ASP.NET PDF Viewer using C#, VB/NET

The two background worker samples we ve shown so far run at full throttle. In other words, the computations run on the background threads as active loops, and their reactive behavior is limited to flags that check for cancellation. In reality, background threads often have to do different kinds of work, either by responding to completing asynchronous I/O requests, by processing messages, by sleeping, or by waiting to acquire shared resources. Fortunately, F# comes with a powerful set of techniques for structuring asynchronous programs in a natural way. These are called asynchronous workflows. In the next three sections, we cover how to use asynchronous workflows to structure asynchronous and message-processing tasks in ways that preserve the essential logical structure of your code.

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I gave the database instance in this case an impossible task; the very act of having 250 users, most executing PL/SQL, plus the sort they were all requesting, just did not fit into the 256MB of RAM I had targeted It simply could not be done Each session therefore used as little memory as possible, but had to allocate as much memory as it needed By the time I finished this test, 250 active sessions were using a total of 304MB of PGA memory as little as they could You should, however, consider what that output would look like under a manual memory management situation Suppose the SORT_AREA_SIZE had been set to 5MB.

The math is very straightforward: each session would be able to perform the sort in RAM (or virtual memory as the machine ran out of real RAM), and thus would consume 6 to 7MB of RAM per session (the amount used without sorting to disk in the previous single-user case) The memory use would look something like this:.

One of the most intuitive asynchronous tasks is fetching a web page; we all use web browsers that can fetch multiple pages simultaneously. In the samples in 2 we showed how to

ops$tkyte%ORA11GR2> column total_pga format 9,999 ops$tkyte%ORA11GR2> with data(users) 2 as 3 (select 1 users from dual 4 union all 5 select users+25 from data where users+25 <= 275) 6 select users, 7 my_pga, 7*users total_pga 7 from data 8 order by users 9 / USERS MY_PGA TOTAL_PGA ---------- ---------- --------1 7 7 26 7 182 51 7 357 76 7 532 101 7 707 126 7 882 151 7 1,057 176 7 1,232 201 7 1,407 226 7 1,582 251 7 1,757 11 rows selected.

Note This query uses a new technique recursive subquery factoring that is available only in Oracle

fetch pages synchronously. This is useful for many purposes, but browsers and high-performance web crawlers will have tens or thousands of connections in flight at once. The type Microsoft.FSharp.Control.Async<'a> lies at the heart of F# asynchronous workflows. A value of type Async<'a> represents a program fragment that will generate a value of type 'a at some point in the future. Listing 13-5 shows how to use asynchronous workflows to fetch several web pages simultaneously. Listing 13-5. Fetching Three Web Pages Simultaneously open System.Net open System.IO open Microsoft.FSharp.Control.CommonExtensions let museums = ["MOMA", "http://moma.org/"; "British Museum", "http://www.thebritishmuseum.ac.uk/"; "Prado", "http://museoprado.mcu.es"] let fetchAsync(nm,url:string) = async { do printfn "Creating request for %s..." nm let req = WebRequest.Create(url) let! resp = req.GetResponseAsync()

Had I run this test (I have 2GB of real memory on this server and my SGA is 256MB), by the time I got to 250 users, the machine would have begun paging and swapping to the point where it would have been impossible to continue; at 500 users I would have allocated around 3,514MB of RAM! So, the DBA would probably not set the SORT_AREA_SIZE to 5MB on this system, but rather to about 0.5 MB, in an attempt to keep the maximum PGA usage at a bearable level at peak. At 500 users, I would have had about 500MB of PGA allocated, perhaps similar to what we observed with automatic memory management, but even when there were fewer users, we would still have written to temp rather than performing the sort in memory. Manual memory management represents a very predictable but suboptimal use of memory as the workload increases or decreases over time. Automatic PGA memory management was designed specifically to allow a small community of users to use as much RAM as possible when it was available, to back off on this allocation over time as the load increased, and increase the amount of RAM allocated for individual operations over time as the load decreased.