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[SPARK-21475][CORE] Use NIO's Files API to replace...

jerryshao authored 7 years ago

[SPARK-21475][CORE] Use NIO's Files API to replace FileInputStream/FileOutputStream in some critical paths

## What changes were proposed in this pull request?

Java's `FileInputStream` and `FileOutputStream` overrides finalize(), even this file input/output stream is closed correctly and promptly, it will still leave some memory footprints which will only get cleaned in Full GC. This will introduce two side effects:

1. Lots of memory footprints regarding to Finalizer will be kept in memory and this will increase the memory overhead. In our use case of external shuffle service, a busy shuffle service will have bunch of this object and potentially lead to OOM.
2. The Finalizer will only be called in Full GC, and this will increase the overhead of Full GC and lead to long GC pause.

https://bugs.openjdk.java.net/browse/JDK-8080225

https://www.cloudbees.com/blog/fileinputstream-fileoutputstream-considered-harmful

So to fix this potential issue, here propose to use NIO's Files#newInput/OutputStream instead in some critical paths like shuffle.

Left unchanged FileInputStream in core which I think is not so critical:

```
./core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:467:    val file = new DataInputStream(new FileInputStream(filename))
./core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:942:    val in = new FileInputStream(new File(path))
./core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala:76:    val fileIn = new FileInputStream(file)
./core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala:248:        val fis = new FileInputStream(file)
./core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala:910:                input = new FileInputStream(new File(t))
./core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala:20:import java.io.{FileInputStream, InputStream}
./core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala:132:        case Some(f) => new FileInputStream(f)
./core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala:20:import java.io.{FileInputStream, InputStream}
./core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala:77:        val fis = new FileInputStream(f)
./core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala:27:import org.apache.spark.io.NioBufferedFileInputStream
./core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala:94:      new DataInputStream(new NioBufferedFileInputStream(index))
./core/src/main/scala/org/apache/spark/storage/DiskStore.scala:111:        val channel = new FileInputStream(file).getChannel()
./core/src/main/scala/org/apache/spark/storage/DiskStore.scala:219:    val channel = new FileInputStream(file).getChannel()
./core/src/main/scala/org/apache/spark/TestUtils.scala:20:import java.io.{ByteArrayInputStream, File, FileInputStream, FileOutputStream}
./core/src/main/scala/org/apache/spark/TestUtils.scala:106:      val in = new FileInputStream(file)
./core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala:89:        inputStream = new FileInputStream(activeFile)
./core/src/main/scala/org/apache/spark/util/Utils.scala:329:      if (in.isInstanceOf[FileInputStream] && out.isInstanceOf[FileOutputStream]
./core/src/main/scala/org/apache/spark/util/Utils.scala:332:        val inChannel = in.asInstanceOf[FileInputStream].getChannel()
./core/src/main/scala/org/apache/spark/util/Utils.scala:1533:      gzInputStream = new GZIPInputStream(new FileInputStream(file))
./core/src/main/scala/org/apache/spark/util/Utils.scala:1560:      new GZIPInputStream(new FileInputStream(file))
./core/src/main/scala/org/apache/spark/util/Utils.scala:1562:      new FileInputStream(file)
./core/src/main/scala/org/apache/spark/util/Utils.scala:2090:    val inReader = new InputStreamReader(new FileInputStream(file), StandardCharsets.UTF_8)
```

Left unchanged FileOutputStream in core:

```
./core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:957:    val out = new FileOutputStream(file)
./core/src/main/scala/org/apache/spark/api/r/RBackend.scala:20:import java.io.{DataOutputStream, File, FileOutputStream, IOException}
./core/src/main/scala/org/apache/spark/api/r/RBackend.scala:131:      val dos = new DataOutputStream(new FileOutputStream(f))
./core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala:62:    val fileOut = new FileOutputStream(file)
./core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala:160:          val outStream = new FileOutputStream(outPath)
./core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala:239:    val zipOutputStream = new ZipOutputStream(new FileOutputStream(zipFile, false))
./core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala:949:        val out = new FileOutputStream(tempFile)
./core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala:20:import java.io.{File, FileOutputStream, InputStream, IOException}
./core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala:106:    val out = new FileOutputStream(file, true)
./core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala:109:     * Therefore, for local files, use FileOutputStream instead. */
./core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala:112:        new FileOutputStream(uri.getPath)
./core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala:20:import java.io.{BufferedOutputStream, File, FileOutputStream, OutputStream}
./core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala:71:  private var fos: FileOutputStream = null
./core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala:102:    fos = new FileOutputStream(file, true)
./core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala:213:      var truncateStream: FileOutputStream = null
./core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala:215:        truncateStream = new FileOutputStream(file, true)
./core/src/main/scala/org/apache/spark/storage/DiskStore.scala:153:    val out = new FileOutputStream(file).getChannel()
./core/src/main/scala/org/apache/spark/TestUtils.scala:20:import java.io.{ByteArrayInputStream, File, FileInputStream, FileOutputStream}
./core/src/main/scala/org/apache/spark/TestUtils.scala:81:    val jarStream = new JarOutputStream(new FileOutputStream(jarFile))
./core/src/main/scala/org/apache/spark/TestUtils.scala:96:    val jarFileStream = new FileOutputStream(jarFile)
./core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala:20:import java.io.{File, FileOutputStream, InputStream, IOException}
./core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala:31:  volatile private var outputStream: FileOutputStream = null
./core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala:97:    outputStream = new FileOutputStream(file, true)
./core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala:90:        gzOutputStream = new GZIPOutputStream(new FileOutputStream(gzFile))
./core/src/main/scala/org/apache/spark/util/Utils.scala:329:      if (in.isInstanceOf[FileInputStream] && out.isInstanceOf[FileOutputStream]
./core/src/main/scala/org/apache/spark/util/Utils.scala:333:        val outChannel = out.asInstanceOf[FileOutputStream].getChannel()
./core/src/main/scala/org/apache/spark/util/Utils.scala:527:      val out = new FileOutputStream(tempFile)
```

Here in `DiskBlockObjectWriter`, it uses `FileDescriptor` so it is not easy to change to NIO Files API.

For the `FileInputStream` and `FileOutputStream` in common/shuffle* I changed them all.

## How was this patch tested?

Existing tests and manual verification.

Author: jerryshao <sshao@hortonworks.com>

Closes #18684 from jerryshao/SPARK-21475.

5fd0294f

5fd0294f 7 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.