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https://issues.apache.org/jira/browse/HUDI-3019?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Raymond Xu updated HUDI-3019:
-----------------------------
Component/s: writer-core
> Upserts with Dataype promotion only to a subset of partition fails
> ------------------------------------------------------------------
>
> Key: HUDI-3019
> URL: https://issues.apache.org/jira/browse/HUDI-3019
> Project: Apache Hudi
> Issue Type: Task
> Components: writer-core
> Affects Versions: 0.10.0
> Reporter: sivabalan narayanan
> Assignee: Sagar Sumit
> Priority: Critical
> Fix For: 0.11.0
>
>
> Upserts with Dataype promotion only to a subset of partition fails.
>
> Lets say intial insert was done to partition1 and partition2. with col1 type
> as integer.
> commit2 inserted records to partition2 and partition3, with col1 type as
> long. integer -> long is backwards compatible evolution and hence write
> succeeds. but when trying to read data from hudi, we run into issues. This is
> not seen when a new column is added.
>
> Reference issue:
> [https://github.com/apache/hudi/issues/3558]
>
> {code:java}
> spark.sql("select * from hudi_trips_snapshot2").show()
> 21/12/14 12:11:48 ERROR Executor: Exception in task 0.0 in stage 165.0 (TID
> 1620)
> java.lang.UnsupportedOperationException:
> org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary
> at org.apache.parquet.column.Dictionary.decodeToLong(Dictionary.java:49)
> at
> org.apache.spark.sql.execution.datasources.parquet.ParquetDictionary.decodeToLong(ParquetDictionary.java:36)
> at
> org.apache.spark.sql.execution.vectorized.OnHeapColumnVector.getLong(OnHeapColumnVector.java:364)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:858)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:858)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:123)
> at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748) {code}
>
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