spark快速入門
阿新 • • 發佈:2018-08-24
stream 語言 目錄 req 應用 虛擬機 hang 終端 key spark框架是用scala寫的,運行在Java虛擬機(JVM)上。支持Python、Java、Scala或R多種語言編寫客戶端應用。
下載Spark
訪問http://spark.apache.org/downloads.html選擇預編譯的版本進行下載。
解壓Spark
打開終端,將工作路徑轉到下載的spark壓縮包所在的目錄,然後解壓壓縮包。
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
註:tar命令中x標記指定tar命令執行解壓縮操作,f標記指定壓縮包的文件名。
spark主要目錄結構
- README.md
包含用來入門spark的簡單使用說明
- bin
包含可用來和spark進行各種方式交互的一系列可執行文件
- core、streaming、python
包含spark項目主要組件的源代碼
- examples
包含一些可查看和運行的spark程序,對學習spark的API非常有幫助
運行案例及交互式Shell
運行案例
./bin/run-example SparkPi 10
scala shell
./bin/spark-shell --master local[2]
# --master選項指定運行模式。local是指使用一個線程本地運行;local[N]是指使用N個線程本地運行。
python shell
./bin/pyspark --master local[2]
R shell
./bin/sparkR --master local[2]
提交應用腳本
#支持多種語言提交
./bin/spark-submit examples/src/main/python/pi.py 10
./bin/spark-submit examples/src/main/r/dataframe.R
...
使用spark shell進行交互式分析
scala
使用spark-shell腳本進行交互式分析。
基礎
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]
#拉鏈方式
scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"?
res3: Long = 15
進階
#使用DataSet的轉換和動作查找最多單詞的行
scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b)
res4: Long = 15
#統計單詞個數
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腳本進行交互式分析
基礎
>>> 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過濾
>>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))
#拉鏈方式
>>> textFile.filter(textFile.value.contains("Spark")).count() # How many lines contain "Spark"?
15
進階
#查找最多單詞的行
>>> from pyspark.sql.functions import *
>>> textFile.select(size(split(textFile.value, "\s+")).name("numWords")).agg(max(col("numWords"))).collect()
[Row(max(numWords)=15)]
#統計單詞個數
>>> 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), ...]
獨立應用
spark除了交互式運行之外,spark也可以在Java、Scala或Python的獨立程序中被連接使用。
獨立應用與shell的主要區別在於需要自行初始化SparkContext。
scala
分別統計包含單詞a和單詞b的行數
/* 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()
}
}
運行應用
# 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
分別統計包含單詞a和單詞b的行數
/* 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();
}
}
運行應用
# 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
分別統計包含單詞a和單詞b的行數
setup.py腳本添加內容
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()
運行應用
# 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快速入門