Match needs to be lower case “match”
From: Mich Talebzadeh <mich.talebza...@gmail.com> Date: Tuesday, May 5, 2020 at 6:13 PM To: Brandon Geise <brandonge...@gmail.com> Cc: Todd Nist <tsind...@gmail.com>, "user @spark" <user@spark.apache.org> Subject: Re: Exception handling in Spark scala> import scala.util.{Try, Success, Failure} import scala.util.{Try, Success, Failure} scala> val df = Try(spark.read.format("com.databricks.spark.xml").option("rootTag", "hierarchy").option("rowTag", "sms_request").load("/tmp/broadcast.xml")) Match {case Success(df) => df case Failure(e) => throw new Exception("foo")} <console>:48: error: value Match is not a member of scala.util.Try[org.apache.spark.sql.DataFrame] val df = Try(spark.read.format("com.databricks.spark.xml").option("rootTag", "hierarchy").option("rowTag", "sms_request").load("/tmp/broadcast.xml")) Match {case Success(df) => df case Failure(e) => throw new Exception("foo")} 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 Tue, 5 May 2020 at 23:10, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: This is what I get scala> val df = Try(spark.read.format("com.databricks.spark.xml").option("rootTag", "hierarchy").option("rowTag", "sms_request").load("/tmp/broadcast.xml")) Match {case Success(df) => df case Failure(e) => throw new Exception("foo")} <console>:47: error: not found: value Try val df = Try(spark.read.format("com.databricks.spark.xml").option("rootTag", "hierarchy").option("rowTag", "sms_request").load("/tmp/broadcast.xml")) Match {case Success(df) => df case Failure(e) => throw new Exception("foo")} ^ <console>:47: error: not found: value Success val df = Try(spark.read.format("com.databricks.spark.xml").option("rootTag", "hierarchy").option("rowTag", "sms_request").load("/tmp/broadcast.xml")) Match {case Success(df) => df case Failure(e) => throw new Exception("foo")} ^ <console>:47: error: not found: value Failure val df = Try(spark.read.format("com.databricks.spark.xml").option("rootTag", "hierarchy").option("rowTag", "sms_request").load("/tmp/broadcast.xml")) Match {case Success(df) => df case Failure(e) => throw new Exception("foo")} 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 Tue, 5 May 2020 at 23:03, Brandon Geise <brandonge...@gmail.com> wrote: This is what I had in mind. Can you give this approach a try? val df = Try(spark.read.csv("")) match { case Success(df) => df case Failure(e) => throw new Exception("foo") } From: Mich Talebzadeh <mich.talebza...@gmail.com> Date: Tuesday, May 5, 2020 at 5:17 PM To: Todd Nist <tsind...@gmail.com> Cc: Brandon Geise <brandonge...@gmail.com>, "user @spark" <user@spark.apache.org> Subject: Re: Exception handling in Spark I am trying this approach val broadcastValue = "123456789" // I assume this will be sent as a constant for the batch // Create a DF on top of XML try { val df = spark.read. format("com.databricks.spark.xml"). option("rootTag", "hierarchy"). option("rowTag", "sms_request"). load("/tmp/broadcast.xml") df } catch { case ex: FileNotFoundException => { println (s"\nFile /tmp/broadcast.xml not found\n") None } case unknown: Exception => { println(s"\n Error encountered $unknown\n") None } } val newDF = df.withColumn("broadcastid", lit(broadcastValue)) But this does not work scala> try { | val df = spark.read. | format("com.databricks.spark.xml"). | option("rootTag", "hierarchy"). | option("rowTag", "sms_request"). | load("/tmp/broadcast.xml") | Some(df) | } catch { | case ex: FileNotFoundException => { | println (s"\nFile /tmp/broadcast.xml not found\n") | None | } | case unknown: Exception => { | println(s"\n Error encountered $unknown\n") | None | } | } res6: Option[org.apache.spark.sql.DataFrame] = Some([brand: string, ocis_party_id: bigint ... 6 more fields]) scala> scala> df.printSchema <console>:48: error: not found: value df df.printSchema data frame seems to be lost! Thanks, 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 Tue, 5 May 2020 at 18:08, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: Thanks Todd. This is what I did before creating DF on top of that file var exists = true exists = xmlDirExists(broadcastStagingConfig.xmlFilePath) if(!exists) { println(s"\n Error: The xml file ${ broadcastStagingConfig.xmlFilePath} does not exist, aborting!\n") sys.exit(1) } . . def xmlFileExists(hdfsDirectory: String): Boolean = { val hadoopConf = new org.apache.hadoop.conf.Configuration() val fs = org.apache.hadoop.fs.FileSystem.get(hadoopConf) fs.exists(new org.apache.hadoop.fs.Path(hdfsDirectory)) } And checked it. It works. 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 Tue, 5 May 2020 at 17:54, Todd Nist <tsind...@gmail.com> wrote: Could you do something like this prior to calling the action. // Create FileSystem object from Hadoop Configuration val fs = FileSystem.get(spark.sparkContext.hadoopConfiguration) // This methods returns Boolean (true - if file exists, false - if file doesn't exist val fileExists = fs.exists(new Path("<parh_to_file>")) if (fileExists) println("File exists!") else println("File doesn't exist!") Not sure that will help you or not, just a thought. -Todd On Tue, May 5, 2020 at 11:45 AM Mich Talebzadeh <mich.talebza...@gmail.com> wrote: Thanks Brandon! i should have remembered that. basically the code gets out with sys.exit(1) if it cannot find the file I guess there is no easy way of validating DF except actioning it by show(1,0) etc and checking if it works? Regards, 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 Tue, 5 May 2020 at 16:41, Brandon Geise <brandonge...@gmail.com> wrote: You could use the Hadoop API and check if the file exists. From: Mich Talebzadeh <mich.talebza...@gmail.com> Date: Tuesday, May 5, 2020 at 11:25 AM To: "user @spark" <user@spark.apache.org> Subject: Exception handling in Spark Hi, As I understand exception handling in Spark only makes sense if one attempts an action as opposed to lazy transformations? Let us assume that I am reading an XML file from the HDFS directory and create a dataframe DF on it val broadcastValue = "123456789" // I assume this will be sent as a constant for the batch // Create a DF on top of XML val df = spark.read. format("com.databricks.spark.xml"). option("rootTag", "hierarchy"). option("rowTag", "sms_request"). load("/tmp/broadcast.xml") val newDF = df.withColumn("broadcastid", lit(broadcastValue)) newDF.createOrReplaceTempView("tmp") // Put data in Hive table // sqltext = """ INSERT INTO TABLE michtest.BroadcastStaging PARTITION (broadcastid="123456", brand) SELECT ocis_party_id AS partyId , target_mobile_no AS phoneNumber , brand , broadcastid FROM tmp """ // // Here I am performing a collection try { spark.sql(sqltext) } catch { case e: SQLException => e.printStackTrace sys.exit() } Now the issue I have is that what if the xml file /tmp/broadcast.xml does not exist or deleted? I won't be able to catch the error until the hive table is populated. Of course I can write a shell script to check if the file exist before running the job or put small collection like df.show(1,0). Are there more general alternatives? Thanks 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.