[BUG]: Hive incompatibility when using microsoft-spark-3-1_2.12-2.1.1.jar
dcosta91 opened this issue · comments
Describe the bug
We are trying to upgrade some 2.4 spark jobs to 3.1 and everything goes well until it tries to write into a partition in Hive.
It complains it cannot find the get_partition_names_ps_req method.
Just as a note, if I use microsoft-spark-2-4_2.11-2.1.1.jar, it works. So it must be some incompatibility between microsoft-spark-3-1_2.12-2.1.1.jar and the cluster I'm using.
Some questions:
- Where can I find the source code for the microsoft-spark-3-1_2.12-2.1.1.jar?
- Which version of hive is this jar using?
- Does anybody has ideas on how to put this working?
Utilized versions:
- microsoft-spark-3-1_2.12-2.1.1.jar
- Cluster versions:
- CDH-7.1.7
- Spark: 3.1.1
- Hive: 3.1.3
Error message:
[2023-03-03T09:18:44.7972462Z] [ldchio1091] [Error] [JvmBridge] org.apache.spark.sql.AnalysisException: org.apache.thrift.TApplicationException: Invalid method name: 'get_partition_names_ps_req' at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$loadDynamicPartitions$1(HiveClientImpl.scala:969) at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23) at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$withHiveState$1(HiveClientImpl.scala:316) at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:245) at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:244) at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:298) at org.apache.spark.sql.hive.client.HiveClientImpl.loadDynamicPartitions(HiveClientImpl.scala:956) at org.apache.spark.sql.hive.HiveExternalCatalog.$anonfun$loadDynamicPartitions$1(HiveExternalCatalog.scala:945) at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23) at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:102) at org.apache.spark.sql.hive.HiveExternalCatalog.loadDynamicPartitions(HiveExternalCatalog.scala:925) at org.apache.spark.sql.catalyst.catalog.ExternalCatalogWithListener.loadDynamicPartitions(ExternalCatalogWithListener.scala:189) at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.processInsert(InsertIntoHiveTable.scala:221) at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.run(InsertIntoHiveTable.scala:103) at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:108) at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:106) at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:131) at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:180) at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:218) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:215) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:176) at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:133) at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:132) at org.apache.spark.sql.DataFrameWriter.$anonfun$runCommand$1(DataFrameWriter.scala:997) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:772) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64) at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:997) at org.apache.spark.sql.DataFrameWriter.insertInto(DataFrameWriter.scala:542) at org.apache.spark.sql.DataFrameWriter.insertInto(DataFrameWriter.scala:496) at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.base/java.lang.reflect.Method.invoke(Method.java:566) at org.apache.spark.api.dotnet.DotnetBackendHandler.handleMethodCall(DotnetBackendHandler.scala:165) at org.apache.spark.api.dotnet.DotnetBackendHandler.$anonfun$handleBackendRequest$2(DotnetBackendHandler.scala:105) at org.apache.spark.api.dotnet.ThreadPool$$anon$1.run(ThreadPool.scala:34) at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515) at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) at java.base/java.lang.Thread.run(Thread.java:829)
I found out now that the method get_partition_names_ps_req does not exist in Hive 3.1.3 and was only added on Hive 4.0.0 (which is not released yet...)
Therefore my question is, why is Microsoft Worker not compatible with Hive 3.1.3?
If I run Java/Scala on the same spark version, it works with hive. What am I missing here?