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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