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From Abdeali Kothari <abdealikoth...@gmail.com>
Subject Re: OversizedAllocationException for pandas_udf in pyspark
Date Fri, 01 Mar 2019 20:32:27 GMT
Forgot to mention: The above testing is with 0.11.1
I tried 0.12.1 as you suggested - and am getting the
OversizedAllocationException with the 80char column. And getting read
length must be positive or -1 without that. So, both the issues are
reproducible with pyarrow 0.12.1

On Sat, Mar 2, 2019 at 1:57 AM Abdeali Kothari <abdealikothari@gmail.com>
wrote:

> That was spot on!
> I had 3 columns with 80characters => 80*21*10^6 = 1.56 bytes
> I removed these columns and replaced each with 10 doubleType columns (so
> it would still be 80 bytes of data) - and this error didn't come up anymore.
> I also removed all the other columns and just kept 1 column with
> 80characters - I got the error again.
>
> I'll make a simpler example and report it to spark - as I guess these
> columns would need some special handling.
>
> Now, when I run - I get a different error:
> 19/03/01 20:16:49 WARN TaskSetManager: Lost task 108.0 in stage 8.0 (TID
> 12, ip-172-31-10-249.us-west-2.compute.internal, executor 1):
> org.apache.spark.api.python.PythonException: Traceback (most recent call
> last):
>   File
> "/mnt/yarn/usercache/hadoop/appcache/application_1551469777576_0010/container_1551469777576_0010_01_000002/pyspark.zip/pyspark/worker.py",
> line 230, in main
>     process()
>   File
> "/mnt/yarn/usercache/hadoop/appcache/application_1551469777576_0010/container_1551469777576_0010_01_000002/pyspark.zip/pyspark/worker.py",
> line 225, in process
>     serializer.dump_stream(func(split_index, iterator), outfile)
>   File
> "/mnt/yarn/usercache/hadoop/appcache/application_1551469777576_0010/container_1551469777576_0010_01_000002/pyspark.zip/pyspark/serializers.py",
> line 260, in dump_stream
>     for series in iterator:
>   File
> "/mnt/yarn/usercache/hadoop/appcache/application_1551469777576_0010/container_1551469777576_0010_01_000002/pyspark.zip/pyspark/serializers.py",
> line 279, in load_stream
>     for batch in reader:
>   File "pyarrow/ipc.pxi", line 265, in __iter__
>   File "pyarrow/ipc.pxi", line 281, in
> pyarrow.lib._RecordBatchReader.read_next_batch
>   File "pyarrow/error.pxi", line 83, in pyarrow.lib.check_status
> pyarrow.lib.ArrowIOError: read length must be positive or -1
>
> Again, any pointers on what this means and what it indicates would be
> really useful for me.
>
> Thanks for the replies!
>
>
> On Fri, Mar 1, 2019 at 11:26 PM Uwe L. Korn <uwelk@xhochy.com> wrote:
>
>> There is currently the limitation that a column in a single RecordBatch
>> can only hold 2G on the Java side. We work around this by splitting the
>> DataFrame under the hood into multiple RecordBatches. I'm not familiar with
>> the Spark<->Arrow code but I guess that in this case, the Spark code can
>> only handle a single RecordBatch.
>>
>> Probably it is best to construct a https://stackoverflow.com/help/mcve
>> and create an issue with the Spark project. Most likely this is not a bug
>> in Arrow but just requires a bit more complicated implementation around the
>> Arrow libs.
>>
>> Still, please have a look at the exact size of your columns. We support
>> 2G per column, if it is only 1.5G, then there is probably a rounding error
>> in the Arrow. Alternatively, you might also be in luck that the following
>> patch
>> https://github.com/apache/arrow/commit/bfe6865ba8087a46bd7665679e48af3a77987cef
>> which is part of Apache Arrow 0.12 already fixes your problem.
>>
>> Uwe
>>
>> On Fri, Mar 1, 2019, at 6:48 PM, Abdeali Kothari wrote:
>> > Is there a limitation that a single column cannot be more than 1-2G ?
>> > One of my columns definitely would be around 1.5GB of memory.
>> >
>> > I cannot split my DF into more partitions as I have only 1 ID and I'm
>> > grouping by that ID.
>> > So, the UDAF would only run on a single pandasDF
>> > I do have a requirement to make a very large DF for this UDAF (8GB as i
>> > mentioned above) - trying to figure out what I need to do here to make
>> this
>> > work.
>> > Increasing RAM, etc. is no issue (i understand I'd need huge executors
>> as I
>> > have a huge data requirement). But trying to figure out how much to
>> > actually get - cause 20GB of RAM for the executor is also erroring out
