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https://issues.apache.org/jira/browse/SPARK-16320?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15431351#comment-15431351
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Yin Huai commented on SPARK-16320:
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After investigation, this perf issue was caused by GC setting
(https://issues.apache.org/jira/browse/SPARK-16320?focusedCommentId=15421699=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15421699).
[~maver1ck] thank you for the investigation. [~srowen], thank you for sending
out the doc to update the doc.
> Document G1 heap region's effect on spark 2.0 vs 1.6
>
>
> Key: SPARK-16320
> URL: https://issues.apache.org/jira/browse/SPARK-16320
> Project: Spark
> Issue Type: Improvement
> Components: Documentation, SQL
>Affects Versions: 2.0.0
>Reporter: Maciej Bryński
>Assignee: Sean Owen
>Priority: Minor
> Fix For: 2.0.1, 2.1.0
>
> Attachments: spark1.6-ui.png, spark2-ui.png
>
>
> I did some test on parquet file with many nested columns (about 30G in
> 400 partitions) and Spark 2.0 is sometimes slower.
> I tested following queries:
> 1) {code}select count(*) where id > some_id{code}
> In this query performance is similar. (about 1 sec)
> 2) {code}select count(*) where nested_column.id > some_id{code}
> Spark 1.6 -> 1.6 min
> Spark 2.0 -> 2.1 min
> Should I expect such a drop in performance ?
> I don't know how to prepare sample data to show the problem.
> Any ideas ? Or public data with many nested columns ?
> *UPDATE*
> I created script to generate data and to confirm this problem.
> {code}
> #Initialization
> from pyspark import SparkContext, SparkConf
> from pyspark.sql import HiveContext
> from pyspark.sql.functions import struct
> conf = SparkConf()
> conf.set('spark.cores.max', 15)
> conf.set('spark.executor.memory', '30g')
> conf.set('spark.driver.memory', '30g')
> sc = SparkContext(conf=conf)
> sqlctx = HiveContext(sc)
> #Data creation
> MAX_SIZE = 2**32 - 1
> path = '/mnt/mfs/parquet_nested'
> def create_sample_data(levels, rows, path):
>
> def _create_column_data(cols):
> import random
> random.seed()
> return {"column{}".format(i): random.randint(0, MAX_SIZE) for i in
> range(cols)}
>
> def _create_sample_df(cols, rows):
> rdd = sc.parallelize(range(rows))
> data = rdd.map(lambda r: _create_column_data(cols))
> df = sqlctx.createDataFrame(data)
> return df
>
> def _create_nested_data(levels, rows):
> if len(levels) == 1:
> return _create_sample_df(levels[0], rows).cache()
> else:
> df = _create_nested_data(levels[1:], rows)
> return df.select([struct(df.columns).alias("column{}".format(i))
> for i in range(levels[0])])
> df = _create_nested_data(levels, rows)
> df.write.mode('overwrite').parquet(path)
>
> #Sample data
> create_sample_data([2,10,200], 100, path)
> #Query
> df = sqlctx.read.parquet(path)
> %%timeit
> df.where("column1.column5.column50 > {}".format(int(MAX_SIZE / 2))).count()
> {code}
> Results
> Spark 1.6
> 1 loop, best of 3: *1min 5s* per loop
> Spark 2.0
> 1 loop, best of 3: *1min 21s* per loop
> *UPDATE 2*
> Analysis in https://issues.apache.org/jira/browse/SPARK-16321 direct to same
> source.
> I attached some VisualVM profiles there.
> Most interesting are from queries.
> https://issues.apache.org/jira/secure/attachment/12818785/spark16_query.nps
> https://issues.apache.org/jira/secure/attachment/12818784/spark2_query.nps
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