Thanks for your suggestions, very useful. See comments inline:
On Wed, Jun 25, 2014 at 3:48 AM, Niels Kristian Schjødt < nielskristian@xxxxxxxxxxxxx> wrote: Hi,
I’m running a search engine for cars. It’s backed by a postgresql 9.3 installation.
Now I’m unsure about the best approach/strategy on doing index optimization for the fronted search.
The problem:
The table containing the cars holds a around 1,5 million rows. People that searches for cars needs different criteria to search by. Some search by brand/model, some by year, some by mileage, some by price and some by special equipment etc. etc. - and often they combine a whole bunch of criteria together. Of cause some, like brand/mode and price, are used more frequently than others. In total we offer: 9 category criteria like brand/model or body type, plus 5 numeric criteria like price or mileage, plus 12 boolean criteria like equipment. Lastly people can order the results by different columns (year, price, mileage and a score we create about the cars). By default we order by our own generated score.
What I’ve done so far:
I have analyzed the usage of the criteria “lightly”, and created a few indexes (10). Among those, are e.g. indexes on price, mileage and a combined index on brand/model. Since we are only interested in showing results for cars which is actually for sale, the indexes are made as partial indexes on a sales state column.
Questions:
1. How would you go about analyzing and determining what columns should be indexed, and how?
mainly frequency of access. 2. What is the best strategy when optimizing indexes for searches happening on 20 + columns, where the use and the combinations varies a lot? (To just index everything, to index some of the columns, to do combined indexes, to only do single column indexes etc. etc.)
don't make 20 indexes. consider installing pg_trgm (for optimized LIKE searching) or hstore (for optmized key value searching) and then using GIST/GIN for multiple attribute search. with 9.4 we have another fancy technique to explore: jsonb searching via GIST/GIN.
Interesting, do you have any good resources on this approach? 3. I expect that it does not make sense to index all columns?
well, maybe. if you only ever search one column at a time, then it might make sense. but if you need to search arbitrary criteria and frequently combine a large number, then no -- particularly if your dataset is very large and individual criteria are not very selective.
So, to just clarify: I’m often combining a large number of search criteria and the individual criteria is often not very selective, in that case, are you arguing for or against indexing all columns? :-) 4. I expect it does not make sense to index boolean columns?
in general, no. an important exception is if you are only interested in true or false and the number of records that have that interesting value is tiny relative to the size of the table. in that case, a partial index can be used for massive optimization.
Thanks, hadn’t been thinking about using partial indexes here as an option. 5. Is it better to do a combined index on 5 frequently used columns rather than having individual indexes on each of them?
Only if you search those 5 columns together a significant portion of the time. 6. Would it be a goof idea to have all indexes sorted by my default sorting?
index order rarely matters. if you always search values backwards and the table is very large you may want to consider it. unfortunately this often doesn't work for composite indexes so sometimes we must explore the old school technique of reversing the value. 7. Do you have so experiences with other approaches that could greatly improve performance (e.g. forcing indexes to stay in memory etc.)?
as noted above, fancy indexing is the first place to look. start with pg_trgm (for like optmization), hstore, and the new json stuff. the big limitation you will hit is that that most index strategies, at least fo the prepackaged stuff will support '=', or partial string (particularly with pg_trgm like), but not > or <: for range operations you have to post process the search or try to work the index from another angle. merlin
|
