Github user NathanHowell commented on the pull request:
https://github.com/apache/spark/pull/5801#issuecomment-97959395
Benchmarked a small-ish real dataset... Runs are with 5 executors (for 5
input splits) with data in HDFS:
step | before | after
------|----------|--------
`val df = sqlContext.jsonRDD(...)` - schema inference | 37.14s | 18.16s |
`df.count()` | 125.8s | 25.7s
`df.select("col1").count()` | 96.9s | 26.5s
Not sure why but the new code seems a bit slower when using projection
pushdowns. It may be schema dependent or overhead from evaluating the
projection expression.
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