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https://issues.apache.org/jira/browse/ARROW-4890?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17251632#comment-17251632
]
Dmitry Kravchuk commented on ARROW-4890:
----------------------------------------
Finally I've tried pandas_udf with 1.72 gb dataset, pyarrow version 2.0.0 and
ARROW_PRE_0_15_IPC_FORMAT=1 prompt - spark-submit returns error "OSError:
Invalid IPC message: negative bodyLength" (look at detail log number 4 in my
previous messages).
Any thoughts?
{code:java|title=Bar.Python|borderStyle=solid}
import pyspark
from pyspark.sql import functions as F, types as T
import pandas as pd
def analyze(spark):
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):
import os
os.environ["ARROW_PRE_0_15_IPC_FORMAT"] = "1"
return df
df4 = df3 \
.withColumn('df1_c1', F.col('df1_c1').cast(T.IntegerType())) \
.withColumn('df1_c2', F.col('df1_c2').cast(T.DoubleType())) \
.withColumn('df1_c3', F.col('df1_c3').cast(T.StringType())) \
.withColumn('df1_c4', F.col('df1_c4').cast(T.StringType())) \
.withColumn('df2_c1', F.col('df2_c1').cast(T.IntegerType())) \
.withColumn('df2_c2', F.col('df2_c2').cast(T.DoubleType())) \
.withColumn('df2_c3', F.col('df2_c3').cast(T.StringType())) \
.withColumn('df2_c4', F.col('df2_c4').cast(T.StringType())) \
.withColumn('df2_c5', F.col('df2_c5').cast(T.StringType())) \
.withColumn('df2_c6', F.col('df2_c6').cast(T.StringType()))
print(df4.printSchema())
udf = F.pandas_udf(df4.schema, F.PandasUDFType.GROUPED_MAP)(myudf)
df5 = df4.groupBy('df1_c1').apply(udf)
print('df5.count()', df5.count())
{code}
> [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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