Apache Spark 1.4 を Debian 8(Jessie)にインストールする

Apache Spark を Debian 7(wheezy)にインストール


Java 8のインストール

Oracle Javaをインストールしました。
Debian 8(jessie)にJava 8(OracleVM)をインストールする


# java -version
java version "1.8.0_45"
Java(TM) SE Runtime Environment (build 1.8.0_45-b14)
Java HotSpot(TM) 64-Bit Server VM (build 25.45-b02, mixed mode)



Hadoop 2.6がつかえるものをダウンロードしてみました。



# cd /opt
# wget
# tar zxf spark-1.4.0-bin-hadoop2.6.tgz
# cd spark-1.4.0-bin-hadoop2.6

Apache Spark spark-shell起動時のログの警告に対処する(

# cp conf/ conf/
# vi conf/


# Set everything to be logged to the console
log4j.rootCategory=ERROR, console
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# Settings to quiet third party logs that are too verbose$exprTyper=INFO$SparkILoopInterpreter=INFO


# bin/spark-shell
Welcome to
     ____             __
     / __/__ ___ _____/ /__
    _\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 1.4.0

Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_45)
Type in expressions to have them evaluated.
Type :help for more information.
Spark context available as sc.
SQL context available as sqlContext.


scala> val txtFile = sc.textFile("")
txtFile: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[1] at textFile at <console>:21

scala> txtFile.count()
res0: Long = 98

scala> txtFile.filter(line => line.contains("Spark")).count()
res1: Long = 19

scala> txtFile.filter(line => line.startsWith("Spark")).foreach(println)
Spark is a fast and general cluster computing system for Big Data. It provides
Spark is built using [Apache Maven](
Spark also comes with several sample programs in the `examples` directory.
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported


scala> exit()


# Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides
high-level APIs in Scala, Java, and Python, 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 structured
data processing, MLlib for machine learning, GraphX for graph processing,
and Spark Streaming for stream processing.


## 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:

    mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)
More detailed documentation is available from the project site, at
["Building Spark"](

## Interactive Scala Shell

The easiest way to start using Spark is through the Scala 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:

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-cluster" or "yarn-client" 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](#building-spark). Once Spark is built, tests
can be run using:


Please see the guidance on how to
[run all automated 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. See also
["Third Party Hadoop Distributions"](
for guidance on building a Spark application that works with a particular

## Configuration

Please refer to the [Configuration guide](
in the online documentation for an overview on how to configure Spark.


テーマ:サーバ - ジャンル:コンピュータ

  1. 2015/06/23(火) 22:44:26|
  2. Scala
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