Naitree Zhu created SPARK-31345:
-----------------------------------
Summary: Spark fails to write hive parquet table with empty array
Key: SPARK-31345
URL: https://issues.apache.org/jira/browse/SPARK-31345
Project: Spark
Issue Type: Bug
Components: Spark Core
Affects Versions: 2.4.5
Environment: - Spark 2.4.5 (compiled against hadoop 2.10.0, and
bundled with hadoop 2.10.0 dependencies in `spark.yarn.archive`)
- Hive 3.1.2
- Hadoop 3.2.1
Reporter: Naitree Zhu
When writing an existing Hive Parquet table using Spark SQL, I encountered an
error when writing empty `array()` or `map()`.
Test case to reproduce:
```
spark.sql("create table test_null (col1 array<int>) stored as parquet")
val df = spark.sql("select cast(array() as array<int>) as col1")
df.write.format("hive").mode(SaveMode.Append).saveAsTable("default.test_null")
```
Exception raised:
```
20/04/04 09:16:03 WARN TaskSetManager: Lost task 0.0 in stage 16.0 (TID 30,
test-node, executor 2): org.apache.spark.SparkException: Task failed while
writing rows.
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeTask(FileFormatWriter.scala:257)
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$write$15(FileFormatWriter.scala:177)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at
org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:411)
at org.apache.spark.util.Utils$.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)
Caused by: java.lang.RuntimeException: Parquet record is malformed: empty
fields are illegal, the field should be ommited completely instead
at
org.apache.hadoop.hive.ql.io.parquet.write.DataWritableWriter.write(DataWritableWriter.java:64)
at
org.apache.hadoop.hive.ql.io.parquet.write.DataWritableWriteSupport.write(DataWritableWriteSupport.java:59)
at
org.apache.hadoop.hive.ql.io.parquet.write.DataWritableWriteSupport.write(DataWritableWriteSupport.java:31)
at
parquet.hadoop.InternalParquetRecordWriter.write(InternalParquetRecordWriter.java:121)
at
parquet.hadoop.ParquetRecordWriter.write(ParquetRecordWriter.java:123)
at parquet.hadoop.ParquetRecordWriter.write(ParquetRecordWriter.java:42)
at
org.apache.hadoop.hive.ql.io.parquet.write.ParquetRecordWriterWrapper.write(ParquetRecordWriterWrapper.java:111)
at
org.apache.hadoop.hive.ql.io.parquet.write.ParquetRecordWriterWrapper.write(ParquetRecordWriterWrapper.java:124)
at
org.apache.spark.sql.hive.execution.HiveOutputWriter.write(HiveFileFormat.scala:149)
at
org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.write(FileFormatDataWriter.scala:137)
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$executeTask$1(FileFormatWriter.scala:245)
at
org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394)
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeTask(FileFormatWriter.scala:242)
... 9 more
Caused by: parquet.io.ParquetEncodingException: empty fields are illegal, the
field should be ommited completely instead
at
parquet.io.MessageColumnIO$MessageColumnIORecordConsumer.endField(MessageColumnIO.java:244)
at
org.apache.hadoop.hive.ql.io.parquet.write.DataWritableWriter.writeArray(DataWritableWriter.java:186)
at
org.apache.hadoop.hive.ql.io.parquet.write.DataWritableWriter.writeValue(DataWritableWriter.java:113)
at
org.apache.hadoop.hive.ql.io.parquet.write.DataWritableWriter.writeGroupFields(DataWritableWriter.java:89)
at
org.apache.hadoop.hive.ql.io.parquet.write.DataWritableWriter.write(DataWritableWriter.java:60)
... 21 more
```
However, letting spark to create the table implicitly, it would succeed.
```
spark.sql("drop table default.test_null")
df.write.format("parquet").mode(SaveMode.Append).saveAsTable("default.test_null")
```
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