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apache / spark / e99a9f78ea2e18eabca722c5714280eb7a737499
commite99a9f78ea2e18eabca722c5714280eb7a737499[log] [tgz]
authorYuming Wang <yumwang@ebay.com>Mon Oct 21 15:53:36 2019 +0800
committerWenchen Fan <wenchen@databricks.com>Mon Oct 21 15:53:36 2019 +0800
tree7d5fddf2da87e08b18b161779f4e2cddd46698f4
parent5b4d9170ed4226c53063a28ba5d124120549cb03 [diff]
[SPARK-29498][SQL] CatalogTable to HiveTable should not change the table's ownership

### What changes were proposed in this pull request?

`CatalogTable` to `HiveTable` will change the table's ownership. How to reproduce:
```scala
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.catalog.{CatalogStorageFormat, CatalogTable, CatalogTableType}
import org.apache.spark.sql.types.{LongType, StructType}

val identifier = TableIdentifier("spark_29498", None)
val owner = "SPARK-29498"
val newTable = CatalogTable(
  identifier,
  tableType = CatalogTableType.EXTERNAL,
  storage = CatalogStorageFormat(
    locationUri = None,
    inputFormat = None,
    outputFormat = None,
    serde = None,
    compressed = false,
    properties = Map.empty),
  owner = owner,
  schema = new StructType().add("i", LongType, false),
  provider = Some("hive"))

spark.sessionState.catalog.createTable(newTable, false)
// The owner is not SPARK-29498
println(spark.sessionState.catalog.getTableMetadata(identifier).owner)
```

This PR makes it set the `HiveTable`'s owner to `CatalogTable`'s owner if it's owner is not empty when converting `CatalogTable` to `HiveTable`.

### Why are the changes needed?
We should not change the ownership of the table when converting `CatalogTable` to `HiveTable`.

### Does this PR introduce any user-facing change?
No

### How was this patch tested?
unit test

Closes #26160 from wangyum/SPARK-29498.

Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2 files changed
tree: 7d5fddf2da87e08b18b161779f4e2cddd46698f4
  1. .github/
  2. assembly/
  3. bin/
  4. build/
  5. common/
  6. conf/
  7. core/
  8. data/
  9. dev/
  10. docs/
  11. examples/
  12. external/
  13. graph/
  14. graphx/
  15. hadoop-cloud/
  16. launcher/
  17. licenses/
  18. licenses-binary/
  19. mllib/
  20. mllib-local/
  21. project/
  22. python/
  23. R/
  24. repl/
  25. resource-managers/
  26. sbin/
  27. sql/
  28. streaming/
  29. tools/
  30. .gitattributes
  31. .gitignore
  32. appveyor.yml
  33. CONTRIBUTING.md
  34. LICENSE
  35. LICENSE-binary
  36. NOTICE
  37. NOTICE-binary
  38. pom.xml
  39. README.md
  40. scalastyle-config.xml
README.md

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.

https://spark.apache.org/

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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.)

You can build Spark using more than one thread by using the -T option with Maven, see “Parallel builds in Maven 3”. 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.