pandas UDF和pyarrow 0.15.0 [英] pandasUDF and pyarrow 0.15.0

问题描述

我最近开始在EMR群集上运行的许多pyspark作业中遇到一堆错误.错误是

I have recently started getting a bunch of errors on a number of pyspark jobs running on EMR clusters. The erros are

java.lang.IllegalArgumentException
    at java.nio.ByteBuffer.allocate(ByteBuffer.java:334)
    at org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543)
    at org.apache.arrow.vector.ipc.message.MessageChannelReader.readNext(MessageChannelReader.java:58)
    at org.apache.arrow.vector.ipc.ArrowStreamReader.readSchema(ArrowStreamReader.java:132)
    at org.apache.arrow.vector.ipc.ArrowReader.initialize(ArrowReader.java:181)
    at org.apache.arrow.vector.ipc.ArrowReader.ensureInitialized(ArrowReader.java:172)
    at org.apache.arrow.vector.ipc.ArrowReader.getVectorSchemaRoot(ArrowReader.java:65)
    at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:162)
    at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
    at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:98)
    at org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:96)
    at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127)...

它们似乎都发生在熊猫系列的apply功能中.我发现的唯一变化是pyarrow已在星期六(05/10/2019)更新.测试似乎适用于0.14.1

They all seem to happen in apply functions of a pandas series. The only change I found is that pyarrow has been updated on Saturday (05/10/2019). Tests seem to work with 0.14.1

所以我的问题是，是否有人知道这是新更新的pyarrow中的错误，还是有一些重大更改会导致将来无法使用pandasUDF?

So my question is if anyone know if this is a bug in the new updated pyarrow or is there some significant change that will make pandasUDF hard to use in the future?

推荐答案

这不是错误.我们在0.15.0中进行了重要的协议更改，使pyarrow的默认行为与Java中的较旧版本的Arrow不兼容-您的Spark环境似乎正在使用较旧的版本.

It's not a bug. We made an important protocol change in 0.15.0 that makes the default behavior of pyarrow incompatible with older versions of Arrow in Java -- your Spark environment seems to be using an older version.

您的选择是

• 在使用Python的位置设置环境变量ARROW_PRE_0_15_IPC_FORMAT=1
• 降级为pyarrow<现在为0.15.0.

希望Spark社区很快就能在Java中升级到0.15.0，所以这个问题就消失了.

Hopefully the Spark community will be able to upgrade to 0.15.0 in Java soon so this issue goes away.

这在 http://arrow.apache中进行了讨论.org/blog/2019/10/06/0.15.0-release/

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