Python pandas_udf火花错误 [英] Python pandas_udf spark error

问题描述

我开始在本地玩火花，发现这个奇怪的问题

I started playing around with spark locally and finding this weird issue

    1) pip install pyspark==2.3.1
    2) pyspark>

    import pandas as pd
    from pyspark.sql.functions import pandas_udf, PandasUDFType, udf
    df = pd.DataFrame({'x': [1,2,3], 'y':[1.0,2.0,3.0]})
    sp_df = spark.createDataFrame(df)

    @pandas_udf('long', PandasUDFType.SCALAR)
    def pandas_plus_one(v):
        return v + 1

    sp_df.withColumn('v2', pandas_plus_one(sp_df.x)).show()

从此处获取此示例 https://databricks.com/blog/2017/10/30/introducing-vectorized-udfs-for-pyspark.html

知道为什么我会不断收到此错误吗?

Any idea why I keep getting this error?

py4j.protocol.Py4JJavaError: An error occurred while calling o108.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 3.0 failed 1 times, most recent failure: Lost task 0.0 in stage 3.0 (TID 8, localhost, executor driver): org.apache.spark.SparkException: Python worker exited unexpectedly (crashed)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator$$anonfun$1.applyOrElse(PythonRunner.scala:333)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator$$anonfun$1.applyOrElse(PythonRunner.scala:322)
    at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:36)
    at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:177)
    at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:121)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
    at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.(ArrowEvalPythonExec.scala:90)
    at org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:88)
    at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:131)
    at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:93)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:800)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:109)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Caused by: java.io.EOFException
    at java.io.DataInputStream.readInt(DataInputStream.java:392)
    at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:158)
    ... 27 more

推荐答案

我遇到了同样的问题.我发现这是熊猫和numpy之间的版本问题.

I had the same problem. I found it to be a version problem between pandas and numpy.

对我来说，以下作品:

numpy==1.14.5
pandas==0.23.4
pyarrow==0.10.0

在我使用以下无效组合之前:

before I had the following non working combination:

numpy==1.15.1
pandas==0.23.4
pyarrow==0.10.0

这篇关于Python pandas_udf火花错误的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！