9eb222c139
Right now `unit-tests.log` are not of much value because we can't tell where the test boundaries are easily. This patch adds log statements before and after each test to outline the test boundaries, e.g.: ``` ===== TEST OUTPUT FOR o.a.s.serializer.KryoSerializerSuite: 'kryo with parallelize for primitive arrays' ===== 15/05/27 12:36:39.596 pool-1-thread-1-ScalaTest-running-KryoSerializerSuite INFO SparkContext: Starting job: count at KryoSerializerSuite.scala:230 15/05/27 12:36:39.596 dag-scheduler-event-loop INFO DAGScheduler: Got job 3 (count at KryoSerializerSuite.scala:230) with 4 output partitions (allowLocal=false) 15/05/27 12:36:39.596 dag-scheduler-event-loop INFO DAGScheduler: Final stage: ResultStage 3(count at KryoSerializerSuite.scala:230) 15/05/27 12:36:39.596 dag-scheduler-event-loop INFO DAGScheduler: Parents of final stage: List() 15/05/27 12:36:39.597 dag-scheduler-event-loop INFO DAGScheduler: Missing parents: List() 15/05/27 12:36:39.597 dag-scheduler-event-loop INFO DAGScheduler: Submitting ResultStage 3 (ParallelCollectionRDD[5] at parallelize at KryoSerializerSuite.scala:230), which has no missing parents ... 15/05/27 12:36:39.624 pool-1-thread-1-ScalaTest-running-KryoSerializerSuite INFO DAGScheduler: Job 3 finished: count at KryoSerializerSuite.scala:230, took 0.028563 s 15/05/27 12:36:39.625 pool-1-thread-1-ScalaTest-running-KryoSerializerSuite INFO KryoSerializerSuite: ***** FINISHED o.a.s.serializer.KryoSerializerSuite: 'kryo with parallelize for primitive arrays' ***** ... ``` Author: Andrew Or <andrew@databricks.com> Closes #6441 from andrewor14/demarcate-tests and squashes the following commits: 879b060 [Andrew Or] Fix compile after rebase d622af7 [Andrew Or] Merge branch 'master' of github.com:apache/spark into demarcate-tests 017c8ba [Andrew Or] Merge branch 'master' of github.com:apache/spark into demarcate-tests 7790b6c [Andrew Or] Fix tests after logical merge conflict c7460c0 [Andrew Or] Merge branch 'master' of github.com:apache/spark into demarcate-tests c43ffc4 [Andrew Or] Fix tests? 8882581 [Andrew Or] Fix tests ee22cda [Andrew Or] Fix log message fa9450e [Andrew Or] Merge branch 'master' of github.com:apache/spark into demarcate-tests 12d1e1b [Andrew Or] Various whitespace changes (minor) 69cbb24 [Andrew Or] Make all test suites extend SparkFunSuite instead of FunSuite bbce12e [Andrew Or] Fix manual things that cannot be covered through automation da0b12f [Andrew Or] Add core tests as dependencies in all modules f7d29ce [Andrew Or] Introduce base abstract class for all test suites |
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| bagel | ||
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| data/mllib | ||
| dev | ||
| docker | ||
| docs | ||
| ec2 | ||
| examples | ||
| external | ||
| extras | ||
| graphx | ||
| launcher | ||
| mllib | ||
| network | ||
| project | ||
| python | ||
| R | ||
| repl | ||
| sbin | ||
| sbt | ||
| sql | ||
| streaming | ||
| tools | ||
| unsafe | ||
| yarn | ||
| .gitattributes | ||
| .gitignore | ||
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| CONTRIBUTING.md | ||
| LICENSE | ||
| make-distribution.sh | ||
| NOTICE | ||
| pom.xml | ||
| README.md | ||
| scalastyle-config.xml | ||
| tox.ini | ||
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 tests for a module, or individual 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.