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https://issues.apache.org/jira/browse/SPARK-35010?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17318347#comment-17318347
]
Yuming Wang commented on SPARK-35010:
-------------------------------------
Yes. It is an issue:
https://github.com/apache/spark/pull/31993
> nestedSchemaPruning causes issue when reading hive generated Orc files
> ----------------------------------------------------------------------
>
> Key: SPARK-35010
> URL: https://issues.apache.org/jira/browse/SPARK-35010
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.0.0, 3.1.0
> Reporter: Baohe Zhang
> Priority: Critical
>
> In spark3, we have spark.sql.orc.imple=native and
> spark.sql.optimizer.nestedSchemaPruning.enabled=true as the default settings.
> And these would cause issues when query struct field of hive-generated orc
> files.
> For example, we got an error when running this query in spark3
> {code:java}
> spark.table("testtable").filter(col("utc_date") ===
> "20210122").select(col("open_count.d35")).show(false)
> {code}
> The error is
> {code:java}
> Caused by: java.lang.AssertionError: assertion failed: The given data schema
> struct<open_count:struct<d35:map<string,double>>> has less fields than the
> actual ORC physical schema, no idea which columns were dropped, fail to read.
> at scala.Predef$.assert(Predef.scala:223)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcUtils$.requestedColumnIds(OrcUtils.scala:153)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFileFormat.$anonfun$buildReaderWithPartitionValues$3(OrcFileFormat.scala:180)
> at org.apache.spark.util.Utils$.tryWithResource(Utils.scala:2539)
> at
> org.apache.spark.sql.execution.datasources.orc.OrcFileFormat.$anonfun$buildReaderWithPartitionValues$1(OrcFileFormat.scala:178)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.org$apache$spark$sql$execution$datasources$FileScanRDD$$anon$$readCurrentFile(FileScanRDD.scala:116)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:169)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:93)
> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
> at
> org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:127)
> at
> org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:446)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> {code}
>
> I think the reason is that we apply the nestedSchemaPruning to the
> dataSchema.
> [https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/SchemaPruning.scala#L75]
> This nestedSchemaPruning not only prunes the unused fields of the struct, it
> also prunes the unused columns. In my test, the dataSchema originally has 48
> columns, but after nested schema pruning, the dataSchema is pruned to 1
> column. This pruning result in an assertion error in
> [https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala#L159]
> because column pruning in hive generated orc files is not supported.
> This issue seems also related to the hive version, we use hive 1.2, and it
> doesn't contain field names in the physical schema.
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