I have a long-running SQL Server 2005 query that I have been hoping to optimize. When I look at the actual execution plan, it says a Clustered Index Seek has 66% of the cost.
Problem String concatenation is slowing down a query: date(extract(YEAR FROM m.taken)||\'-1-1\') d1, date(extract(YEAR FROM m.taken)||\'-1-31\') d2
I\'m getting more and more confused as I try to distinguish from the ambiguities of these terms.I have a query that is taking longer than necessary simply because I cannot get the key on on table to w
I am wondering what\'s the best type for a price field in SQL Server for a shop-like structure? Looking at this overview we have data types called money, smallmoney, then we have decimal/numeric and
I know i am writing query\'s wrong and when we get a lot of traffic, our database gets hit HARD and the page slows to a grind...
Ok my Giant friends once again I seek a little space in your shoulders :P Here is the issue, I have a python script that is fixing some database issues but it is taking way too long, the main update
My MySQL is not strong, so please forgive any rookie mistakes.Short version: SELECT locId,count,avg FROM destAgg_geo is significantly slower than SELECT * from destAgg_geo
I\'m wondering if someone can explain how the IN calculates?Well, u开发者_StackOverflow社区ltimately I\'m trying to find out why this query is slow and how to optimize it.I waited over 3 minutes and w
I need to select a top row for each category from a known set (somewhat similar to this question). The problem is, how to make this query efficient on the large number of rows.
I have an sql query i am struggling to optimise. It basically is used to pull back products for a shopping cart. The products each have tags attached using a many to many table product_tag and also i
加载中,请稍侯......