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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