b2bfe985e8
### What changes were proposed in this pull request? In ProcfsMetricsGetter.scala, propogating IOException from addProcfsMetricsFromOneProcess to computeAllMetrics when the child pid's proc stat file is unavailable. As a result, the for-loop in computeAllMetrics() can terminate earlier and return an all-0 procfs metric. ### Why are the changes needed? In the case of a child pid's stat file missing and the subsequent child pids' stat files exist, ProcfsMetricsGetter.computeAllMetrics() will return partial metrics (the sum of a subset of child pids), which can be misleading and is undesired per the existing code comments in https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/executor/ProcfsMetricsGetter.scala#L214. Also, a side effect of this bug is that it can lead to a verbose warning log if many pids' stat files are missing. An early terminating can make the warning logs more concise. The unit test can also explain the bug well. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? A unit test is added. Closes #31945 from baohe-zhang/SPARK-34845. Authored-by: Baohe Zhang <baohe.zhang@verizonmedia.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> |
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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.