Locally developed spark code uploads the spark Cluster service and runs it (based on the Spark website documentation)

Source: Internet
Author: User

Open idea under the SRC under main under Scala right click to create a Scala class named Simpleapp, the content is as follows

ImportOrg.apache.spark.SparkContextImportOrg.apache.spark.sparkcontext._ImportOrg.apache.spark.SparkConfObjectSimpleapp{defMain(Args:array[string]) {ValLogFile ="/home/spark/opt/spark-1.2.0-bin-hadoop2.4/readme.md"//should be some file on your system    Valconf =NewSparkconf (). Setappname ("Simple Application")Valsc =NewSparkcontext (CONF)ValLogdata = Sc.textfile (logFile, 2). Cache ()ValNumas = Logdata.filter (line = Line.contains ("a"). Count ()Valnumbs = Logdata.filter (line = Line.contains ("B"). Count () println ("Lines with a:%s, Lines with B:%s". Format (Numas, numbs))}}
Packaging files:

File-->>projectstructure-click artificats-->> click the Green Plus-click jar-->> Select from module with Dependices


Click Output Layout to see if there are no third-party jar packages, because you use the Spark cluster environment, so you don't need a third-party jar package


Re-build:

Build-->>build Artifcat. --->>build or rebuild.




After execution, you will see the Study-scala.jar in the D:\mygit\study-scala\out\artifacts\study_scala_jar directory.


Upload to the spark_home under MyApp under the Spark Cluster Server


Upload/home/spark/opt/spark-1.2.0-bin-hadoop2.4/readme.md to HDFs


To submit a spark task:

./bin/spark-submit--class "Simpleapp"--master Local[4] Myapp/study-scala.jar



Execution result is a:60,b:29

Locally developed spark code uploads the spark Cluster service and runs it (based on the Spark website documentation)

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