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### What changes were proposed in this pull request? This patch proposes to unload inactive state store as soon as possible. The timing of unload inactive state stores, happens when we get to load active state store provider at executors. At the time, state store coordinator will return back the state store provider list including loaded stores that are already loaded by other executors in new batch. Each state store provider in the list will go to unload. ### Why are the changes needed? Per the discussion at #30770, it makes sense to me we should unload inactive state store asap. Now we run a maintenance task periodically to unload inactive state stores. So there will be some delays between a state store becomes inactive and it is unloaded. However, we can force Spark to always allocate a state store to same executor, by using task locality configuration. This can reduce the possibility to have inactive state store. Normally, with locality configuration, we might not able to see inactive state store generally. There is still chance an executor can be failed and reallocated, but in this case, inactive state store is also lost too. So it is not an issue. Making driver-executor bi-directional for unloading inactive state store looks non-trivial, and seems to me, it is not worth, after considering what we can do with locality. This proposes a simpler but effective approach. We can check if loaded state store is already loaded at other executor during reporting active state store to the coordinator. If so, it means the loaded store is inactive now, and it is going to be unload by the next maintenance task. Then we unload that store immediately. How do we make sure the loaded state store in previous batch is loaded at other executor in this batch before reporting in this executor? With task locality and preferred location, once an executor is ready to be scheduled, Spark should assign the state store provider previously loaded at the executor. So when this executor gets a new assignment other than previously loaded state store, it means the previously loaded one is already assigned to other executor. There is still a delay between the state store is loaded at other executor, and unloading it when reporting active state store at this executor. But it should be minimized now. And there won't be multiple state store belonging to same operator are loaded at the same time at one single executor, because once the executor reports any active store, it will unload all inactive stores. This should not be an issue IMHO. This is a minimal change to unload inactive state store asap without significant change. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Unit test. Closes #30827 from viirya/SPARK-33827. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.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.