Dude,
please, attach the execution plan of the query and details about the
indexes.
2015-03-06 9:07 GMT-03:00 anu <anamika.guo...@gmail.com>:
> I have a query that's like:
>
> Could you help in providing me pointers as to how to start to optimize it
> w.r.t. spark sql:
>
>
> sqlContext.sql("
>
> SELECT dw.DAY_OF_WEEK, dw.HOUR, avg(dw.SDP_USAGE) AS AVG_SDP_USAGE
>
> FROM (
> SELECT sdp.WID, DAY_OF_WEEK, HOUR, SUM(INTERVAL_VALUE) AS
> SDP_USAGE
>
> FROM (
> SELECT *
>
> FROM date_d dd JOIN interval_f intf
>
> ON intf.DATE_WID = dd.WID
>
> WHERE intf.DATE_WID >= 20141116 AND
> intf.DATE_WID <= 20141125 AND CAST(INTERVAL_END_TIME AS STRING) >=
> '2014-11-16 00:00:00.000' AND CAST(INTERVAL_END_TIME
> AS STRING) <= '2014-11-26
> 00:00:00.000' AND MEAS_WID = 3
>
> ) test JOIN sdp_d sdp
>
> ON test.SDP_WID = sdp.WID
>
> WHERE sdp.UDC_ID = 'SP-1931201848'
>
> GROUP BY sdp.WID, DAY_OF_WEEK, HOUR, sdp.UDC_ID
>
> ) dw
>
> GROUP BY dw.DAY_OF_WEEK, dw.HOUR")
>
>
>
> Currently the query takes 15 minutes execution time where interval_f table
> holds approx 170GB of raw data, date_d --> 170 MB and sdp_d --> 490MB
>
>
>
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