I'd ask another question first: If your SQL query can be executed in a performant fashion against a conventional (RDBMS?) database, why are you trying to use Spark?  How you answer that question will be the key to deciding among the engineering design tradeoffs to effectively use Spark or some other solution.

On Tue, Dec 1, 2015 at 4:23 PM, Andrés Ivaldi <iaivaldi@gmail.com> wrote:
Ok, so latency problem is being generated because I'm using SQL as source? how about csv, hive, or another source?

On Tue, Dec 1, 2015 at 9:18 PM, Mark Hamstra <mark@clearstorydata.com> wrote:
It is not designed for interactive queries.

You might want to ask the designers of Spark, Spark SQL, and particularly some things built on top of Spark (such as BlinkDB) about their intent with regard to interactive queries.  Interactive queries are not the only designed use of Spark, but it is going too far to claim that Spark is not designed at all to handle interactive queries.

That being said, I think that you are correct to question the wisdom of expecting lowest-latency query response from Spark using SQL (sic, presumably a RDBMS is intended) as the datastore.

On Tue, Dec 1, 2015 at 4:05 PM, Jörn Franke <jornfranke@gmail.com> wrote:
Hmm it will never be faster than SQL if you use SQL as an underlying storage. Spark is (currently) an in-memory batch engine for iterative machine learning workloads. It is not designed for interactive queries. 
Currently hive is going into the direction of interactive queries. Alternatives are Hbase on Phoenix or Impala.

On 01 Dec 2015, at 21:58, Andrés Ivaldi <iaivaldi@gmail.com> wrote:

The use case would be,
Have spark in a service (I didnt invertigate this yet), through api calls of this service we perform some aggregations over data in SQL, We are already doing this with an internal development

Nothing complicated, for instance, a table with Product, Product Family, cost, price, etc. Columns like Dimension and Measures,

I want to Spark for query that table to perform a kind of rollup, with cost as Measure and Prodcut, Product Family as Dimension

Only 3 columns, it takes like 20s to perform that query and the aggregation, the  query directly to the database with a grouping at the columns takes like 1s


On Tue, Dec 1, 2015 at 5:38 PM, Jörn Franke <jornfranke@gmail.com> wrote:
can you elaborate more on the use case?

> On 01 Dec 2015, at 20:51, Andrés Ivaldi <iaivaldi@gmail.com> wrote:
> Hi,
> I'd like to use spark to perform some transformations over data stored inSQL, but I need low Latency, I'm doing some test and I run into spark context creation and data query over SQL takes too long time.
> Any idea for speed up the process?
> regards.
> --
> Ing. Ivaldi Andres

Ing. Ivaldi Andres

Ing. Ivaldi Andres