e6795cd341
### What changes were proposed in this pull request?
In many operations on CompactibleFileStreamLog reads a metadata log file and materializes all entries into memory. As the nature of the compact operation, CompactibleFileStreamLog may have a huge compact log file with bunch of entries included, and for now they're just monotonically increasing, which means the amount of memory to materialize also grows incrementally. This leads pressure on GC.
This patch proposes to streamline the logic on file stream source and sink whenever possible to avoid memory issue. To make this possible we have to break the existing behavior of excluding entries - now the `compactLogs` method is called with all entries, which forces us to materialize all entries into memory. This is hopefully no effect on end users, because only file stream sink has a condition to exclude entries, and the condition has been never true. (DELETE_ACTION has been never set.)
Based on the observation, this patch also changes the existing UT a bit which simulates the situation where "A" file is added, and another batch marks the "A" file as deleted. This situation simply doesn't work with the change, but as I mentioned earlier it hasn't been used. (I'm not sure the UT is from the actual run. I guess not.)
### Why are the changes needed?
The memory issue (OOME) is reported by both JIRA issue and user mailing list.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
* Existing UTs
* Manual test done
The manual test leverages the simple apps which continuously writes the file stream sink metadata log.
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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.