spark快速入门

标签：方式   选项   连接   本地   logfile   ext   ORC   numa   cache   

下载Spark

  访问http://spark.apache.org/downloads.html选择预编译的版本进行下载。

解压Spark

  打开终端，将工作路径转到下载的spark压缩包所在的目录，然后解压压缩包。
可使用如下命令：

cd ~
tar -xf spark-2.2.2-bin-hadoop2.7.tgz -C /opt/module/
cd spark-2.2.2-bin-hadoop2.7
ls

  注：tar命令中x标记指定tar命令执行解压缩操作，f标记指定压缩包的文件名。

spark主要目录结构

• README.md

  包含用来入门spark的简单使用说明

• bin

  包含可用来和spark进行各种方式交互的一系列可执行文件

• core、streaming、python

  包含spark项目主要组件的源代码

• examples

  包含一些可查看和运行的spark程序，对学习spark的API非常有帮助

运行案例及交互式Shell

运行案例

./bin/run-example SparkPi 10

scala shell

./bin/spark-shell --master local[2]

 # --master选项指定运行模式。local是指使用一个线程本地运行；local[N]是指使用N个线程本地运行。

python shell

./bin/pyspark --master local[2]

R shell

./bin/sparkR --master local[2]

提交应用脚本

#支持多种语言提交
./bin/spark-submit examples/src/main/python/pi.py 10
./bin/spark-submit examples/src/main/r/dataframe.R
...

使用spark shell进行交互式分析

scala

  使用spark-shell脚本进行交互式分析。

基础

scala> val textFile = spark.read.textFile("README.md")
textFile: org.apache.spark.sql.Dataset[String] = [value: string]

scala> textFile.count() // Number of items in this Dataset
res0: Long = 126 // May be different from yours as README.md will change over time, similar to other outputs

scala> textFile.first() // First item in this Dataset
res1: String = # Apache Spark

#使用filter算子返回原DataSet的子集
scala> val linesWithSpark = textFile.filter(line => line.contains("Spark"))
linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]

#拉链方式
scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"?
res3: Long = 15

进阶

#使用DataSet的转换和动作查找最多单词的行
scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b)
res4: Long = 15

#统计单词个数
scala> val wordCounts = textFile.flatMap(line => line.split(" ")).groupByKey(identity).count()
wordCounts: org.apache.spark.sql.Dataset[(String, Long)] = [value: string, count(1): bigint]

scala> wordCounts.collect()
res6: Array[(String, Int)] = Array((means,1), (under,2), (this,3), (Because,1), (Python,2), (agree,1), (cluster.,1), ...)

python

  使用pyspark脚本进行交互式分析

基础

>>> textFile = spark.read.text("README.md")

>>> textFile.count()  # Number of rows in this DataFrame
126

>>> textFile.first()  # First row in this DataFrame
Row(value=u‘# Apache Spark‘)

#filter过滤
>>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))

#拉链方式
>>> textFile.filter(textFile.value.contains("Spark")).count()  # How many lines contain "Spark"?
15

进阶

#查找最多单词的行
>>> from pyspark.sql.functions import *

>>> textFile.select(size(split(textFile.value, "\s+")).name("numWords")).agg(max(col("numWords"))).collect()
[Row(max(numWords)=15)]

#统计单词个数
>>> wordCounts = textFile.select(explode(split(textFile.value, "\s+")).alias("word")).groupBy("word").count()

>>> wordCounts.collect()
[Row(word=u‘online‘, count=1), Row(word=u‘graphs‘, count=1), ...]

独立应用

  spark除了交互式运行之外，spark也可以在Java、Scala或Python的独立程序中被连接使用。
  独立应用与shell的主要区别在于需要自行初始化SparkContext。

scala

分别统计包含单词a和单词b的行数

/* SimpleApp.scala */
import org.apache.spark.sql.SparkSession

object SimpleApp {
  def main(args: Array[String]) {
    val logFile = "YOUR_SPARK_HOME/README.md" // Should be some file on your system
    val spark = SparkSession.builder.appName("Simple Application").getOrCreate()
    val logData = spark.read.textFile(logFile).cache()
    val numAs = logData.filter(line => line.contains("a")).count()
    val numBs = logData.filter(line => line.contains("b")).count()
    println(s"Lines with a: $numAs, Lines with b: $numBs")
    spark.stop()
  }
}

运行应用

# Use spark-submit to run your application
$ YOUR_SPARK_HOME/bin/spark-submit   --class "SimpleApp"   --master local[4]   target/scala-2.11/simple-project_2.11-1.0.jar
...
Lines with a: 46, Lines with b: 23

java

分别统计包含单词a和单词b的行数

/* SimpleApp.java */
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;

public class SimpleApp {
  public static void main(String[] args) {
    String logFile = "YOUR_SPARK_HOME/README.md"; // Should be some file on your system
    SparkSession spark = SparkSession.builder().appName("Simple Application").getOrCreate();
    Dataset<String> logData = spark.read().textFile(logFile).cache();

    long numAs = logData.filter(s -> s.contains("a")).count();
    long numBs = logData.filter(s -> s.contains("b")).count();

    System.out.println("Lines with a: " + numAs + ", lines with b: " + numBs);

    spark.stop();
  }
}

运行应用

# Use spark-submit to run your application
$ YOUR_SPARK_HOME/bin/spark-submit   --class "SimpleApp"   --master local[4]   target/simple-project-1.0.jar
...
Lines with a: 46, Lines with b: 23

python

分别统计包含单词a和单词b的行数

setup.py脚本添加内容
install_requires=[
    ‘pyspark=={site.SPARK_VERSION}‘
]

"""SimpleApp.py"""
from pyspark.sql import SparkSession

logFile = "YOUR_SPARK_HOME/README.md"  # Should be some file on your system
spark = SparkSession.builder().appName(appName).master(master).getOrCreate()
logData = spark.read.text(logFile).cache()

numAs = logData.filter(logData.value.contains(‘a‘)).count()
numBs = logData.filter(logData.value.contains(‘b‘)).count()

print("Lines with a: %i, lines with b: %i" % (numAs, numBs))

spark.stop()

运行应用

# Use spark-submit to run your application
$ YOUR_SPARK_HOME/bin/spark-submit   --master local[4]   SimpleApp.py
...
Lines with a: 46, Lines with b: 23

spark快速入门

标签：方式   选项   连接   本地   logfile   ext   ORC   numa   cache   

原文地址：http://blog.51cto.com/12967015/2163611