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https://issues.apache.org/jira/browse/ARROW-6011?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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jiangyu updated ARROW-6011:
---------------------------
Attachment: test.csv
test.py
> Data incomplete when using pyarrow in pyspark in python 3.x
> -----------------------------------------------------------
>
> Key: ARROW-6011
> URL: https://issues.apache.org/jira/browse/ARROW-6011
> Project: Apache Arrow
> Issue Type: Bug
> Components: Java, Python
> Affects Versions: 0.10.0, 0.14.0
> Environment: ceonts 7.4 pyarrow 0.10.0 0.14.0 python 2.7 3.5
> 3.6
> Reporter: jiangyu
> Priority: Major
> Attachments: image-2019-07-23-16-06-49-889.png, py3.6.png, test.csv,
> test.py, worker.png
>
>
> Hi,
>
> In spark 2.3, pandas udf add to pyspark and pyarrow as a default
> serialization and deserialization method. It is a great feature, and we use
> it a lot.
> But , when we change the default python version from 2.7 to 3.5 or 3.6 (
> conda as python envs manager), we encounter a fatal problem.
> We use pandas udf to process batches of data, but we find the data is
> incompelted. 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 it:
>
> Below is how to reproduce it:
> 1.generate data
> first generate a very simple data, the data have seven column, a、b、c、d、e、f
> and g, every row is the same，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
> we can 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
> make a very simple pandas udf function and register it.The function is very
> simple , just print “iterator one time” and do nothing then return.
>
> {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.trigger spark to action
>
> {code:java}
> def trigger_func(iterator):
> yield iterator
> df_result.rdd.foreachPartition(trigger_func){code}
>
> 4.execute it in pyspark (local or yarn)
> we set spark.sql.execution.arrow.maxRecordsPerBatch=100000, and the rows is
> 1,000,000 , so it is should print “iterator one time” for 10 times.
> (1)Here is result in 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}
>
> !image-2019-07-23-16-06-49-889.png!
> The result is right, 10 times of print.
>
>
> (2)Then change to python 3.6 envs,with the same code.
> {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.
> The exception is print by spark which have been added by us , I will explain
> it later.
>
>
> h3. Investigation
> So i just add some log to trace it. The “process done” is added in the
> worker.py.
> !worker.png!
> In order to get the exception, we also 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
> {code}
>
> return pyarrow_wrap_batch(batch)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 will happened 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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