Pyspark GCP UnsupportedOperationException：org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainDoubleDictionary

Question

我是 pyspark 的新手，所以希望有人能提供帮助。我正在尝试读取存储在 GCP 存储桶上的镶木地板文件。文件按日期分区，例如

bucket-name/year={}/month={}/day={}

对于给定的文件，我们有以下模式描述：

1. 直到 3 月份，我们的 x 列和 y 列都采用 float 数据类型
2. 自 3 月份以来，这两列现在采用 double 数据类型

据我所知，pyspark 在评估 float 和 double 数据类型是兼容的数据类型时没有问题。 （我在网上找到的类似错误示例与数据类型不兼容有关，例如字符串和浮点数） 然而，我们面临着这个奇怪的问题，如果我们尝试读入该文件的所有可用数据：

#i.e. read all the data we have ever received for this file
 path = 'bucket-name/year=*/month=*/day=*' 

df = spark.read.format('parquet').load(path)
df.cache().count()

我们收到以下错误。 （请注意，如果我们这样做

df.count()

，我们不会收到此错误，只有在我们先缓存时才会遇到）

此外，spark.read 生成的模式提到列 x 的数据类型为 float。因此，从模式角度来看，spark 很乐意读取数据并表示数据类型是 float。然而，如果我们缓存，事情就会变得糟糕。

希望情况细节足够清楚:)

An error occurred while calling o923.count. :
org.apache.spark.SparkException: Job aborted due to stage failure:
Task 15 in stage 41.0 failed 4 times, most recent failure: Lost task
15.3 in stage 41.0 (TID 13228, avroconversion-validation-w-1.c.vf-gned-nwp-live.internal, executor
47): java.lang.UnsupportedOperationException:
org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainDoubleDictionary
    at
org.apache.parquet.column.Dictionary.decodeToFloat(Dictionary.java:53)
    at
org.apache.spark.sql.execution.datasources.parquet.ParquetDictionary.decodeToFloat(ParquetDictionary.java:41)
    at
org.apache.spark.sql.execution.vectorized.OnHeapColumnVector.getFloat(OnHeapColumnVector.java:423)
    at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
Source)     at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$2.hasNext(WholeStageCodegenExec.scala:636)
    at
org.apache.spark.sql.execution.columnar.CachedRDDBuilder$$anon$1.hasNext(InMemoryRelation.scala:125)
    at
org.apache.spark.storage.memory.MemoryStore.putIterator(MemoryStore.scala:221)
    at
org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:299)
    at
org.apache.spark.storage.BlockManager.$anonfun$doPutIterator$1(BlockManager.scala:1165)
    at
org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1091)
    at
org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1156)
    at
org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:882)
    at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:357)     at
org.apache.spark.rdd.RDD.iterator(RDD.scala:308)    at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)     at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)     at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.$anonfun$getOrCompute$1(RDD.scala:359)
    at
org.apache.spark.storage.BlockManager.$anonfun$doPutIterator$1(BlockManager.scala:1165)
    at
org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1091)
    at
org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1156)
    at
org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:882)
    at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:357)     at
org.apache.spark.rdd.RDD.iterator(RDD.scala:308)    at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)     at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)     at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)     at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:310)     at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
    at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
    at org.apache.spark.scheduler.Task.run(Task.scala:123)  at
org.apache.spark.executor.Executor$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)

Answer 1

根据文档：

cache()方法是使用默认存储级别的简写， 这是 StorageLevel.MEMORY_ONLY （将反序列化的对象存储在 记忆）

cache()

是一个惰性操作，如果您查看

MEMORY_ONLY

部分，您会注意到

cache()

尝试将 RDD/DataFrame 作为反序列化的 Java 对象存储在 JVM 中[一旦您对缓存的 RDD/DataFrame 调用操作]，因此您可以RDD/DataFrame 中对象的反序列化存在问题。 我建议尝试执行一些转换，例如

map()

来检查序列化/反序列化是否正常工作

如果您在

df.count()

中调用

df

而没有进行任何转换，则 Spark 不会反序列化您的对象