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

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)