Skip to main content

Oozie hangs on a single node for work flow with fork-Join

By Dinesh
For each action a job is launched by oozie:launcher which in turn executes your actual action job. For example you have a flow like :-

It actually going to launch 4 MR's as below:-

So, at this point of time four Map slots are required (two for MR launchers, two for actual jobs). But, default only two Map slots are available in each node. That is, only two Map tasks will run at any point of time in a node. This is specified by the `mapred.tasktracker.map.tasks.maximum` property which defaults to 2 in the mapred-site.xml file.

Available slots are occupied by 2 launchers and So, the 2 Jobs wait for available slots.

This happens in pseudo-distributed mode. So may be one can run it over cluster or change "mapred.tasktracker.map.tasks.maximum" property in mapred-site.xml.

Note that setting property in pseudo -distribute may not even work. So, one might has to bump Memory and Cores too.

Comments

Post a Comment

Popular posts

Hive Parse JSON with Array Columns and Explode it in to Multiple rows.

By Dinesh
 Say we have a JSON String like below -  { "billingCountry":"US" "orderItems":[       {          "itemId":1,          "product":"D1"       },   {          "itemId":2,          "product":"D2"       }    ] } And, our aim is to get output parsed like below -  itemId product 1 D1 2 D2   First, We can parse JSON as follows to get JSON String get_json_object(value, '$.orderItems.itemId') as itemId get_json_object(value, '$.orderItems.product') as product Second, Above will result String value like "[1,2]". We want to convert it to Array as follows - split(regexp_extract(get_json_object(value, '$.orderItems.itemId'),'^\\["(.*)\\"]$',1),'","') as itemId split(regexp_extract(get_json_object(value, '$.orderItems.product'),'^\\["(.*)\\"]$',1),...
Post a Comment
Read more




org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.;

By Dinesh
  Caused by: org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.; at org.apache.spark.sql.execution.command.DDLUtils$.verifyNotReadPath(ddl.scala:906) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:192) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:134) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis.apply(DataSourceStrategy.scala:134) at org.apache.spark.sql.execution.dataso...
Post a Comment
Read more




Read from a hive table and write back to it using spark sql

By Dinesh
In context to Spark 2.2 - if we read from an hive table and write to same, we get following exception- scala > dy . write . mode ( "overwrite" ). insertInto ( "incremental.test2" ) org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; 1. This error means that our process is reading from same table and writing to same table. 2. Normally, this should work as process writes to directory .hiveStaging... 3. This error occurs in case of saveAsTable method, as it overwrites entire table instead of individual partitions. 4. This error should not occur with insertInto method, as it overwrites partitions not the table. 5. A reason why this happening is because Hive table has following Spark TBLProperties in its definition. This problem will solve for insertInto met...
Post a Comment
Read more




Hadoop Distcp Error Duplicate files in input path

By Dinesh
  One may face following error while copying data from one cluster to other, using Distcp  Command: hadoop distcp -i {src} {tgt} Error: org.apache.hadoop.toolsCopyListing$DulicateFileException: File would cause duplicates. Ideally there can't be same file names. So, what might be happening in your case is you trying to copy partitioned table from one cluster to other. And, 2 different named partitions have same file name. Your solution is to correct Source path  {src}  in your command, such that you provide path uptil partitioned sub directory, not the file. For ex - Refer below : /a/partcol=1/file1.txt /a/partcol=2/file1.txt If you use  {src}  as  "/a/*/*"  then you will get the error  "File would cause duplicates." But, if you use  {src}  as  "/a"  then you will not get error in copying.
Post a Comment
Read more




Scala Spark building Jar leads java.lang.StackOverflowError

By Dinesh
  Exception -  [Thread-3] ERROR scala_maven.ScalaCompileMojo - error: java.lang.StackOverflowError [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.generic.TraversableForwarder$class.isEmpty(TraversableForwarder.scala:36) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.isEmpty(ListBuffer.scala:45) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.toList(ListBuffer.scala:306) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.ListBuffer.result(ListBuffer.scala:300) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.Stack$StackBuilder.result(Stack.scala:31) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.mutable.Stack$StackBuilder.result(Stack.scala:27) [Thread-3] INFO scala_maven.ScalaCompileMojo - at scala.collection.generic.GenericCompanion.apply(GenericCompanion.scala:50) [Thread-3] INFO scala_maven.ScalaCompile...
Post a Comment
Read more