The 2G limit that Uwe mentioned definitely exists, Spark serialize each group as a single RecordBatch currently.
The "pyarrow.lib.ArrowIOError: read length must be positive or -1" is strange, I think Spark is on an older version of the Java side (0.10 for Spark 2.4 and 0.8 for Spark 2.3). I forgot whether there is binary incompatibility between these versions and pyarrow 0.12. On Fri, Mar 1, 2019 at 3:32 PM Abdeali Kothari <abdealikoth...@gmail.com> wrote: > 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 <abdealikoth...@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 <uw...@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 <uw...@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, > >> > > > > >> > > > >> > > >> > > >
