Craig R. McClanahan,
Iam really thank ful to u for the time u devoted to answer my
question.
But my problem still remains I can have gibs of RAM but still I
cant support the number of concurent users i want to support(in some cases
can go upto 1000) and the some results can be million records.
Regards,
Karan
>From: "Craig R. McClanahan" <[EMAIL PROTECTED]>
>Reply-To: A mailing list about Java Server Pages specification and
> reference <[EMAIL PROTECTED]>
>To: [EMAIL PROTECTED]
>Subject: Re: interesting one, 200 concurent users, large resultstes
>Date: Wed, 2 Aug 2000 15:35:36 -0700
>
>Karanjit Singh wrote:
>
> > Yes i understand that i have to bring only 100 records to client ,
>but
> > when the client wants next 100 records from where will I get them on the
> > server(i can find them only if i saved them somewhere on my server ),
>only
> > way is to requery the database and that is even more expensive in my
>case
> > becuse iam using 3 tier system, so everytime goin back to database
>server is
> > very expensive.
> > regards
> > karan
> >
>
>I had a similar requirement on one application, and followed this sort of
>an
>approach:
>
>* Run the middle tier on a system with gobs of real memory (for me it was
> a gigabyte, which was cheap relative to the value of the application).
>Devote
> lots of it to your JVM.
>
>* As records are accessed from the database, cache them in a Java
>collection
> of some sort that is stored as a servlet context attribute (i.e.
>application
>scope
> bean for JSP). For me, we had already designed JavaBeans that mapped to
> the underlying data so I cached the "bean" version rather than rows and
>columns
> from a ResultSet. This also avoided any need to keep database cursors
>open
> for longer than necessary.
>
>* To make sure that the cache is up-to-date, run a background thread that
>is
> checking for cached rows that have been changed or deleted, or that have
>been
> in the cache to long (the data I was dealing with was time-sensitive,
>and has an
> "interesting" lifetime measured in minutes to hours).
>
>* When doing a query, use the normal database facilities (some of my
>"where"
> clauses got quite intricate) to make sure that the results are always
>accurate
> no matter whether the actual data is cached or not). BUT, retrieve only
>the
> primary keys of the result records. Now, of that set of keys, some of
>the
>records
> are already in the cache and some aren't. For the ones that are, just
>add
>references
> to them to an ArrayList or Vector. For the ones that aren't, go read
>them from
>the
> database (and cache them) as you add them to the list.
>
>* As you do update transactions, be sure that you update the cached version
>of the
> information as well as the underlying database.
>
>The net effect of all this was that the cache contained the data that has
>been
>retrieved or modified recently, which tended to be accessed frequently for
>a while
>and then not at all later. This works pretty well as long as you can
>afford enough
>memory to keep this "recently accessed working set" of data in the cache,
>which was
>true for me. It also let me scale to multiple middle tier servers that
>accessed
>the same back-end database -- primarily because I did not require
>up-to-the-second
>accuracy of the search results, so I could ignore the issue of
>synchronizing the
>various caches.
>
>I hope some of these ideas might be useful in your app design.
>
>Craig McClanahan
>
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