>> > where I thought ~10GB would have been enough
>> >
>> >
>> >
>> > On Fri, Mar 1, 2019 at 10:25 PM Uwe L. Korn <uwelk@xhochy.com> wrote:
>> >
>> > > Hello Abdeali,
>> > >
>> > > a problem could here be that a single column of your dataframe is
>> using
>> > > more than 2GB of RAM (possibly also just 1G). Try splitting your
>> DataFrame
>> > > into more partitions before applying the UDAF.
>> > >
>> > > Cheers
>> > > Uwe
>> > >
>> > > On Fri, Mar 1, 2019, at 9:09 AM, Abdeali Kothari wrote:
>> > > > I was using arrow with spark+python and when I'm trying some
>> pandas-UDAF
>> > > > functions I am getting this error:
>> > > >
>> > > > org.apache.arrow.vector.util.OversizedAllocationException: Unable
to
>> > > > expand
>> > > > the buffer
>> > > > at
>> > > >
>> > >
>> org.apache.arrow.vector.BaseVariableWidthVector.reallocDataBuffer(BaseVariableWidthVector.java:457)
>> > > > at
>> > > >
>> > >
>> org.apache.arrow.vector.BaseVariableWidthVector.handleSafe(BaseVariableWidthVector.java:1188)
>> > > > at
>> > > >
>> > >
>> org.apache.arrow.vector.BaseVariableWidthVector.setSafe(BaseVariableWidthVector.java:1026)
>> > > > at
>> > > >
>> > >
>> org.apache.spark.sql.execution.arrow.StringWriter.setValue(ArrowWriter.scala:256)
>> > > > at
>> > > >
>> > >
>> org.apache.spark.sql.execution.arrow.ArrowFieldWriter.write(ArrowWriter.scala:122)
>> > > > at
>> > > >
>> > >
>> org.apache.spark.sql.execution.arrow.ArrowWriter.write(ArrowWriter.scala:87)
>> > > > at
>> > > >
>> > >
>> org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$2$$anonfun$writeIteratorToStream$1.apply$mcV$sp(ArrowPythonRunner.scala:84)
>> > > > at
>> > > >
>> > >
>> org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$2$$anonfun$writeIteratorToStream$1.apply(ArrowPythonRunner.scala:75)
>> > > > at
>> > > >
>> > >
>> org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$2$$anonfun$writeIteratorToStream$1.apply(ArrowPythonRunner.scala:75)
>> > > > at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1380)
>> > > > at
>> > > >
>> > >
>> org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$2.writeIteratorToStream(ArrowPythonRunner.scala:95)
>> > > > at
>> > > >
>> > >
>> org.apache.spark.api.python.BasePythonRunner$WriterThread$$anonfun$run$1.apply(PythonRunner.scala:215)
>> > > > at
>> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1991)
>> > > > at
>> > > >
>> > >
>> org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:170)
>> > > >
>> > > > I was initially getting a RAM is insufficient error - and
>> theoretically
>> > > > (with no compression) realized that the pandas DataFrame it would
>> try to
>> > > > create would be ~8GB (21million records with each record having ~400
>> > > > bytes). I have increased my executor memory to be 20GB per
>> executor, but
>> > > am
>> > > > now getting this error from Arrow.
>> > > > Looking for some pointers so I can understand this issue better.
>> > > >
>> > > > Here's what I am trying. I have 2 tables with string columns where
>> the
>> > > > strings always have a fixed length:
>> > > > *Table 1*:
>> > > >     id: integer
>> > > >    char_column1: string (length = 30)
>> > > >    char_column2: string (length = 40)
>> > > >    char_column3: string (length = 10)
>> > > >    ...
>> > > > In total, in table1, the char-columns have ~250 characters
>> > > >
>> > > > *Table 2*:
>> > > >     id: integer
>> > > >    char_column1: string (length = 50)
>> > > >    char_column2: string (length = 3)
>> > > >    char_column3: string (length = 4)
>> > > >    ...
>> > > > In total, in table2, the char-columns have ~150 characters
>> > > >
>> > > > I am joining these tables by ID. In my current dataset, I have
>> filtered
>> > > my
>> > > > data so only id=1 exists.
>> > > > Table1 has ~400 records for id=1 and table2 has 50k records for
>> id=1.
>> > > > Hence, total number of records (after joining) for table1_join2 =
>> 400 *
>> > > 50k
>> > > > = 20*10^6 records
>> > > > Each row has ~400bytes (150+250) => overall memory = 8*10^9 bytes
>> => ~8GB
>> > > >
>> > > > Now, when I try an executor with 20GB RAM, it does not work.
>> > > > Is there some data duplicity happening internally ? What should be
>> the
>> > > > estimated RAM I need to give for this to work ?
>> > > >
>> > > > Thanks for reading,
>> > > >
>> > >
>> >
>>
>

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