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[SPARK-19235][SQL][TESTS] Enable Test Cases in DDLSuite with Hive Metastore

Xiao Li authored 8 years ago

### What changes were proposed in this pull request?
So far, the test cases in DDLSuites only verify the behaviors of InMemoryCatalog. That means, they do not cover the scenarios using HiveExternalCatalog. Thus, we need to improve the existing test suite to run these cases using Hive metastore.

When porting these test cases, a bug of `SET LOCATION` is found. `path` is not set when the location is changed.

After this PR, a few changes are made, as summarized below,
- `DDLSuite` becomes an abstract class. Both `InMemoryCatalogedDDLSuite` and `HiveCatalogedDDLSuite` extend it. `InMemoryCatalogedDDLSuite` is using `InMemoryCatalog`. `HiveCatalogedDDLSuite` is using `HiveExternalCatalog`.
- `InMemoryCatalogedDDLSuite` contains all the existing test cases in `DDLSuite`.
- `HiveCatalogedDDLSuite` contains a subset of `DDLSuite`. The following test cases are excluded:

1. The following test cases only make sense for `InMemoryCatalog`:
```
  test("desc table for parquet data source table using in-memory catalog")
  test("create a managed Hive source table") {
  test("create an external Hive source table")
  test("Create Hive Table As Select")
```

2. The following test cases are unable to be ported because we are unable to alter table provider when using Hive metastore. In the future PRs we need to improve the test cases so that altering table provider is not needed:
```
  test("alter table: set location (datasource table)")
  test("alter table: set properties (datasource table)")
  test("alter table: unset properties (datasource table)")
  test("alter table: set serde (datasource table)")
  test("alter table: set serde partition (datasource table)")
  test("alter table: change column (datasource table)")
  test("alter table: add partition (datasource table)")
  test("alter table: drop partition (datasource table)")
  test("alter table: rename partition (datasource table)")
  test("drop table - data source table")
```

**TODO** : in the future PRs, we need to remove `HiveDDLSuite` and move the test cases to either `DDLSuite`,  `InMemoryCatalogedDDLSuite` or `HiveCatalogedDDLSuite`.

### How was this patch tested?
N/A

Author: Xiao Li <gatorsmile@gmail.com>
Author: gatorsmile <gatorsmile@gmail.com>

Closes #16592 from gatorsmile/refactorDDLSuite.

09829be6

09829be6 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. 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 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 guide for information on how to get started contributing to the project.