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From James Aley <>
Subject Help optimising Spark SQL query
Date Mon, 22 Jun 2015 15:28:52 GMT

A colleague of mine ran the following Spark SQL query:

  count(*) as uses,
  count (distinct cast(id as string)) as users
from usage_events
  from_unixtime(cast(timestamp_millis/1000 as bigint))
between '2015-06-09' and '2015-06-16'

The table contains billions of rows, but totals only 64GB of data across
~30 separate files, which are stored as Parquet with LZO compression in S3.

>From the referenced columns:

* id is Binary, which we cast to a String so that we can DISTINCT by it. (I
was already told this will improve in a later release, in a separate
* timestamp_millis is a long, containing a unix timestamp with millisecond

This took nearly 2 hours to run on a 5 node cluster of r3.xlarge EC2
instances, using 20 executors, each with 4GB memory. I can see from
monitoring tools that the CPU usage is at 100% on all nodes, but incoming
network seems a bit low at 2.5MB/s, suggesting to me that this is CPU-bound.

Does that seem slow? Can anyone offer any ideas by glancing at the query as
to why this might be slow? We'll profile it meanwhile and post back if we
find anything ourselves.

A side issue - I've found that this query, and others, sometimes completes
but doesn't return any results. There appears to be no error that I can see
in the logs, and Spark reports the job as successful, but the connected
JDBC client (SQLWorkbenchJ in this case), just sits there forever waiting.
I did a quick Google and couldn't find anyone else having similar issues.

Many thanks,


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