aa13e207c9
### What changes were proposed in this pull request? This PR provides us with a way to check if a data source is going to reject the delete via `deleteWhere` at planning time. ### Why are the changes needed? The only way to support delete statements right now is to implement ``SupportsDelete``. According to its Javadoc, that interface is meant for cases when we can delete data without much effort (e.g. like deleting a complete partition in a Hive table). This PR actually provides us with a way to check if a data source is going to reject the delete via `deleteWhere` at planning time instead of just getting an exception during execution. In the future, we can use this functionality to decide whether Spark should rewrite this delete and execute a distributed query or it can just pass a set of filters. Consider an example of a partitioned Hive table. If we have a delete predicate like `part_col = '2020'`, we can just drop the matching partition to satisfy this delete. In this case, the data source should return `true` from `canDeleteWhere` and use the filters it accepts in `deleteWhere` to drop the partition. I consider this as a delete without significant effort. At the same time, if we have a delete predicate like `id = 10`, Hive tables would not be able to execute this delete using a metadata only operation without rewriting files. In that case, the data source should return `false` from `canDeleteWhere` and we should use a more sophisticated row-level API to find out which records should be removed (the API is yet to be discussed, but we need this PR as a basis). If we decide to support subqueries and all delete use cases by simply extending the existing API, this will mean all data sources will have to implement a lot of Spark logic to determine which records changed. I don't think we want to go that way as the Spark logic to determine which records should be deleted is independent of the underlying data source. So the assumption is that Spark will execute a plan to find which records must be deleted for data sources that return `false` from `canDeleteWhere`. ### Does this PR introduce _any_ user-facing change? Yes but it is backward compatible. ### How was this patch tested? This PR comes with a new test. Closes #30562 from aokolnychyi/spark-33623. Authored-by: Anton Okolnychyi <aokolnychyi@apple.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.