Hey Matei,
Thanks for your reply. We would keep in mind to use JDBC server for smaller
queries.
For the mapreduce job start-up, are you pointing towards JVM initialization
latencies in MR? Other than JVM initialization, does Spark do any
optimization (that is not done by mapreduce) to speed up the startup?
--
Regards,
Saumitra Shahapure
On Fri, Jan 23, 2015 at 2:08 PM, Matei Zaharia matei.zaha...@gmail.com
wrote:
It's hard to tell without more details, but the start-up latency in Hive
can sometimes be high, especially if you are running Hive on MapReduce. MR
just takes 20-30 seconds per job to spin up even if the job is doing
nothing.
For real use of Spark SQL for short queries by the way, I'd recommend
using the JDBC server so that you can have a long-running Spark process. It
gets quite a bit faster after the first few queries.
Matei
On Jan 22, 2015, at 10:22 PM, Saumitra Shahapure (Vizury)
saumitra.shahap...@vizury.com wrote:
Hello,
We were comparing performance of some of our production hive queries
between Hive and Spark. We compared Hive(0.13)+hadoop (1.2.1) against
both
Spark 0.9 and 1.1. We could see that the performance gains have been good
in Spark.
We tried a very simple query,
select count(*) from T where col3=123
in both sparkSQL and Hive (with hive.map.aggr=true) and found that Spark
performance had been 2x better than Hive (120sec vs 60sec). Table T is
stored in S3 and contains 600MB single GZIP file.
My question is, why Spark is faster than Hive here? In both of the cases,
the file will be downloaded, uncompressed and lines will be counted by a
single process. For Hive case, reducer will be identity function
since hive.map.aggr is true.
Note that disk spills and network I/O are very less for Hive's case as
well,
--
Regards,
Saumitra Shahapure
