Spark is an excellent ETL tool to lift data from source and put it in target. Spark uses JDBC connection similar to Sqoop. I don't see the need for Sqoop with Spark here.
Where is the source (Oracle MSSQL, etc) and target (Hive?) here HTH Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* http://talebzadehmich.wordpress.com *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On Thu, 29 Aug 2019 at 21:01, Chetan Khatri <chetan.opensou...@gmail.com> wrote: > Hi Users, > I am launching a Sqoop job from Spark job and would like to FAIL Spark job > if Sqoop job fails. > > def executeSqoopOriginal(serverName: String, schemaName: String, username: > String, password: String, > query: String, splitBy: String, fetchSize: Int, numMappers: > Int, targetDir: String, jobName: String, dateColumns: String) = { > > val connectionString = "jdbc:sqlserver://" + serverName + ";" + > "databaseName=" + schemaName > var parameters = Array("import") > parameters = parameters :+ "-Dmapreduce.job.user.classpath.first=true" > parameters = parameters :+ "--connect" > parameters = parameters :+ connectionString > parameters = parameters :+ "--mapreduce-job-name" > parameters = parameters :+ jobName > parameters = parameters :+ "--username" > parameters = parameters :+ username > parameters = parameters :+ "--password" > parameters = parameters :+ password > parameters = parameters :+ "--hadoop-mapred-home" > parameters = parameters :+ "/usr/hdp/2.6.5.0-292/hadoop-mapreduce/" > parameters = parameters :+ "--hadoop-home" > parameters = parameters :+ "/usr/hdp/2.6.5.0-292/hadoop/" > parameters = parameters :+ "--query" > parameters = parameters :+ query > parameters = parameters :+ "--split-by" > parameters = parameters :+ splitBy > parameters = parameters :+ "--fetch-size" > parameters = parameters :+ fetchSize.toString > parameters = parameters :+ "--num-mappers" > parameters = parameters :+ numMappers.toString > if (dateColumns.length() > 0) { > parameters = parameters :+ "--map-column-java" > parameters = parameters :+ dateColumns > } > parameters = parameters :+ "--target-dir" > parameters = parameters :+ targetDir > parameters = parameters :+ "--delete-target-dir" > parameters = parameters :+ "--as-avrodatafile" > > } > >
