This I would not say. The only “issue” with Spark is that you need to build some functionality on top which is available in Sqoop out of the box, especially for import processes and if you need to define a lot of them.
> Am 03.09.2019 um 09:30 schrieb Shyam P <shyamabigd...@gmail.com>: > > Hi Mich, > Lot of people say that Spark does not have proven record in migrating data > from oracle as sqoop has. > At list in production. > > Please correct me if I am wrong and suggest how to deal with shuffling when > dealing with groupBy ? > > Thanks, > Shyam > >> On Sat, Aug 31, 2019 at 12:17 PM Mich Talebzadeh <mich.talebza...@gmail.com> >> wrote: >> 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 >> >> 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" >>> >>> }
