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### What changes were proposed in this pull request? Spark creates local server to serialize several type of data for python. The python code tries to connect to the server, immediately after it's created but there are several system calls in between (this may change in each Spark version): * getaddrinfo * socket * settimeout * connect Under some circumstances in heavy user environments these calls can be super slow (more than 15 seconds). These issues must be analyzed one-by-one but since these are system calls the underlying OS and/or DNS servers must be debugged and fixed. This is not trivial task and at the same time data processing must work somehow. In this PR I'm only intended to add a configuration possibility to increase the mentioned timeouts in order to be able to provide temporary workaround. The rootcause analysis is ongoing but I think this can vary in each case. Because the server part doesn't contain huge amount of log entries to with one can measure time, I've added some. ### Why are the changes needed? Provide workaround when localhost python server connection timeout appears. ### Does this PR introduce _any_ user-facing change? Yes, new configuration added. ### How was this patch tested? Existing unit tests + manual test. ``` #Compile Spark echo "spark.io.encryption.enabled true" >> conf/spark-defaults.conf echo "spark.python.authenticate.socketTimeout 10" >> conf/spark-defaults.conf $ ./bin/pyspark Python 3.8.5 (default, Jul 21 2020, 10:48:26) [Clang 11.0.3 (clang-1103.0.32.62)] on darwin Type "help", "copyright", "credits" or "license" for more information. 20/11/20 10:17:03 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 20/11/20 10:17:03 WARN SparkEnv: I/O encryption enabled without RPC encryption: keys will be visible on the wire. Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /__ / .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT /_/ Using Python version 3.8.5 (default, Jul 21 2020 10:48:26) Spark context Web UI available at http://192.168.0.189:4040 Spark context available as 'sc' (master = local[*], app id = local-1605863824276). SparkSession available as 'spark'. >>> sc.setLogLevel("TRACE") >>> sc.parallelize([0, 2, 3, 4, 6], 5).glom().collect() 20/11/20 10:17:09 TRACE PythonParallelizeServer: Creating listening socket 20/11/20 10:17:09 TRACE PythonParallelizeServer: Setting timeout to 10 sec 20/11/20 10:17:09 TRACE PythonParallelizeServer: Waiting for connection on port 59726 20/11/20 10:17:09 TRACE PythonParallelizeServer: Connection accepted from address /127.0.0.1:59727 20/11/20 10:17:09 TRACE PythonParallelizeServer: Client authenticated 20/11/20 10:17:09 TRACE PythonParallelizeServer: Closing server ... 20/11/20 10:17:10 TRACE SocketFuncServer: Creating listening socket 20/11/20 10:17:10 TRACE SocketFuncServer: Setting timeout to 10 sec 20/11/20 10:17:10 TRACE SocketFuncServer: Waiting for connection on port 59735 20/11/20 10:17:10 TRACE SocketFuncServer: Connection accepted from address /127.0.0.1:59736 20/11/20 10:17:10 TRACE SocketFuncServer: Client authenticated 20/11/20 10:17:10 TRACE SocketFuncServer: Closing server [[0], [2], [3], [4], [6]] >>> ``` Closes #30389 from gaborgsomogyi/SPARK-33143. Lead-authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com> Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com> Co-authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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Apache Spark
Spark is a unified analytics engine for large-scale data processing. 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 Structured 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.)
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 1,000,000,000:
scala> spark.range(1000 * 1000 * 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 1,000,000,000:
>>> spark.range(1000 * 1000 * 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.