indexing - PostgreSQL: GIN or GiST indexes?

Question

From what information I could find, they both solve the same problems - more esoteric operations like array containment and intersection (&&, @>, <@, etc). However I would be interested in advice about when to use one or the other (or neither possibly).
The PostgreSQL documentation has some information about this:

• GIN index lookups are about three times faster than GiST
• GIN indexes take about three times longer to build than GiST
• GIN indexes are about ten times slower to update than GiST
• GIN indexes are two-to-three times larger than GiST

However I would be particularly interested to know if there is a performance impact when the memory to index size ration starts getting small (ie. the index size becomes much bigger than the available memory)? I've been told on the #postgresql IRC channel that GIN needs to keep all the index in memory, otherwise it won't be effective, because, unlike B-Tree, it doesn't know which part to read in from disk for a particular query? The question would be: is this true (because I've also been told the opposite of this)? Does GiST have the same restrictions? Are there other restrictions I should be aware of while using one of these indexing algorithms?

question from:https://stackoverflow.com/questions/21830/postgresql-gin-or-gist-indexes

Answer 1

First of all, do you need to use them for text search indexing? GIN and GiST are index specialized for some data types. If you need to index simple char or integer values then the normal B-Tree index is the best.
Anyway, PostgreSQL documentation has a chapter on GIST and one on GIN, where you can find more info.
And, last but not least, the best way to find which is best is to generate sample data (as much as you need to be a real scenario) and then create a GIST index, measuring how much time is needed to create the index, insert a new value, execute a sample query. Then drop the index and do the same with a GIN index. Compare the values and you will have the answer you need, based on your data.