saiku執行速度優化二

上一篇文章介紹了添加filter能夠加快查詢速度。下面繼續分析：

下面這個MDX語句：

WITH
SET [~FILTER] AS
    {[create_date].[create_date].[2013-01-01]}
SET [~ROWS_dimPartner_dimPartner] AS
    {[dimPartner].[dimPartner].[name].Members}
SET [~ROWS_sight_sight] AS
    Hierarchize({{[sight].[sight].[country].Members}, {[sight].[sight].[area].Members}})
SELECT
NON EMPTY {[Measures].[money], [Measures].[quantity], [Measures].[qunar_income], [Measures].[order_num]} ON COLUMNS,
NON EMPTY Order(NonEmptyCrossJoin([~ROWS_dimPartner_dimPartner], [~ROWS_sight_sight]), [Measures].[money], BDESC) ON ROWS
FROM [com_order_detail_cube]
WHERE [~FILTER]
2016-04-15 17:56:05,176 DEBUG [org.saiku.service.olap.ThinQueryService] Query End
2016-04-15 17:56:05,177 INFO  [org.saiku.service.olap.ThinQueryService] RUN#:94 Size: 7/8       Execute:        444501ms        Format: 1ms     Totals: 0ms      Total: 444502ms

查看數據查詢語句，本琢磨這應該很快，執行一個sql便可

select "com_order_detail_view"."create_date" as "c0", "dim_partner"."name" as "c1", "com_order_detail_view"."area" as "c2", sum("com_order_detail_view"."money") as "m0", sum("com_order_detail_view"."quantity") as "m1", sum("com_order_detail_view"."qunar_income") as "m2", count(distinct "com_order_detail_view"."display_id") as "m3" from "com_order_detail_view" as "com_order_detail_view", "dim_partner" as "dim_partner" where "com_order_detail_view"."create_date" = DATE '2013-01-01' and "com_order_detail_view"."partner" = "dim_partner"."code" group by "com_order_detail_view"."create_date", "dim_partner"."name", "com_order_detail_view"."area";

結果發如今這以前，還須要執行這條語句，且執行時間最長：

select count(distinct "area") from "com_order_detail_view"

意淫緣由應該是saiku對執行的結果進行format，因此才執行這條語句，而這個字段應該是維度表，因此猜測使用維度表或者能夠提高效率。通過測試，發現確實如此，因此儘可能使用緯度表優化數據的使用。