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From Eric Pederson <>
Subject Re: Subsecond queries possible?
Date Wed, 01 Jul 2015 23:02:17 GMT
I removed all of the indices from the table in IQ and the time went up to
700ms for the query on the full dataset.   The best time I've got so far
with Spark for the full dataset is 4s with a cached table and 30 cores.

However, every column in IQ is automatically indexed by default
and those indexes you can't remove.  They aren't even listed in the
metadata.  So even though I removed all of the indexes the default indexes
are still there.

It's a baseline but I'm really comparing apples and oranges right now.
 But it's an interesting experiment nonetheless.

-- Eric

On Wed, Jul 1, 2015 at 12:47 PM, Debasish Das <>

> If you take bitmap indices out of sybase then I am guessing spark sql will
> be at par with sybase ?
> On that note are there plans of integrating indexed rdd ideas to spark sql
> to build indices ? Is there a JIRA tracking it ?
> On Jun 30, 2015 7:29 PM, "Eric Pederson" <> wrote:
>> Hi Debasish:
>> We have the same dataset running on SybaseIQ and after the caches are
>> warm the queries come back in about 300ms.  We're looking at options to
>> relieve overutilization and to bring down licensing costs.  I realize that
>> Spark may not be the best fit for this use case but I'm interested to see
>> how far it can be pushed.
>> Thanks for your help!
>> -- Eric
>> On Tue, Jun 30, 2015 at 5:28 PM, Debasish Das <>
>> wrote:
>>> I got good runtime improvement from hive partitioninp, caching the
>>> dataset and increasing the cores through repartition...I think for your
>>> case generating mysql style indexing will help is not supported
>>> in spark sql yet...
>>> I know the dataset might be too big for 1 node mysql but do you have a
>>> runtime estimate from running the same query on mysql with appropriate
>>> column indexing ? That should give us a good baseline number...
>>> For my case at least I could not put the data on 1 node mysql as it was
>>> big...
>>> If you can write the problem in a document view you can use a document
>>> store like solr/elastisearch to boost runtime...the reverse indices can get
>>> you subsecond latencies...again the schema design matters for that and you
>>> might have to let go some of sql expressiveness (like balance in a
>>> predefined bucket might be fine but looking for the exact number might be
>>> slow)

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