qr code generator in vb.net Top Waits in Visual Studio .NET

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Table 3-2. Top Waits
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wait_type IO_COMPLETION PAGEIOLATCH_SH ASYNC_NETWORK_IO wait_time_s pct 466.24 365.08 358.21 running_pct
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23.98 23.98 18.78 42.76 18.42 61.18 10.22 71.40 10.02 81.42 5.01 2.39 2.26 86.43 88.83 91.09
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LOGMGR_RESERVE_APPEND 198.75 LOGBUFFER WRITELOG PAGEIOLATCH_EX ASYNC_IO_COMPLETION 194.92 97.40 46.54 43.99
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This query uses techniques to calculate running aggregates, which I'll explain later in the book. Remember, focus for now on the concepts rather than on the techniques used to achieve them. This query returns the top waits that accumulate to 90 percent of the waits in the system, after filtering out irrelevant wait types. Of course, you can adjust the threshold and filter out other irrelevant waits to your analysis. If you want to see at least n rows in the output (say n = 10), add the expression OR W1.rn <= 10 to the HAVING clause. With each wait type, the query returns the following: the total
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wait time in seconds that processes waited on that wait type since the system was last restarted or the counters were cleared; the percentage of the wait time of this type out of the total; and the running percentage from the top-most wait type until the current one.
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Note
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In the sys.dm_os_wait_stats DMV, wait_time_ms represents the total wait time of all processes that waited on this type, even if multiple processes were waiting concurrently. Still, these numbers would typically give you a good sense of the main problem areas in the system.
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Examining the top waits shown in Table 3-2, you can identify three problem areas: I/O, network, and the transaction log. With this information in hand, you are ready for the next step. I also find it handy to collect wait information in a table and update it at regular intervals (for example, once an hour). By doing this, you can analyze the distribution of waits during the day and identify peak periods. Run the following code to create the WaitStats table: USE Performance; GO IF OBJECT_ID('dbo.WaitStats') IS NOT NULL DROP TABLE dbo.WaitStats; GO SELECT GETDATE() AS dt, wait_type, waiting_tasks_count, wait_time_ms, max_wait_time_ms, signal_wait_time_ms INTO dbo.WaitStats FROM sys.dm_os_wait_stats WHERE 1 = 2; ALTER TABLE dbo.WaitStats ADD CONSTRAINT PK_WaitStats PRIMARY KEY(dt, wait_type); CREATE INDEX idx_type_dt ON dbo.WaitStats(wait_type, dt);
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Define a job that runs on regular intervals and uses the following code to load the current data from the DMV: INSERT INTO Performance.dbo.WaitStats SELECT GETDATE(), wait_type, waiting_tasks_count, wait_time_ms, max_wait_time_ms, signal_wait_time_ms FROM sys.dm_os_wait_stats;
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Remember that the wait information in the DMV is cumulative. To get the waits that took place within each interval, you need to apply a self-join between two instances of the tableone representing the
current samples, and the other representing the previous samples. The join condition will match to each current row the row representing the previous sampling for the same wait type. Then you can subtract the cumulative wait time of the previous sampling from the current, thus producing the wait time during the interval. The following code creates the fn_interval_waits function, which implements this logic: IF OBJECT_ID('dbo.fn_interval_waits') IS NOT NULL DROP FUNCTION dbo.fn_interval_waits; GO CREATE FUNCTION dbo.fn_interval_waits (@fromdt AS DATETIME, @todt AS DATETIME) RETURNS TABLE AS RETURN WITH Waits AS ( SELECT dt, wait_type, wait_time_ms, ROW_NUMBER() OVER(PARTITION BY wait_type ORDER BY dt) AS rn FROM dbo.WaitStats WHERE dt >= @fromdt AND dt < @todt + 1 ) SELECT Prv.wait_type, Prv.dt AS start_time, CAST((Cur.wait_time_ms - Prv.wait_time_ms) / 1000. AS DECIMAL(12, 2)) AS interval_wait_s FROM Waits AS Cur JOIN Waits AS Prv ON Cur.wait_type = Prv.wait_type AND Cur.rn = Prv.rn + 1 AND Prv.dt <= @todt; GO
The function accepts the boundaries of a period that you want to analyze. For example, the following query returns the interval waits for the period '20060212' through '20060215' (inclusive), sorted by the totals for each wait type in descending order, wait type, and start time: SELECT wait_type, start_time, interval_wait_s FROM dbo.fn_interval_waits('20060212', '20060215') AS F ORDER BY SUM(interval_wait_s) OVER(PARTITION BY wait_type) DESC, wait_type, start_time;
I find Microsoft Office Excel pivot tables or Analysis Services cubes extremely handy in analyzing such information graphically. These tools allow you to easily see the distribution of waits graphically. For example, suppose you want to analyze the waits over the period '20060212' through '20060215' using Excel pivot tables. Prepare the following VIntervalWaits view, which will be used as the external source data for the pivot table: IF OBJECT_ID('dbo.VIntervalWaits') IS NOT NULL DROP VIEW dbo.VIntervalWaits; GO CREATE VIEW dbo.VIntervalWaits
AS SELECT wait_type, start_time, interval_wait_s FROM dbo.fn_interval_waits('20060212', '20060215') AS F; GO
Create a pivot table and pivot chart in Excel, and specify the VIntervalWaits view as the pivot table's external source data. Figure 3-1 shows what the pivot table looks like with my sample data, after filtering only the top waits.
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