After setting up change data capture (CDC), there are a few configuration options that allow for optimal performance based upon applications load. Using the default values, the most number of transactions that can be grabbed every 5 seconds is 5000. Remember, a transaction is not the same as a database row. One transaction can update “n” number of rows. In order to determine latency for you CDC solution, you will need to query a DMV view: [cc lang=”sql”] select latency, * from sys.dm_cdc_log_scan_sessions [/cc] The latency will be shown in seconds, if it is getting too high, start increasing maxtrans, maxscans, and decrease the polling interval. polling interval – (default 5 seconds) the amount of time to wait between log scans maxtrans – (default 500) the number of transactions to grab with each scan maxscans – (default 10) the number of scans performed between each polling interval In order to change these settings, use the system stored procedure (sys.sp_cdc_change_job) after changing the database which contains your capture instance. The CDC job needs to be restarted after making these changes. [cc lang=”sql”] — Change to the database that contains your capture instance use YourDatabaseHere go exec sys.sp_cdc_change_job @job_type = ‘capture’ ,@maxtrans = 501 ,@maxscans = 10 ,@continuous = 1 ,@pollinginterval = 5 [/cc] Changing the latency between the cleanup is done using the same stored procedure: [cc lang=”sql”] exec sys.sp_cdc_change_job @job_type = ‘cleanup’ ,@retention = 4320 — Number of minutes to retain (72 hours) ,@threshold = 5000 [/cc] To view the […]
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The following short code will return the date and time for the very end of the month passed in. It works by first getting the beginning of the month for the date passed in, adding one month to that date, then subtracting 3 milliseconds. This gives the latest possible date for the end of the month as the DateTime data type has a granularity of 3 milliseconds. [cc lang=”sql”] DECLARE @DateIn datetime SET @DateIn = GETDATE() SELECT DATEADD(ms,-3,DATEADD(mm,0,DATEADD(mm,DATEDIFF(mm,0,@DateIn)+1,0))) [/cc]
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Possibly the easiest way to calculate the beginning of the month, is to use the DateAdd function and add a date difference of nothing to the month: [cc lang=”sql”] DECLARE @Date datetime SET @Date = GETDATE() SELECT DATEADD(mm,DATEDIFF(mm,0,GETDATE()),0) [/cc]
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Creating an index on a table variable can be done implicitly within the declaration of the table variable by defining a primary key and creating unique constraints. The primary key will represent a clustered index, while the unique constraint a non clustered index. [cc lang=”sql”] DECLARE @Users TABLE ( UserID INT PRIMARY KEY, UserName varchar(50), UNIQUE (UserName) ) [/cc] The drawback is that the indexes (or constraints) need to be unique. One potential way to circumvent this however, is to create a composite unique constraint: [cc lang=”sql”] DECLARE @Users TABLE ( UserID INT PRIMARY KEY, UserName varchar(50), FirstName varchar(50), UNIQUE (UserName,UserID) ) [/cc] You can also create the equivalent of a clustered index. To do so, just add the clustered reserved word. [cc lang=”sql”] DECLARE @Users TABLE ( UserID INT PRIMARY KEY, UserName varchar(50), FirstName varchar(50), UNIQUE CLUSTERED (UserName,UserID) ) [/cc] Generally, temp tables perform better in situations where an index is needed. The downfall to temp tables is that they will frequently cause recompilation. This was more of an issue with SQL 2000 when compilation was performed at the procedure level instead of the statement level. SQL 2005 and above perform compilation at the statement level so if only one statement utilizes a temp table then that statement is the only one that gets recompiled. Contrary to popular belief, table variables can and do write to disk.
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One of the most valuable assets of a temp table (#temp) is the ability to add either a clustered or non clustered index. Additionally, #temp tables allow for the auto-generated statistics to be created against them. This can help the optimizer when determining cardinality. Below is an example of creating both a clustered and non-clustered index on a temp table. [cc lang=”sql”] CREATE TABLE #Users ( ID int IDENTITY(1,1), UserID int, UserName varchar(50) ) INSERT INTO #Users ( UserID, UserName ) SELECT UserID = u.UserID ,UserName = u.UserName FROM dbo.Users u CREATE CLUSTERED INDEX IDX_C_Users_UserID ON #Users(UserID) CREATE INDEX IDX_Users_UserName ON #Users(UserName) [/cc] Even though you can implicitly create a clustered index on a table variable (@table) by defining a primary key or unique constraint, it is generally more efficient to use a temp table. The downside is recompilation, however this can be suppressed with the use of (Keep Plan) or (Keep Fixed Plan) hints.
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