Spark: error reading DateType columns in partitioned parquet data
22,433
I just used StringType instead of DateType when writing parquet. Don't have the issue anymore.
Author by
Kamil Sindi
Updated on May 26, 2021Comments
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Kamil Sindi almost 3 years
I have parquet data in S3 partitioned by nyc_date in the format
s3://mybucket/mykey/nyc_date=Y-m-d/*.gz.parquet
.I have a DateType column
event_date
that for some reason throws this error when I try to read from S3 and write to hdfs using EMR.from pyspark.sql import SparkSession spark = SparkSession.builder.enableHiveSupport().getOrCreate() df = spark.read.parquet('s3a://mybucket/mykey/') df.limit(100).write.parquet('hdfs:///output/', compression='gzip')
Error:
java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainBinaryDictionary at org.apache.parquet.column.Dictionary.decodeToInt(Dictionary.java:48) at org.apache.spark.sql.execution.vectorized.OnHeapColumnVector.getInt(OnHeapColumnVector.java:233) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370) at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:389) at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408) at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47) at org.apache.spark.scheduler.Task.run(Task.scala:86) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745)
Here's what I figured out:
- Local works :-): I copied over some data locally in the same format and can query fine.
- Avoid selecting event_date works :-): Selecting all 50+ columns but for
event_date
doesn't cause any errors. - Explicit read path throws error :-(: Changing the read path to
's3a://mybucket/mykey/*/*.gz.parquet'
still throws error. - Specifying schema still throws error :-(: specifying the schema before loading still causes the same error.
- I can load the data including eastern_date into a data warehouse :-).
Really weird this causes an error only for a DateType column. I don't have any other DateType columns.
Using Spark 2.0.2 and EMR 5.2.0.
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Eric Bellet about 2 yearsI can't rewrite the files because I'm not the owner. I can just read the table with Spark SQL... Can I solve the problem with some config or creating the table on top of the parquets?