[
https://issues.apache.org/jira/browse/ARROW-10957?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17253444#comment-17253444
]
Liya Fan commented on ARROW-10957:
----------------------------------
[~dishka_krauch] Sorry I don't know how to help. It seems difficult to share a
data set with 1.72 gb size.
Currently, Arrow supports buffer sizes larger than 2GB. So it is OK to transfer
large vectors/arrays/record batches. However, for metadata (e.g. schema), the
2gb limit still exists. This is because in most applications, the metadata size
is relatively small, so 2gb should be more than enough.
So my suggestion is to debug your code to see why the metadata size is so
large. Is it plausible, or there are some bugs?
> Expanding pyarrow buffer size more than 2GB for pandas_udf functions
> --------------------------------------------------------------------
>
> Key: ARROW-10957
> URL: https://issues.apache.org/jira/browse/ARROW-10957
> Project: Apache Arrow
> Issue Type: Improvement
> Components: C++, Java, Python
> Affects Versions: 2.0.0
> Environment: Spark: 2.4.4
> Python:
> Dcycler (0.10.0)
> glmnet-py (0.1.0b2)
> joblib (1.0.0)
> kiwisolver (1.3.1)
> lightgbm (3.1.1) EPRECATION
> matplotlib (3.0.3)
> numpy (1.19.4)
> pandas (1.1.5)
> pip (9.0.3: The default format will switch to columns in the future. You can)
> pyarrow 2.0.0
> pyparsing (2.4.7) use --format=(legacy|columns) (or define a
> format=(python-dateutil (2.8.1)
> pytz (202legacy|columns) in yo0.4)
> scikit-learn (0.23.2)
> scipy (1.5.4)
> setuptools (51.0.0) ur pip.conf under the [list] section) to disable this
> warnsix (1.15.0)
> sklearn (0.0)
> threadpoolctl (2.1.0)
> venv-paing. ck (0.2.0)
> wheel (0.36.2)
> Reporter: Dmitry Kravchuk
> Priority: Major
> Labels: features, patch, performance
> Fix For: 2.0.1
>
> Original Estimate: 672h
> Remaining Estimate: 672h
>
> There is 2GB limit for data that can be passed to any pandas_udf function and
> the aim of this issue is to expand this limit. It's very small buffer size if
> we use pyspark and our goal is fitting machine learning models.
> Steps to reproduce - just use following spark-submit for executing following
> after python function.
> {code:java}
> %sh
> cd /home/zeppelin/code && \
> export PYSPARK_DRIVER_PYTHON=/home/zeppelin/envs/env3/bin/python && \
> export PYSPARK_PYTHON=./env3/bin/python && \
> export ARROW_PRE_0_15_IPC_FORMAT=1 && \
> spark-submit \
> --master yarn \
> --deploy-mode client \
> --num-executors 5 \
> --executor-cores 5 \
> --driver-memory 8G \
> --executor-memory 8G \
> --conf spark.executor.memoryOverhead=4G \
> --conf spark.driver.memoryOverhead=4G \
> --archives /home/zeppelin/env3.tar.gz#env3 \
> --jars "/opt/deltalake/delta-core_2.11-0.5.0.jar" \
> --py-files jobs.zip,"/opt/deltalake/delta-core_2.11-0.5.0.jar" main.py \
> --job temp
> {code}
>
> {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}
> If you need more details please let me know.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
