query-optimization

In a typical many-many arrangement like this... Movies Actors Movies_Actors ------ ------ ------------- movie_ID actor_ID FK_movie_ID title name FK_actor_ID ... how should the association table ( 'Movies_Actors' ) be indexed for optimal read speed? I usually see this done only with the composite primary key in the association table, like so: CREATE TABLE Movies_Actors ( FK_movie_ID INTEGER, FK_actor_ID INTEGER, PRIMARY KEY (FK_movie_ID, FK_actor_ID) ) However, this seems like the index will only be useful when searching for both movie_ID and actor_ID (although I'm not certain on whether a

How can I compare two queries X and Y and say that X is better than Y, when they both take almost the same time in small cases scenarios? The problem is that I have two queries that are supposed to run on a very big database, so run and evaluate is not quite an option. Therefore, we created a small database to perform some tests. Evaluating which query is better is a problem, since on our test base, they run in almost the same time (about 5 minutes). Besides the time taken to return, what is another way to measure how good a query is? SET STATISTICS IO ON SET STATISTICS TIME ON Run the queries

I have a database with hundreds of awkwardly named tables in it (CG001T, GH066L, etc), and I have views on every one with its "friendly" name (the view "CUSTOMERS" is "SELECT * FROM GG120T", for example). I want to add "WITH SCHEMABINDING" to my views so that I can have some of the advantages associated with it, like being able to index the view, since a handful of views have computed columns that are expensive to compute on the fly. Are there downsides to SCHEMABINDING these views? I've found some articles that vaguely allude to the downsides, but never go into them in detail. I know that

I have been working in a web project(asp.net) for around six months. The final product is about to go live. The project uses SQL Server as the database. We have done performance testing with some large volumes of data, results show that performance degrades when data becomes too large, say 2 million rows (timeout issues, delayed reponses, etc). At first we were using fully normailized database, but now we made it partially normalized due to performance issues (to reduce joins). First of all, is it the right decision? Plus what are the possible solutions when data size becomes very large, as

In output of explain command I found two terms 'Seq Scan' and 'Bitmap heap Scan'. Can somebody tell me what is the difference between these two types of scan? (I am using PostgreSql) http://www.postgresql.org/docs/8.2/static/using-explain.html Basically, a sequential scan is going to the actual rows, and start reading from row 1, and continue until the query is satisfied (this may not be the entire table, e.g., in the case of limit) Bitmap heap scan means that PostgreSQL has found a small subset of rows to fetch (e.g., from an index), and is going to fetch only those rows. This will of course

I am developing a web application that can support threaded comments. I need the ability to rearrange the comments based on the number of votes received. (Identical to how threaded comments work in reddit ) I would love to hear the inputs from the SO community on how to do it. How should I design the comments table? Here is the structure I am using now: Comment id parent_post parent_comment author points What changes should be done to this structure? How should I get the details from this table to display them in the correct manner? (Implementation in any language is welcome. I just want to

I have a table I'm doing an ORDER BY on before a LIMIT and OFFSET in order to paginate. Adding an index on the ORDER BY column makes a massive difference to performance (when used in combination with a small LIMIT). On a 500,000 row table, I saw a 10,000x improvement adding the index, as long as there was a small LIMIT. However, the index has no impact for high OFFSETs (i.e. later pages in my pagination). This is understandable: a b-tree index makes it easy to iterate in order from the beginning but not to find the nth item. It seems that what would help is a counted b-tree index , but I'm not