pyspark：重新分区后出现“太多值”错误答案

【问题标题】：pyspark: "too many values" error after repartitioningpyspark：重新分区后出现“太多值”错误
【发布时间】：2015-11-20 22:54:52
【问题描述】：

我有一个 DataFrame（被转换为 RDD）并且想要重新分区，以便每个键（第一列）都有自己的分区。这就是我所做的：

# Repartition to # key partitions and map each row to a partition given their key rank
my_rdd = df.rdd.partitionBy(len(keys), lambda row: int(row[0]))

但是，当我尝试将其映射回 DataFrame 或保存时，我收到此错误：

Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "spark-1.5.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/worker.py", line 111, in main
        process()
      File "spark-1.5.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/worker.py",     line 106, in process
serializer.dump_stream(func(split_index, iterator), outfile)
  File "spark-1.5.1-bin-hadoop2.6/python/lib/pyspark.zip/pyspark/serializers.py", line 133, in dump_stream
    for obj in iterator:
  File "spark-1.5.1-bin-hadoop2.6/python/pyspark/rdd.py", line 1703, in add_shuffle_key
    for k, v in iterator:
ValueError: too many values to unpack

        at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
        at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
        at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
        at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:70)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
        at org.apache.spark.api.python.PairwiseRDD.compute(PythonRDD.scala:342)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
        at org.apache.spark.scheduler.Task.run(Task.scala:88)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
           at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        ... 1 more

更多测试表明，即使这样也会导致相同的错误： my_rdd = df.rdd.partitionBy(x) # x = 可以是 5、100 等

你们有没有遇到过这种情况。如果有，你是怎么解决的？

【问题讨论】：

标签： python apache-spark apache-spark-sql pyspark rdd

【解决方案1】：

partitionBy 需要一个PairwiseRDD，它在 Python 中相当于长度为 2 的元组（列表）的RDD，其中第一个元素是键，第二个元素是值。

partitionFunc 获取密钥并将其映射到分区号。当您在 RDD[Row] 上使用它时，它会尝试将行解压缩为键值并失败：

from pyspark.sql import Row

row = Row(1, 2, 3)
k, v = row

## Traceback (most recent call last):
##   ...
## ValueError: too many values to unpack (expected 2)

即使你提供了正确的数据做这样的事情：

my_rdd = (df.rdd.map(lambda row: (int(row[0]), row)).partitionBy(len(keys))

这真的没有意义。对于DataFrames，分区并不是特别有意义。有关详细信息，请参阅 my answer 至 How to define partitioning of DataFrame?。

【讨论】：