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## What changes were proposed in this pull request? `org.apache.spark.deploy.RPackageUtilsSuite` ``` - jars without manifest return false *** FAILED *** (109 milliseconds) java.io.IOException: Unable to delete file: C:\projects\spark\target\tmp\1500266936418-0\dep1-c.jar ``` `org.apache.spark.deploy.SparkSubmitSuite` ``` - download one file to local *** FAILED *** (16 milliseconds) java.net.URISyntaxException: Illegal character in authority at index 6: s3a://C:\projects\spark\target\tmp\test2630198944759847458.jar - download list of files to local *** FAILED *** (0 milliseconds) java.net.URISyntaxException: Illegal character in authority at index 6: s3a://C:\projects\spark\target\tmp\test2783551769392880031.jar ``` `org.apache.spark.scheduler.ReplayListenerSuite` ``` - Replay compressed inprogress log file succeeding on partial read (156 milliseconds) Exception encountered when attempting to run a suite with class name: org.apache.spark.scheduler.ReplayListenerSuite *** ABORTED *** (1 second, 391 milliseconds) java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-8f3cacd6-faad-4121-b901-ba1bba8025a0 - End-to-end replay *** FAILED *** (62 milliseconds) java.io.IOException: No FileSystem for scheme: C - End-to-end replay with compression *** FAILED *** (110 milliseconds) java.io.IOException: No FileSystem for scheme: C ``` `org.apache.spark.sql.hive.StatisticsSuite` ``` - SPARK-21079 - analyze table with location different than that of individual partitions *** FAILED *** (875 milliseconds) org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string); - SPARK-21079 - analyze partitioned table with only a subset of partitions visible *** FAILED *** (47 milliseconds) org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string); ``` **Note:** this PR does not fix: `org.apache.spark.deploy.SparkSubmitSuite` ``` - launch simple application with spark-submit with redaction *** FAILED *** (172 milliseconds) java.util.NoSuchElementException: next on empty iterator ``` I can't reproduce this on my Windows machine but looks appearntly consistently failed on AppVeyor. This one is unclear to me yet and hard to debug so I did not include this one for now. **Note:** it looks there are more instances but it is hard to identify them partly due to flakiness and partly due to swarming logs and errors. Will probably go one more time if it is fine. ## How was this patch tested? Manually via AppVeyor: **Before** - `org.apache.spark.deploy.RPackageUtilsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/8t8ra3lrljuir7q4 - `org.apache.spark.deploy.SparkSubmitSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/taquy84yudjjen64 - `org.apache.spark.scheduler.ReplayListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/24omrfn2k0xfa9xq - `org.apache.spark.sql.hive.StatisticsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/2079y1plgj76dc9l **After** - `org.apache.spark.deploy.RPackageUtilsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/3803dbfn89ne1164 - `org.apache.spark.deploy.SparkSubmitSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/m5l350dp7u9a4xjr - `org.apache.spark.scheduler.ReplayListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/565vf74pp6bfdk18 - `org.apache.spark.sql.hive.StatisticsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/qm78tsk8c37jb6s4 Jenkins tests are required and AppVeyor tests will be triggered. Author: hyukjinkwon <gurwls223@gmail.com> Closes #18971 from HyukjinKwon/windows-fixes. |
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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, Python, and R, 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 DataFrames, 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. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
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" 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.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.