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Database statistics is a subject near and dear to my heart.
This best practice option ensures your statistics stay up to date as much as possible.It’s a simple change that can have a big impact when implemented in highly transactional OLTP environments.Notice I said OLTP not OLAP, since data in an OLAP environment tends to not be as dynamic, so it’s rare to enable this in a data warehouse.While digging into this, I realized that I was double dipping when I immediately updated statistics after I rebuild. This process was going to significantly reduce my maintenance windows!SELECT GETDATE() as [Date], CONVERT(VARCHAR(100), SERVERPROPERTY('Server Name')) as [Server], DB_NAME() as [DBName], as [Schema Name], OBJECT_NAME(i.object_id) as [Table Name], as [Index Name], STATS_DATE(i.object_id, index_id) as [Stats_Date], as [Row Count], si.rowmodctr as [Row Mod Ctr], cast(si.rowmodctr as Decimal(15,2))/as [Percent Change], 'UPDATE STATISTICS [' db_name() '].[' '].[' OBJECT_NAME(i.object_id) ']([' '])WITH SAMPLE 100 Percent' FROM sys.indexes i WITH(NOLOCK) INNER JOIN sys.sysindexes si WITH(NOLOCK) ON = INNER JOIN sys.tables t WITH(NOLOCK) ON i.object_id=t.object_id INNER JOIN sys.sysobjects so WITH(NOLOCK) ON t.object_id = INNER JOIN sys.schemas sch WITH(NOLOCK) ON = sch.schema_id WHERE OBJECTPROPERTY(t. Less than 65,000 rows table in a 20 terabyte database caused a noticeable performance issue to my users.