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One way to solve the problem is to generate an auxiliary table with all possible points in time during the covered period, use a subquery to count the number of active sessions during each such point in time, create a derived table/CTE from the result table, and then group the rows from the derived table by application, requesting the maximum count of concurrent sessions for each application Such a solution is extremely inefficient Assuming you create the optimal index for it one on (app, starttime, endtime) the total number of rows you end up scanning just in the leaf level of the index is huge It s at minimum the number of rows in the auxiliary table multiplied by the average number of active sessions at any point in time .

Before we move on, let s review the steps that we have taken so far. We began by choosing a suitable environment for our application. Next, we developed an analysis architecture that satisfied our case study requirements. Then, we added detail to those components during and added additional design level components necessary to make our application complete. Table 6.3 lists each component in our system and its purpose.

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To give you a sense of the enormousness of the task, if you need to perform the calculations for a month s worth of activity, the number of rows in the auxiliary table will be: 31 (days) 24 (hours) 60 (minutes) 60 (seconds) 300 (units within a second) Now multiply the result of this calculation by the average number of active sessions at any given point in time (let s use 20 as an example), and you get 16,070,400,000 That s assuming that you use the DATETIME data type If you use the DATETIME2 data type, which has an accuracy of 100 nanoseconds, the number is 26,784,000,000,000 Of course there s room for optimization .

There are periods in which the number of concurrent sessions doesn t change, so why calculate the counts for those The count changes only when a new session starts (increased by 1) or an existing session ends (decreased by 1) Furthermore, because a start of a session increases the count and an end of a session decreases it, a start event of one of the sessions is bound to be the point at which you will find the maximum you re looking for In short, you can simply use as your auxiliary table a derived table or CTE that returns all start times of sessions per application From there, all you need to do is follow logic similar to that mentioned earlier Here s the optimized set-based solution:.

SELECT app, MAX(concurrent) AS mx FROM (SELECT app, (SELECT COUNT(*) FROM dbo.Sessions AS S WHERE T.app = S.app AND T.ts >= S.starttime AND T.ts < S.endtime) AS concurrent FROM (SELECT app, starttime AS ts FROM dbo.Sessions) AS T) AS C GROUP BY app;

This generates the following output:

Communicates with partner web service. Locates components and services. Caches application menu data. Controller servlet for application. Performs XSLT transformation and rendering

app ---------app1 app2 mx ----------4 3

Notice that instead of using a BETWEEN predicate to determine whether a session was active at a certain point in time (ts), I used ts >= starttime AND ts < endtime . If a session ends at the ts point in time, I don t want to consider it as active . The execution plan for this query is shown in Figure 8-3 . Note that I captured this plan after populating the Sales table with a more realistic volume of data . With very little data you might get a different plan .

First, the index I created on (app, starttime, endtime) is scanned The number of rows returned will be the same as the number of rows in the table For each app, starttime (call it ts) returned, a Nested Loops operator initiates activity that calculates the count of active sessions (by a seek within the index, followed by a partial scan to count active sessions) The number of pages read in each iteration of the Nested Loops operator is the number of levels in the index plus at least the number of pages consumed by the rows representing active sessions To make my point, I ll focus on the number of rows scanned at the leaf level because this number varies based on active sessions Of course, to do adequate performance estimations, you should take page counts (logical reads) as well as many other factors into consideration .

When evaluating your options and deciding on the best wireless technology to adopt, you face some difficult choices. A wide variety of products that conform to different wireless standards, such as 802.11a, 802.11b, 802.11g, and Bluetooth, are readily available. To make an educated network building decision, it s important to understand the relative pros and cons of each of these technologies.

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