spencerlee created SPARK-13140:
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Summary: spark sql aggregate performance decrease
Key: SPARK-13140
URL: https://issues.apache.org/jira/browse/SPARK-13140
Project: Spark
Issue Type: Question
Affects Versions: 1.6.0
Reporter: spencerlee
In our scenario, their are 30 + key columns with 60+ metric columns.
our typical query is: select key1, key2, key3, key4, key5, sum(metric1),
sum(metric2), sum(metric3).... sum(metric30) from table_name group by key1,
key2, key3, key4, key5.
I import a single parquet file(60M, about 250w+ records) into sparksql , and do
the typical query with local mode. I found that, when I only aggregate 24
metrics, the response time is about 4.81s, when I aggregate 25+ metrics, the
response time is 45.9s, which is almost 10 times slower. that's obviously
unreasonable.
Is this a bug or need modify some configuration to tune the query?
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