5f11e8c4cb
Currently, we do not have a mechanism to collect driver logs if a user chooses to run their application in client mode. This is a big issue as admin teams need to create their own mechanisms to capture driver logs. This commit adds a logger which, if enabled, adds a local log appender to the root logger and asynchronously syncs it an application specific log file on hdfs (Spark Driver Log Dir). Additionally, this collects spark-shell driver logs at INFO level by default. The change is that instead of setting root logger level to WARN, we will set the consoleAppender threshold to WARN, in case of spark-shell. This ensures that only WARN logs are printed on CONSOLE but other log appenders still capture INFO (or the default log level logs). 1. Verified that logs are written to local and remote dir 2. Added a unit test case 3. Verified this for spark-shell, client mode and pyspark. 4. Verified in both non-kerberos and kerberos environment 5. Verified with following unexpected termination conditions: Ctrl + C, Driver OOM, Large Log Files 6. Ran an application in spark-shell and ensured that driver logs were captured at INFO level 7. Started the application at WARN level, programmatically changed the level to INFO and ensured that logs on console were printed at INFO level Closes #22504 from ankuriitg/ankurgupta/SPARK-25118. Authored-by: ankurgupta <ankur.gupta@cloudera.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.