I have a software who does a heavy processing based on some files. I have to query some tables in SQL Server in the process and this is killing the DB and the application performance. (other applications use the same tables).
After optimizing queries and code, getting better results but not enough. After research I reached the solution: Caching some query results. My idea is cache one specific table (identified as the overhead) rows that the file being process need.
I was think in using AppCache Fabric (I'm on MS stack), made some tests it have a large memory usage for small objects ( appcache service have ~350MB of ram usage without objects). But I need to make some queries in these result table (like search for lastname, ssn, birthdate etc.)
My second option is MongoDb as a cache store. I've research about this and most of people I read recommend using memcached or Redis, but I'm using Windows servers and they're not supported officialy.
Using mongo as cache store in this case it is a good approach? Or AppFabric Caching + tag search is better?
It is hard to tell what is better because we don't know enough about your bottlenecks. A lot is depending on quality of the data you're discussing. If the data is very static and is not called constantly but to compile the data set is time-consuming, the good solution might be to use the materialized view. If this data is frequently called than you better caching it on some server (e.g. app fabric). There are many techniques and possibilities. But you really need to think of the network traffic, demand, size, etc, etc. And it is hard to answer this here without knowing all the details. Looks like you are on the right way but may be all you need is just a parametrized query. Hard to tell. But I would add Materialized view into the roster that you just posted. May be all you need is to build this view from all the data you need and just access its contents.