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## What changes were proposed in this pull request? Different SSL passwords shown up as command line argument on executor side in standalone mode: * keyStorePassword * keyPassword * trustStorePassword In this PR I've removed SSL configurations from executors. ## How was this patch tested? Existing + additional unit tests. Additionally tested with standalone mode and checked the command line arguments: ``` [gaborsomogyi:~/spark] SPARK-26998(+4/-0,3)+ ± jps 94803 CoarseGrainedExecutorBackend 94818 Jps 90149 RemoteMavenServer 91925 Nailgun 94793 SparkSubmit 94680 Worker 94556 Master 398 [gaborsomogyi:~/spark] SPARK-26998(+4/-1,3)+ ± ps -ef | egrep "94556|94680|94793|94803" 502 94556 1 0 2:02PM ttys007 0:07.39 /Library/Java/JavaVirtualMachines/jdk1.8.0_152.jdk/Contents/Home/bin/java -cp /Users/gaborsomogyi/spark/conf/:/Users/gaborsomogyi/spark/assembly/target/scala-2.12/jars/* -Xmx1g org.apache.spark.deploy.master.Master --host gsomogyi-MBP.local --port 7077 --webui-port 8080 --properties-file conf/spark-defaults.conf 502 94680 1 0 2:02PM ttys007 0:07.27 /Library/Java/JavaVirtualMachines/jdk1.8.0_152.jdk/Contents/Home/bin/java -cp /Users/gaborsomogyi/spark/conf/:/Users/gaborsomogyi/spark/assembly/target/scala-2.12/jars/* -Xmx1g org.apache.spark.deploy.worker.Worker --webui-port 8081 --properties-file conf/spark-defaults.conf spark://gsomogyi-MBP.local:7077 502 94793 94782 0 2:02PM ttys007 0:35.52 /Library/Java/JavaVirtualMachines/jdk1.8.0_152.jdk/Contents/Home/bin/java -cp /Users/gaborsomogyi/spark/conf/:/Users/gaborsomogyi/spark/assembly/target/scala-2.12/jars/* -Dscala.usejavacp=true -Xmx1g org.apache.spark.deploy.SparkSubmit --master spark://gsomogyi-MBP.local:7077 --class org.apache.spark.repl.Main --name Spark shell spark-shell 502 94803 94680 0 2:03PM ttys007 0:05.20 /Library/Java/JavaVirtualMachines/jdk1.8.0_152.jdk/Contents/Home/bin/java -cp /Users/gaborsomogyi/spark/conf/:/Users/gaborsomogyi/spark/assembly/target/scala-2.12/jars/* -Xmx1024M -Dspark.ssl.ui.port=0 -Dspark.driver.port=60902 org.apache.spark.executor.CoarseGrainedExecutorBackend --driver-url spark://CoarseGrainedScheduler172.30.65.186:60902 --executor-id 0 --hostname 172.30.65.186 --cores 8 --app-id app-20190326140311-0000 --worker-url spark://Worker172.30.65.186:60899 502 94910 57352 0 2:05PM ttys008 0:00.00 egrep 94556|94680|94793|94803 ``` Closes #24170 from gaborgsomogyi/SPARK-26998. Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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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.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
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 and Enabling YARN" 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.