spark快速入门
spark框架是用scala写的，运行在Java虚拟机（JVM）上。支持Python、Java、Scala或R多种语言编写客户端应用。
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 = "YOURSPARKHOME/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
$ YOURSPARKHOME/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 = "YOURSPARKHOME/README.md"; // Should be some file on your system SparkSession spark = SparkSession.builder().appName("Simple Application").getOrCreate(); Dataset 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
$ YOURSPARKHOME/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 = "YOURSPARKHOME/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
$ YOURSPARKHOME/bin/spark-submit \ --master local[4] \ SimpleApp.py ... Lines with a: 46, Lines with b: 23 ```