Spark:读取分区镶木地板数据中的DateType列时出错

Question

我在S3中通过nyc_date以格式分区的镶木地板数据s3://mybucket/mykey/nyc_date=Y-m-d/*.gz.parquet.

我有一个DateType列event_date,当我尝试从S3读取并使用EMR写入hdfs时,由于某种原因抛出此错误.

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')

错误:

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)

这是我想出的:

• 本地作品:-):我以相同的格式在本地复制了一些数据,可以查询.
• 避免选择event_date工作:-):选择所有50+列,但event_date不会导致任何错误.
• 显式读取路径抛出错误:-(:更改读取路径's3a://mybucket/mykey/*/*.gz.parquet'仍然会引发错误.
• 指定模式仍然会引发错误:-(:在加载之前指定模式仍会导致相同的错误.
• 我可以将包括eastern_date在内的数据加载到数据仓库中:-).

真的很奇怪,这只会导致DateType列的错误.我没有任何其他DateType列.

使用Spark 2.0.2和EMR 5.2.0.

Answer 1

我在编写 parquet 时只是使用 StringType 而不是 DateType。不再有这个问题了