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https://issues.apache.org/jira/browse/SPARK-16321?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Maciej Bryński updated SPARK-16321:
-----------------------------------
Description:
I did some test on parquet file with many nested columns (about 30G in
400 partitions) and Spark 2.0 is 2x slower.
{code}
df = sqlctx.read.parquet(path)
df.where('id > some_id').rdd.flatMap(lambda r: [r.id] if not r.id %100000 else
[]).collect()
{code}
Spark 1.6 -> 2.3 min
Spark 2.0 -> 4.6 min (2x slower)
I used BasicProfiler for this task and cumulative time was:
Spark 1.6 - 4300 sec
Spark 2.0 - 5800 sec
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 ?
was:
I did some test on parquet file with many nested columns (about 30G in
400 partitions) and Spark 2.0 is 2x slower.
{code}
df = sqlctx.read.parquet(path)
df.where('id > some_id').rdd.flatMap(lambda r: [r.id] if not r.id %
100000 else []).collect()
{code}
Spark 1.6 -> 2.3 min
Spark 2.0 -> 4.6 min (2x slower)
I used BasicProfiler for this task and cumulative time was:
Spark 1.6 - 4300 sec
Spark 2.0 - 5800 sec
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 ?
> Pyspark 2.0 performance drop vs pyspark 1.6
> -------------------------------------------
>
> Key: SPARK-16321
> URL: https://issues.apache.org/jira/browse/SPARK-16321
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.0.0
> Reporter: Maciej Bryński
>
> I did some test on parquet file with many nested columns (about 30G in
> 400 partitions) and Spark 2.0 is 2x slower.
> {code}
> df = sqlctx.read.parquet(path)
> df.where('id > some_id').rdd.flatMap(lambda r: [r.id] if not r.id %100000
> else []).collect()
> {code}
> Spark 1.6 -> 2.3 min
> Spark 2.0 -> 4.6 min (2x slower)
> I used BasicProfiler for this task and cumulative time was:
> Spark 1.6 - 4300 sec
> Spark 2.0 - 5800 sec
> 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 ?
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