.png) Author: Kay Ousterhout <kayousterhout@gmail.com> Closes #4510 from kayousterhout/SPARK-5645 and squashes the following commits: 4a0182c [Kay Ousterhout] oops 5f5da1b [Kay Ousterhout] Small style fix 5da04cf [Kay Ousterhout] Addressed more comments from Kostas ba05149 [Kay Ousterhout] Remove parens a9dc685 [Kay Ousterhout] Kostas comment, test fix 33d2e2d [Kay Ousterhout] Merge remote-tracking branch 'upstream/master' into SPARK-5645 347e2cd [Kay Ousterhout] [SPARK-5645] Added local read bytes/time to task metrics |
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Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1000:
>>> sc.parallelize(range(1000)).count()
Example Programs
Spark also comes with several sample programs in the examples
directory.
To run one of them, use ./bin/run-example <class> [params]
. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run all automated tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.