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[SPARK-17470][SQL] unify path for data source table and locationUri for hive serde table

Wenchen Fan authored 8 years ago

## What changes were proposed in this pull request?

Due to a limitation of hive metastore(table location must be directory path, not file path), we always store `path` for data source table in storage properties, instead of the `locationUri` field. However, we should not expose this difference to `CatalogTable` level, but just treat it as a hack in `HiveExternalCatalog`, like we store table schema of data source table in table properties.

This PR unifies `path` and `locationUri` outside of `HiveExternalCatalog`, both data source table and hive serde table should use the `locationUri` field.

This PR also unifies the way we handle default table location for managed table. Previously, the default table location of hive serde managed table is set by external catalog, but the one of data source table is set by command. After this PR, we follow the hive way and the default table location is always set by external catalog.

For managed non-file-based tables, we will assign a default table location and create an empty directory for it, the table location will be removed when the table is dropped. This is reasonable as metastore doesn't care about whether a table is file-based or not, and an empty table directory has no harm.
For external non-file-based tables, ideally we can omit the table location, but due to a hive metastore issue, we will assign a random location to it, and remove it right after the table is created. See SPARK-15269 for more details. This is fine as it's well isolated in `HiveExternalCatalog`.

To keep the existing behaviour of the `path` option, in this PR we always add the `locationUri` to storage properties using key `path`, before passing storage properties to `DataSource` as data source options.
## How was this patch tested?

existing tests.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #15024 from cloud-fan/path.

3a1bc6f4

3a1bc6f4 8 years ago

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Apache Spark

Spark is a fast and general cluster computing system for Big Data. 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 Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. 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 developing Spark using an IDE, see Eclipse and IntelliJ.

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 1000:

scala> sc.parallelize(1 to 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 1000:

>>> sc.parallelize(range(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.

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" 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 wiki for information on how to get started contributing to the project.