apache spark and scala tutorial

The previous article "Apache Spark Learning: Deploying Spark to Hadoop 2.2.0" describes how to use MAVEN compilation to build spark jar packages that run directly on the Hadoop 2.2.0, and on this basis, Describes how to build an spark integrated development environment with

:[ERROR]/home/hadoop/.ivy2/cache/org.apache.spark/spark-streaming-kafka_2.10/jars/spark-streaming-kafka_ 2.10-1.3.0.jar:org/apache/Spark/unused/unusedstubclass.class[ERROR]/home/hadoop/.ivy2/cache/org.spark-project.spark/unused/jars/unused-1.0.0.jar:org/apache/

express our calculations by performing operations on distributed datasets that execute in parallel on the cluster. These distributed data sets are called elastic distributed datasets, or RDD. The RDD is the basic abstraction of spark for distributed data and computing.Before we tell more about the RDD, let's create an RDD in the shell based on a local text file and do some very simple point-to-point analysis on it, in Example 2-1 (Python) and Example

parsing:Sparkcontex is located in the project's source path \spark-master\core\src\main\scala\org\apache\spark\ Sparkcontext.scala, the source file contains the Sparkcontextclasss declaration and its associated object SparkcontextobjectClass Sparkcontext extends the logging. Logging is a trait, which is a container fo

[Big data from introductory to discard series tutorial] in Idea's Java project, configure and join Scala, write and run Scala's Hello WorldOriginal link: http://www.cnblogs.com/blog5277/p/8615984.htmlOriginal Author: Blog Park------Click on the menu below to view Big data Getting started all tutorialsBig data from getting started to giving upUrl:Http://www.cnblogs.com/blog5277/category/1179528.htmlSplit ***

crossvalidator is very high, however, compared with heuristic manual validation, cross-validation is still a very useful parameter selection method in existence. Scala: Import org.apache.spark.ml.Pipeline Import org.apache.spark.ml.classification.LogisticRegression Import Org.apache.spark.ml.evaluation.BinaryClassificationEvaluator import org.apache.spark.ml.feature. {HASHINGTF, tokenizer} import org.apache.spark.ml.linalg.Vector import org.apache.s

. Spark compilation is still very simple. Most of the causes of all failures can be attributed to the failure to download the dependent jar package. To enable spark 1.0 to support hadoop 2.4.0 and hive, use the following command to compile SPARK_HADOOP_VERSION=2.4.0 SPARK_YARN=true SPARK_HIVE=true sbt/sbt assembly If everything goes well, it will be generated under the Assembly directory.Spark-assembly-1.

= Records.newRecord(ContainerLaunchContext.class); amContainer.setCommands( Collections.singletonList( "$JAVA_HOME/bin/java" + " -Xmx256M" + " com.hortonworks.simpleyarnapp.ApplicationMaster" + " " + command + " " + String.valueOf(n) + " 1>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout" + " 2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr" ) ); However, the Class

Savetocassandra the stored procedure that triggered the data Another place worth documenting is that if the table created in Cassandra uses the UUID as primary key, use the following function in Scala to generate the UUIDimport java.util.UUIDUUID.randomUUIDVerification stepsUse Cqlsh to see if the data is actually written to the TEST.KV table.SummaryThis experiment combines the following knowledge S