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# What changes were proposed in this pull request? This PR adds a `FileNotFoundException` try catch block while adding a new entry to history server application listing to skip the non-existing path. ### Why are the changes needed? If there are a large number (>100k) of applications log dir, listing the log dir will take a few seconds. After getting the path list some applications might have finished already, and the filename will change from `foo.inprogress` to `foo`. It leads to a problem when adding an entry to the listing, querying file status like `fileSizeForLastIndex` will throw out a `FileNotFoundException` exception if the application was finished. And the exception will abort current loop, in a busy cluster, it will make history server couldn't list and load any application log. ``` 20/08/03 15:17:23 ERROR FsHistoryProvider: Exception in checking for event log updates java.io.FileNotFoundException: File does not exist: hdfs://xx/logs/spark/application_11111111111111.lz4.inprogress at org.apache.hadoop.hdfs.DistributedFileSystem$29.doCall(DistributedFileSystem.java:1527) at org.apache.hadoop.hdfs.DistributedFileSystem$29.doCall(DistributedFileSystem.java:1520) at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81) at org.apache.hadoop.hdfs.DistributedFileSystem.getFileStatus(DistributedFileSystem.java:1520) at org.apache.spark.deploy.history.SingleFileEventLogFileReader.status$lzycompute(EventLogFileReaders.scala:170) ``` ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? 1. setup another script keeps changing the filename of applications under history log dir 2. launch the history server 3. check whether the `File does not exist` error log was gone. Closes #29350 from yanxiaole/SPARK-32529. Authored-by: Yan Xiaole <xiaole.yan@gmail.com> Signed-off-by: Dongjoon Hyun <dongjoon@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.