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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/06/10 16:06:19 UTC
[jira] [Commented] (SPARK-21045) Spark executor blocked instead of
throwing exception because exception occur when python worker send
exception info to Java Gateway
[ https://issues.apache.org/jira/browse/SPARK-21045?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16045592#comment-16045592 ]
Apache Spark commented on SPARK-21045:
--------------------------------------
User 'dataknocker' has created a pull request for this issue:
https://github.com/apache/spark/pull/18261
> Spark executor blocked instead of throwing exception because exception occur when python worker send exception info to Java Gateway
> -----------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-21045
> URL: https://issues.apache.org/jira/browse/SPARK-21045
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.0.1, 2.0.2, 2.1.1
> Reporter: Joshuawangzj
>
> My pyspark program is always blocking in product yarn cluster. Then I jstack and found :
> {code}
> "Executor task launch worker for task 0" #60 daemon prio=5 os_prio=31 tid=0x00007fb2f44e3000 nid=0xa003 runnable [0x0000000123b4a000]
> java.lang.Thread.State: RUNNABLE
> at java.net.SocketInputStream.socketRead0(Native Method)
> at java.net.SocketInputStream.socketRead(SocketInputStream.java:116)
> at java.net.SocketInputStream.read(SocketInputStream.java:170)
> at java.net.SocketInputStream.read(SocketInputStream.java:141)
> at java.io.BufferedInputStream.fill(BufferedInputStream.java:246)
> at java.io.BufferedInputStream.read(BufferedInputStream.java:265)
> - locked <0x00000007acab1c98> (a java.io.BufferedInputStream)
> at java.io.DataInputStream.readInt(DataInputStream.java:387)
> at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:190)
> at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
> at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
> 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)
> {code}
> It is blocking in socket read. I view the log on blocking executor and found error:
> {code}
> Traceback (most recent call last):
> File "/opt/spark/python/lib/pyspark.zip/pyspark/worker.py", line 178, in main
> write_with_length(traceback.format_exc().encode("utf-8"), outfile)
> UnicodeDecodeError: 'ascii' codec can't decode byte 0xe4 in position 618: ordinal not in range(128)
> {code}
> Finally I found the problem:
> {code:title=worker.py|borderStyle=solid}
> # 178 line in spark 2.1.1
> except Exception:
> try:
> write_int(SpecialLengths.PYTHON_EXCEPTION_THROWN, outfile)
> write_with_length(traceback.format_exc().encode("utf-8"), outfile)
> except IOError:
> # JVM close the socket
> pass
> except Exception:
> # Write the error to stderr if it happened while serializing
> print("PySpark worker failed with exception:", file=sys.stderr)
> print(traceback.format_exc(), file=sys.stderr)
> {code}
> when write_with_length(traceback.format_exc().encode("utf-8"), outfile) occur exception like UnicodeDecodeError, the python worker can't send the trace info, but when the PythonRDD get PYTHON_EXCEPTION_THROWN, It should read the trace info length next. So it is blocking.
> {code:title=PythonRDD.scala|borderStyle=solid}
> # 190 line in spark 2.1.1
> case SpecialLengths.PYTHON_EXCEPTION_THROWN =>
> // Signals that an exception has been thrown in python
> val exLength = stream.readInt() // It is possible to be blocked
> {code}
> {color:red}
> We can triggle the bug use simple program:
> {color}
> {code:title=test.py|borderStyle=solid}
> spark = SparkSession.builder.master('local').getOrCreate()
> rdd = spark.sparkContext.parallelize(['δΈ']).map(lambda x: x.encode("utf8"))
> rdd.collect()
> {code}
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