You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@arrow.apache.org by "SURESH CHAGANTI (Jira)" <ji...@apache.org> on 2019/11/04 20:51:00 UTC
[jira] [Commented] (ARROW-4890) [Python] Spark+Arrow Grouped pandas
UDAF - read length must be positive or -1
[ https://issues.apache.org/jira/browse/ARROW-4890?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16966994#comment-16966994 ]
SURESH CHAGANTI commented on ARROW-4890:
----------------------------------------
[~emkornfield@gmail.com] is there any size limit as to how much we can send to pandas_udf ? I am also seeing the same error as above and my groups are pretty large around 200M records and size is around 2 to 4 GB
> [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_1Nst5AjjCRg0MExO5Kby9i-g@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.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)