明夷待访录（二）：Spark 安装与使用

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安装和使用

安装 Hadoop 和 Spark

这里注意 openjdk 的版本必须是 8！

  1. 安装 java，配置JAVA_HOME 

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    export JAVA_HOME=JDK 安装路径

  2. 安装 Hadoop到 /usr/local/hadoop
  3. 修改配置文件 core-site.xml 和hdfs-site.xml

  4. 启动 HDFS 

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    /usr/local/hadoop/sbin/start-dfs.sh

停止是 

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/usr/local/hadoop/sbin/stop-all.sh

  1. Spark 安装到/usr/local/spark
  2. 修改配置文件cp spark-env.sh.template spark-env.sh，在行首添加export SPARK_DIST_CLASSPATH=$(/usr/local/hadoop/bin/hadoop classpath)
  3. 在 .bashrc 中写入HADOOP_HOME SPARK_HOME

在 pyspark 中运行代码

conda 安装，直接启动即可

Spark 独立应用程序编程

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#!/usr/bin/env python3
# coding: utf-8

from pyspark import SparkContext

sc = SparkContext('local', 'test')

logFile = "file:///usr/local/spark/README.md"
logData = sc.textFile(logFile, 2).cache()

numAs = logData.filter(lambda line: 'a' in line).count()
numBs = logData.filter(lambda line: 'b' in line).count()

print('Lines with a: %s, Lines with b: %s' % (numAs, numBs))

Spark 的安装与使用

第一个 Spark 应用程序：WordCount

任务要求

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! mkdir wordcount
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! cd wordcount
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!pwd
/home/hadoop/spark/src/test

在 pyspark 中执行词频统计

启动 pyspark

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!pyspark
Python 3.7.3 (default, Mar 27 2019, 22:11:17) 
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
2019-06-18 16:39:00,498 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.4.3
      /_/

Using Python version 3.7.3 (default, Mar 27 2019 22:11:17)
SparkSession available as 'spark'.
>>> 
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/spark/python/pyspark/context.py", line 270, in signal_handler
    raise KeyboardInterrupt()
KeyboardInterrupt
>>>

加载本地文件

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from pyspark import SparkContext
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sc = SparkContext("local", "test")
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textFile = sc.textFile("file:///usr/local/spark/README.md")
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textFile.first()
'# Apache Spark'
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textFile.saveAsTextFile("file:///home/hadoop/spark/data/wordcount/writeback")

加载 HDFS 文件

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!/usr/local/hadoop/bin/hdfs dfs -ls .
Found 2 items
drwxr-xr-x   - hadoop supergroup          0 2019-06-18 15:48 input
drwxr-xr-x   - hadoop supergroup          0 2019-06-18 15:49 output

查看 Linux 当前登录用户 hadoop 在 HDFS 文件系统中与 hadoop 对应的目录下的文件

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!/usr/local/hadoop/bin/hdfs dfs -ls /
Found 1 items
drwxr-xr-x   - hadoop supergroup          0 2019-06-18 15:48 /user

上传到分布式文件系统 HDFS 中

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!/usr/local/hadoop/bin/hdfs dfs -put /usr/local/spark/README.md .
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!/usr/local/hadoop/bin/hdfs dfs -ls .
Found 3 items
-rw-r--r--   1 hadoop supergroup       3952 2019-06-18 16:48 README.md
drwxr-xr-x   - hadoop supergroup          0 2019-06-18 15:48 input
drwxr-xr-x   - hadoop supergroup          0 2019-06-18 15:49 output
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!/usr/local/hadoop/bin/hdfs dfs -cat ./README.md
# 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](http://spark.apache.org/documentation.html).
This README file only contains basic setup instructions.

## Building Spark

Spark is built using [Apache Maven](http://maven.apache.org/).
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"](https://cwiki.apache.org/confluence/display/MAVEN/Parallel+builds+in+Maven+3).
More detailed documentation is available from the project site, at
["Building Spark"](http://spark.apache.org/docs/latest/building-spark.html).

For general development tips, including info on developing Spark using an IDE, see ["Useful Developer Tools"](http://spark.apache.org/developer-tools.html).

## 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](#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](http://spark.apache.org/developer-tools.html#individual-tests).

There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md

## 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 and Enabling YARN"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version-and-enabling-yarn)
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](http://spark.apache.org/docs/latest/configuration.html)
in the online documentation for an overview on how to configure Spark.

## Contributing

Please review the [Contribution to Spark guide](http://spark.apache.org/contributing.html)
for information on how to get started contributing to the project.
1
textFile.saveAsTextFile("writeback")
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!/usr/local/hadoop/bin/hdfs dfs -ls .
Found 4 items
-rw-r--r--   1 hadoop supergroup       3952 2019-06-18 16:48 README.md
drwxr-xr-x   - hadoop supergroup          0 2019-06-18 15:48 input
drwxr-xr-x   - hadoop supergroup          0 2019-06-18 15:49 output
drwxr-xr-x   - hadoop supergroup          0 2019-06-18 16:50 writeback
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!/usr/local/hadoop/bin/hdfs dfs -ls ./writeback
Found 2 items
-rw-r--r--   1 hadoop supergroup          0 2019-06-18 16:50 writeback/_SUCCESS
-rw-r--r--   1 hadoop supergroup       3952 2019-06-18 16:50 writeback/part-00000

词频统计

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wordCount = textFile.flatMap(lambda line: line.split("")).map(lambda word: (word,1)).reduceByKey(lambda a, b : a + b)
1
wordCount.collect()
[('#', 1),
 ('Apache', 1),
 ('Spark', 16),
 ('', 72),
 ('is', 7),
 ('a', 9),
 ('fast', 1),
 ('and', 10),
 ('general', 3),
 ('cluster', 2),
 ('computing', 1),
 ('system', 1),
 ('for', 12),
 ('Big', 1),
 ('Data.', 1),
 ('It', 2),
 ('provides', 1),
 ('high-level', 1),
 ('APIs', 1),
 ('in', 6),
 ('Scala,', 1),
 ('Java,', 1),
 ('Python,', 2),
 ('R,', 1),
 ('an', 4),
 ('optimized', 1),
 ('engine', 1),
 ('that', 2),
 ('supports', 2),
 ('computation', 1),
 ('graphs', 1),
 ('data', 1),
 ('analysis.', 1),
 ('also', 5),
 ('rich', 1),
 ('set', 2),
 ('of', 5),
 ('higher-level', 1),
 ('tools', 1),
 ('including', 4),
 ('SQL', 2),
 ('DataFrames,', 1),
 ('MLlib', 1),
 ('machine', 1),
 ('learning,', 1),
 ('GraphX', 1),
 ('graph', 1),
 ('processing,', 1),
 ('Streaming', 1),
 ('stream', 1),
 ('processing.', 1),
 ('<http://spark.apache.org/>', 1),
 ('##', 9),
 ('Online', 1),
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 ('You', 4),
 ('can', 7),
 ('find', 1),
 ('the', 24),
 ('latest', 1),
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 ('on', 7),
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 ('./bin/run-example', 2),
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 ('Hive', 2),
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编写独立应用程序执行词频统计

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wordCount.foreach(print)
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