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[SPARK-18922][TESTS] Fix new test failures on Windows due to path and resource not closed

hyukjinkwon authored 8 years ago

## What changes were proposed in this pull request?

This PR proposes to fix new test failures on WIndows as below:

**Before**

```
KafkaRelationSuite:
 - test late binding start offsets *** FAILED *** (7 seconds, 679 milliseconds)
   Cause: java.nio.file.FileSystemException: C:\projects\spark\target\tmp\spark-4c4b0cd1-4cb7-4908-949d-1b0cc8addb50\topic-4-0\00000000000000000000.log -> C:\projects\spark\target\tmp\spark-4c4b0cd1-4cb7-4908-949d-1b0cc8addb50\topic-4-0\00000000000000000000.log.deleted: The process cannot access the file because it is being used by another process.

KafkaSourceSuite:
 - deserialization of initial offset with Spark 2.1.0 *** FAILED *** (3 seconds, 542 milliseconds)
   java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-97ef64fc-ae61-4ce3-ac59-287fd38bd824

 - deserialization of initial offset written by Spark 2.1.0 *** FAILED *** (60 milliseconds)
   java.nio.file.InvalidPathException: Illegal char <:> at index 2: /C:/projects/spark/external/kafka-0-10-sql/target/scala-2.11/test-classes/kafka-source-initial-offset-version-2.1.0.b

HiveDDLSuite:
 - partitioned table should always put partition columns at the end of table schema *** FAILED *** (657 milliseconds)
   org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark	arget	mpspark-f1b83d09-850a-4bba-8e43-a2a28dfaa757;

DDLSuite:
 - create a data source table without schema *** FAILED *** (94 milliseconds)
   org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark	arget	mpspark-a3f3c161-afae-4d6f-9182-e8642f77062b;

 - SET LOCATION for managed table *** FAILED *** (219 milliseconds)
   org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree:
 Exchange SinglePartit
 +- *HashAggregate(keys=[], functions=[partial_count(1)], output=[count#99367L])
    +- *FileScan parquet default.tbl[] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/C:projectsspark	arget	mpspark-15be2f2f-4ea9-4c47-bfee-1b7b49363033], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<>

 - insert data to a data source table which has a not existed location should succeed *** FAILED *** (16 milliseconds)
   org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark	arget	mpspark-34987671-e8d1-4624-ba5b-db1012e1246b;

 - insert into a data source table with no existed partition location should succeed *** FAILED *** (16 milliseconds)
   org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark	arget	mpspark-4c6ccfbf-4091-4032-9fbc-3d40c58267d5;

 - read data from a data source table which has a not existed location should succeed *** FAILED *** (0 milliseconds)

 - read data from a data source table with no existed partition location should succeed *** FAILED *** (0 milliseconds)
   org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark	arget	mpspark-6af39e37-abd1-44e8-ac68-e2dfcf67a2f3;

InputOutputMetricsSuite:
 - output metrics on records written *** FAILED *** (0 milliseconds)
   java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-cd69ee77-88f2-4202-bed6-19c0ee05ef55\InputOutputMetricsSuite, expected: file:///

 - output metrics on records written - new Hadoop API *** FAILED *** (16 milliseconds)
   java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-b69e8fcb-047b-4de8-9cdf-5f026efb6762\InputOutputMetricsSuite, expected: file:///
```

**After**

```
KafkaRelationSuite:
 - test late binding start offsets !!! CANCELED !!! (62 milliseconds)

KafkaSourceSuite:
 - deserialization of initial offset with Spark 2.1.0 (5 seconds, 341 milliseconds)
 - deserialization of initial offset written by Spark 2.1.0 (910 milliseconds)

HiveDDLSuite:
 - partitioned table should always put partition columns at the end of table schema (2 seconds)

DDLSuite:
 - create a data source table without schema (828 milliseconds)
 - SET LOCATION for managed table (406 milliseconds)
 - insert data to a data source table which has a not existed location should succeed (406 milliseconds)
 - insert into a data source table with no existed partition location should succeed (453 milliseconds)
 - read data from a data source table which has a not existed location should succeed (94 milliseconds)
 - read data from a data source table with no existed partition location should succeed (265 milliseconds)

InputOutputMetricsSuite:
 - output metrics on records written (172 milliseconds)
 - output metrics on records written - new Hadoop API (297 milliseconds)
```

## How was this patch tested?

Fixed tests in `InputOutputMetricsSuite`, `KafkaRelationSuite`,  `KafkaSourceSuite`, `DDLSuite.scala` and `HiveDDLSuite`.

Manually tested via AppVeyor as below:

`InputOutputMetricsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/ex8nvwa6tsh7rmto
`KafkaRelationSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/h8dlcowew52y8ncw
`KafkaSourceSuite`: https://ci.appveyor.com/project/spark-test/spark/build/634-20170219-windows-test/job/9ybgjl7yeubxcre4
`DDLSuite`: https://ci.appveyor.com/project/spark-test/spark/build/635-20170219-windows-test
`HiveDDLSuite`: https://ci.appveyor.com/project/spark-test/spark/build/633-20170219-windows-test/job/up6o9n47er087ltb

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #16999 from HyukjinKwon/windows-fix.

17b93b5f

17b93b5f 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.