Skip to main content

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.datasources.DataSourceAnalysis.apply(DataSourceStrategy.scala

Reason - With HDP Upgrade to 2.6.3 , we upgraded from Spark 2.2 to Spark 2.3 which resulted in exception above. This error can be seen for jobs where-in job is - 

  • reading and writing from same table or path
  • SCD Logic Jobs

Solution -

  • Set --conf "spark.sql.hive.convertMetastoreOrc=false"
  • or, update the job such that it writes data to a temporary table. Then reads from temporary table and insert it into final table.

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




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




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




AWS EMR Spark – Much Larger Executors are Created than Requested

By Dinesh
  Starting EMR 5.32 and EMR 6.2 you can notice that Spark can launch much larger executors that you request in your job settings. For example - We started a Spark Job with  spark.executor.cores  =   4 But, one can see that the executors with 20 cores (instead of 4 as defined by spark.executor.cores) were launched. The reason for allocating larger executors is that there is a AWS specific Spark option spark.yarn.heterogeneousExecutors.enabled (exists in EMR only, does not exist in Open Source Spark) that is set to true by default that combines multiple executor creation requests on the same node into a larger executor container. So as the result you have fewer executor containers than you expected, each of them has more memory and cores that you specified. If you disable this option (--conf "spark.yarn.heterogeneousExecutors.enabled=false"), EMR will create containers with the specified spark.executor.memory and spark.executor.cores settings and will not co...
Post a Comment
Read more