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Both sides previous revision Previous revision 2022/12/14 13:25 straka [Usage Examples] 2022/12/14 13:24 straka 2022/12/14 13:05 straka [Usage Examples] 2022/12/14 13:02 straka [Usage Examples] 2018/11/24 22:42 straka [Usage Examples] 2017/10/16 21:24 ufal [Using Scala] 2017/10/16 21:23 ufal [Usage Examples] 2015/07/27 18:46 straka 2015/07/27 18:34 straka 2014/11/11 09:32 straka 2014/11/11 09:31 straka 2014/11/11 09:29 straka 2014/11/10 17:40 straka 2014/11/10 16:03 straka 2014/11/10 15:43 straka created Go Next revision		Previous revision 2022/12/14 13:25 straka [Usage Examples] 2022/12/14 13:24 straka 2022/12/14 13:05 straka [Usage Examples] 2022/12/14 13:02 straka [Usage Examples] 2018/11/24 22:42 straka [Usage Examples] 2017/10/16 21:24 ufal [Using Scala] 2017/10/16 21:23 ufal [Usage Examples] 2015/07/27 18:46 straka 2015/07/27 18:34 straka 2014/11/11 09:32 straka 2014/11/11 09:31 straka 2014/11/11 09:29 straka 2014/11/10 17:40 straka 2014/11/10 16:03 straka 2014/11/10 15:43 straka created Go Last revision Both sides next revision
spark:using-scala [2022/12/14 13:02] straka [Usage Examples]		spark:using-scala [2022/12/14 13:24] straka
Line 13:		Line 13:
	Consider the following simple script computing 10 most frequent words of Czech Wikipedia:		Consider the following simple script computing 10 most frequent words of Czech Wikipedia:
	<file scala>		<file scala>
-	(sc.textFile("/lnet/troja/data/npfl118/wiki/cs/wiki.txt", 3*sc.defaultParallelism)	+	(sc.textFile("/net/projects/spark-example-data/wiki-cs", 3*sc.defaultParallelism)
	.flatMap(_.split("\\s"))		.flatMap(_.split("\\s"))
	.map((_,1)).reduceByKey(_+_)		.map((_,1)).reduceByKey(_+_)
Line 72:		Line 72:
	version := "1.0"		version := "1.0"
-	scalaVersion := "2.11.12"	+	scalaVersion := "2.12.17"
-		+
-	libraryDependencies += "org.apache.spark" %% "spark-core" % "2.3.2"	+	libraryDependencies += "org.apache.spark" %% "spark-core" % "3.3.1"
	</file>		</file>
Line 80:		Line 80:
	<file>sbt package</file>		<file>sbt package</file>
-	* run ''word_count'' application inside existing Spark cluster (i.e., inside ''spark-qsub'' or ''spark-qrsh''), or start local Spark cluster using as many threads as there are cores if there is none:	+	* run ''word_count'' application inside existing Spark cluster (i.e., inside ''spark-sbatch'' or ''spark-srun''), or start local Spark cluster using as many threads as there are cores if there is none:
-	<file>spark-submit target/scala-2.11/word_count_2.11-1.0.jar /net/projects/spark-example-data/wiki-cs outdir</file>	+	<file>spark-submit target/scala-2.12/word_count_2.12-1.0.jar /net/projects/spark-example-data/wiki-cs outdir</file>
	* run ''word_count'' application with local Spark cluster using one thread:		* run ''word_count'' application with local Spark cluster using one thread:
-	<file>MASTER=local spark-submit target/scala-2.11/word_count_2.11-1.0.jar /net/projects/spark-example-data/wiki-cs outdir</file>	+	<file>MASTER=local spark-submit target/scala-2.12/word_count_2.12-1.0.jar /net/projects/spark-example-data/wiki-cs outdir</file>
-	* start Spark cluster (10 machines, 1GB RAM each) on SGE and run ''word_count'' application:	+	* start Spark cluster (10 machines, 2GB RAM each) on SGE and run ''word_count'' application:
-	<file>spark-qsub 10 1G spark-submit target/scala-2.11/word_count_2.11-1.0.jar /net/projects/spark-example-data/wiki-cs outdir</file>	+	<file>spark-sbatch 10 2G spark-submit target/scala-2.12/word_count_2.12-1.0.jar /net/projects/spark-example-data/wiki-cs outdir</file>

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