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https://issues.apache.org/jira/browse/SPARK-28482?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16913611#comment-16913611
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Bryan Cutler commented on SPARK-28482:
--------------------------------------
I'm not really sure what you are doing above, are you saying the row count is
not correct using Spark unmodified? I used 120,000 rows and it is correct for
me:
{code}
In [1]: from pyspark.sql.types import *
...: from pyspark.sql.functions import *
...: df=spark.read.format('csv').option("header","true").load('test.csv')
...: df=df.select(*(col(c).cast("int").alias(c) for c in df.columns))
...: df=df.repartition(1)
...: def add_func(a,b,c,d,e,f,g):
...: print('iterator one time')
...: return a
...: add = pandas_udf(add_func, returnType=IntegerType())
...:
df_result=df.select(add(col("a"),col("b"),col("c"),col("d"),col("e"),col("f"),col("g")))
In [2]: r = df_result.toPandas()
[Stage 2:> (0 + 1) /
1]iterator one time
iterator one time
iterator one time
iterator one time
iterator one time
iterator one time
iterator one time
iterator one time
iterator one time
iterator one time
iterator one time
iterator one time
In [3]: len(r)
Out[3]: 120000
{code}
> Data incomplete when using pandas udf in Python 3
> -------------------------------------------------
>
> Key: SPARK-28482
> URL: https://issues.apache.org/jira/browse/SPARK-28482
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.3.3, 2.4.3
> Environment: centos 7.4
> pyarrow 0.10.0 0.14.0
> python 2.7 3.5 3.6
> Reporter: jiangyu
> Priority: Major
> Attachments: py2.7.png, py3.6.png, test.csv, test.py, worker.png
>
>
> Hi,
>
> Since Spark 2.3.x, pandas udf has been introduced as default ser/des method
> when using udf. However, an issue raises with python >= 3.5.x version.
> We use pandas udf to process batches of data, but we find the data is
> incomplete in python 3.x. At first , i think the process logical maybe wrong,
> so i change the code to very simple one and it has the same problem.After
> investigate for a week, i find it is related to pyarrow.
>
> *Reproduce procedure:*
> 1. prepare data
> The data have seven column, a、b、c、d、e、f and g, data type is Integer
> a,b,c,d,e,f,g
> 1,2,3,4,5,6,7
> 1,2,3,4,5,6,7
> 1,2,3,4,5,6,7
> 1,2,3,4,5,6,7
> produce 100,000 rows and name the file test.csv ,upload to hdfs, then load
> it , and repartition it to 1 partition.
>
> {code:java}
> df=spark.read.format('csv').option("header","true").load('/test.csv')
> df=df.select(*(col(c).cast("int").alias(c) for c in df.columns))
> df=df.repartition(1)
> spark_context = SparkContext.getOrCreate() {code}
>
> 2.register pandas udf
>
> {code:java}
> def add_func(a,b,c,d,e,f,g):
> print('iterator one time')
> return a
> add = pandas_udf(add_func, returnType=IntegerType())
> df_result=df.select(add(col("a"),col("b"),col("c"),col("d"),col("e"),col("f"),col("g"))){code}
>
> 3.apply pandas udf
>
> {code:java}
> def trigger_func(iterator):
> yield iterator
> df_result.rdd.foreachPartition(trigger_func){code}
>
> 4.execute it in pyspark (local or yarn)
> run it with conf spark.sql.execution.arrow.maxRecordsPerBatch=100000. As
> mentioned before the total row number is 1000000, it should print "iterator
> one time " 10 times.
> (1)Python 2.7 envs:
>
> {code:java}
> PYSPARK_PYTHON=/usr/lib/conda/envs/py2.7/bin/python pyspark --conf
> spark.sql.execution.arrow.maxRecordsPerBatch=100000 --conf
> spark.executor.pyspark.memory=2g --conf
> spark.sql.execution.arrow.enabled=true --executor-cores 1{code}
>
> !py2.7.png!
> The result is right, 10 times of print.
>
>
> (2)Python 3.5 or 3.6 envs:
> {code:java}
> PYSPARK_PYTHON=/usr/lib/conda/envs/python3.6/bin/python pyspark --conf
> spark.sql.execution.arrow.maxRecordsPerBatch=100000 --conf
> spark.executor.pyspark.memory=2g --conf
> spark.sql.execution.arrow.enabled=true --executor-cores{code}
>
> !py3.6.png!
> The data is incomplete. Exception is print by jvm spark which have been added
> by us , I will explain it later.
>
>
> h3. *Investigation*
> The “process done” is added in the worker.py.
> !worker.png!
> In order to get the exception, change the spark code, the code is under
> core/src/main/scala/org/apache/spark/util/Utils.scala , and add this code to
> print the exception.
>
>
> {code:java}
> @@ -1362,6 +1362,8 @@ private[spark] object Utils extends Logging {
> case t: Throwable =>
> // Purposefully not using NonFatal, because even fatal exceptions
> // we don't want to have our finallyBlock suppress
> + logInfo(t.getLocalizedMessage)
> + t.printStackTrace()
> originalThrowable = t
> throw originalThrowable
> } finally {{code}
>
>
> It seems the pyspark get the data from jvm , but pyarrow get the data
> incomplete. Pyarrow side think the data is finished, then shutdown the
> socket. At the same time, the jvm side still writes to the same socket , but
> get socket close exception.
> The pyarrow part is in ipc.pxi:
>
> {code:java}
> cdef class _RecordBatchReader:
> cdef:
> shared_ptr[CRecordBatchReader] reader
> shared_ptr[InputStream] in_stream
> cdef readonly:
> Schema schema
> def _cinit_(self):
> pass
> def _open(self, source):
> get_input_stream(source, &self.in_stream)
> with nogil:
> check_status(CRecordBatchStreamReader.Open(
> self.in_stream.get(), &self.reader))
> self.schema = pyarrow_wrap_schema(self.reader.get().schema())
> def _iter_(self):
> while True:
> yield self.read_next_batch()
> def get_next_batch(self):
> import warnings
> warnings.warn('Please use read_next_batch instead of '
> 'get_next_batch', FutureWarning)
> return self.read_next_batch()
> def read_next_batch(self):
> """
> Read next RecordBatch from the stream. Raises StopIteration at end of
> stream
> """
> cdef shared_ptr[CRecordBatch] batch
> with nogil:
> check_status(self.reader.get().ReadNext(&batch))
> if batch.get() == NULL:
> raise StopIteration
> return pyarrow_wrap_batch(batch){code}
>
> read_next_batch function get NULL, think the iterator is over.
>
> h3. *RESULT*
> Our environment is spark 2.4.3, we have tried pyarrow version 0.10.0 and
> 0.14.0 , python version is python 2.7, python 3.5, python 3.6.
> When using python 2.7, everything is fine. But when change to python
> 3.5,3,6, the data is wrong.
> The column number is critical to trigger this bug, if column number is less
> than 5 , this bug probably will not happen. But If the column number is big ,
> for example 7 or above, it happens every time.
> So we wonder if there is some conflict between python 3.x and pyarrow
> version?
> I have put the code and data as attachment.
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