[jira] [Created] (SPARK-16321) Pyspark 2.0 performance drop vs pyspark 1.6

["Maciej Bryński (JIRA)" <ji...@apache.org>]

Maciej Bryński created SPARK-16321:
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             Summary: 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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