I am having trouble optimizing a query that does an inner join using a date range. The purposes of the query is take daily data and summarize by week.
Select pcw.EndDate WeekEndDate, h.Store, SUM(h.DeliveryChargesTotal) DeliveryChargesTotal from Daily_GC_Headers h inner join PeriodCalendar_Weeks pcw on h.SalesDate between pcw.StartDate and pcw.EndDate where SalesDate between @StartDate and @EndDate and isCanceled = 0 group by pcw.EndDate, h.Store
Simplified schema of
Daily_GC_Headers table (13.8 million rows; about 5.4 million match criteria in WHERE clause):
Store - Varchar(10) (PK) SalesDate - Date (PK) TicketNumber - SmallInt (PK; starts over 1 each day at each store.) IsCanceled - Bit DeliveryChargesTotal - Decimal(9,2)
Simplified schema of
PeriodCalendar_Weeks Table (570 rows; 53 match the criteria):
Year - smallint (PK) Period - tinyint (PK) Week - tinyint (PK) StartDate - Date EndDate - Date
This query takes about 15 seconds in SSMS. Querying
Daily_GC_Headers by itself (and just grouping by
Store) takes 2 seconds. A query against
PeriodCalendar_Weeks is "instant".
DBCC SHOW_STATISTICS indicates that the stats are both tables are current (we run a weekly job to update them). I've tried clearing the plan caches.
The execution plan is strange. For example, it is doing an Eager Spool on
PeriodCalendar_Weeks. The estimated rows is 156.6 but the actual rows is 153,971. It then filters the results of that first spool and does a Lazy Spool. The estimated/actual rows of that 2nd spool is 5.4 million, even though the underlying table has less than 600 rows in it.
What should I be looking for or doing to optimize this?
For sake of clarity, I initially described an oversimplified PK on the Weeks table. I have update the schema above to show the full key. The PK described for the Headers is (and was) the full key.
Screen shot of some rows from the Weeks table:
Stats from the Weeks table:
Some stats from Headers table. There seems to be an histogram record for about every 5-10 days for the entire history in the table (3 years).