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https://issues.apache.org/jira/browse/ARROW-4890?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17156903#comment-17156903
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Ruslan Dautkhanov commented on ARROW-4890:
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Thanks for the details [[email protected]]! Great to know this issue is
with netty allocator and other issues are resolved. Understood that there might
be other things to complete on Arrow side to lift the 2Gb limitation. Created
https://issues.apache.org/jira/browse/SPARK-32294 for the Spark side.
> [Python] Spark+Arrow Grouped pandas UDAF - read length must be positive or -1
> -----------------------------------------------------------------------------
>
> Key: ARROW-4890
> URL: https://issues.apache.org/jira/browse/ARROW-4890
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 0.8.0
> Environment: Cloudera cdh5.13.3
> Cloudera Spark 2.3.0.cloudera3
> Reporter: Abdeali Kothari
> Priority: Major
> Attachments: Task retry fails.png, image-2019-07-04-12-03-57-002.png
>
>
> Creating this in Arrow project as the traceback seems to suggest this is an
> issue in Arrow.
> Continuation from the conversation on the
> https://mail-archives.apache.org/mod_mbox/arrow-dev/201903.mbox/%3CCAK7Z5T_mChuqhFDAF2U68dO=p_1nst5ajjcrg0mexo5kby9...@mail.gmail.com%3E
> When I run a GROUPED_MAP UDF in Spark using PySpark, I run into the error:
> {noformat}
> File
> "/opt/cloudera/parcels/SPARK2-2.3.0.cloudera3-1.cdh5.13.3.p0.458809/lib/spark2/python/lib/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
> {noformat}
> as my dataset size starts increasing that I want to group on. Here is a
> reproducible code snippet where I can reproduce this.
> Note: My actual dataset is much larger and has many more unique IDs and is a
> valid usecase where I cannot simplify this groupby in any way. I have
> stripped out all the logic to make this example as simple as I could.
> {code:java}
> import os
> os.environ['PYSPARK_SUBMIT_ARGS'] = '--executor-memory 9G pyspark-shell'
> import findspark
> findspark.init()
> import pyspark
> from pyspark.sql import functions as F, types as T
> import pandas as pd
> spark = pyspark.sql.SparkSession.builder.getOrCreate()
> pdf1 = pd.DataFrame(
> [[1234567, 0.0, "abcdefghij", "2000-01-01T00:00:00.000Z"]],
> columns=['df1_c1', 'df1_c2', 'df1_c3', 'df1_c4']
> )
> df1 = spark.createDataFrame(pd.concat([pdf1 for i in
> range(429)]).reset_index()).drop('index')
> pdf2 = pd.DataFrame(
> [[1234567, 0.0, "abcdefghijklmno", "2000-01-01", "abcdefghijklmno",
> "abcdefghijklmno"]],
> columns=['df2_c1', 'df2_c2', 'df2_c3', 'df2_c4', 'df2_c5', 'df2_c6']
> )
> df2 = spark.createDataFrame(pd.concat([pdf2 for i in
> range(48993)]).reset_index()).drop('index')
> df3 = df1.join(df2, df1['df1_c1'] == df2['df2_c1'], how='inner')
> def myudf(df):
> return df
> df4 = df3
> udf = F.pandas_udf(df4.schema, F.PandasUDFType.GROUPED_MAP)(myudf)
> df5 = df4.groupBy('df1_c1').apply(udf)
> print('df5.count()', df5.count())
> # df5.write.parquet('/tmp/temp.parquet', mode='overwrite')
> {code}
> I have tried running this on Amazon EMR with Spark 2.3.1 and 20GB RAM per
> executor too.
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