大数据spark计算引擎快速入门_spark计算引擎 路径搜索

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 10scala 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 outputsscala> 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 126textFile.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.SparkSessionobject 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 SparkSessionlogFile = “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

/bin/spark-submit \ --master local[4] \ SimpleApp.py ... Lines with a: 46, Lines with b: 23 ```
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