rum VS pg_search

My experience has been that sorting by relevance ranking is quite expensive. I looked into this a bit and found https://github.com/postgrespro/rum (and some earlier slide decks about it) that explains why the GIN index type can't support searching and ranking itself (meaning you need to do heap scans for ranking). This is especially problematic if your users routinely do searches that match a lot of documents and you only want to show the top X results.

You might be just fine adding an unindexed tsvector column, since you've already filtered down the results.

The GIN indexes for FTS don't really work in conjunction with other indices, which is why https://github.com/postgrespro/rum exists. Luckily, it sounds like you can use your existing indices to filter and let postgres scan for matches on the tsvector.

We have been bitten by the same behavior. I gave a talk with a friend about this exact topic (diagnosing GIN pending list updates) at PGCon 2019 in Ottawa[1][2].

What you need to know is that the pending list will be merged with the main b-tree during several operations. Only one of them is so extremely critical for your insert performance - that is during actual insert. Both vacuum and autovacuum (including autovacuum analyze but not direct analyze) will merge the pending list. So frequent autovacuums are the first thing you should tune. Merging on insert happens when you exceed the gin_pending_list_limit. In all cases it is also interesting to know which memory parameter is used to rebuild the index as that inpacts how long it will take: work_mem (when triggered on insert), autovacuum_work_mem (when triggered during autovauum) and maintainance_work_mem (triggered by a call to gin_clean_pending_list()) define how much memory can be used for the rebuild.

What you can do is:

- tune the size of the pending list (like you did)

- make sure vacuum runs frequently

- if you have a bulk insert heavy workload (ie. nightly imports), drop the index and create it after inserting rows (not always makes sense business wise, depends on your app)

- disable fastupdate, you pay a higher cost per insert but remove the fluctuctuation when the merge needs to happen

The first thing was done in the article. However I believe the author still relies on the list being merged on insert. If vacuums were tuned agressively along with the limit (vacuums can be tuned per table). Then the list would be merged out of bound of ongoing inserts.

I also had the pleasure of speaking with one main authors of GIN indexes (Oleg Bartunov) during the mentioned PGCon. He gave probably the best solution and informed me to "just use RUM indexes". RUM[3] indexes are like GIN indexes, without the pending list and with faster ranking, faster phrase searches and faster timestamp based ordering. It is however out of the main postgresql release so it might be hard to get it running if you don't control the extensions that are loaded to your Postgres instance.

[1] - wideo https://www.youtube.com/watch?v=Brt41xnMZqo&t=1s

[2] - slides https://www.pgcon.org/2019/schedule/attachments/541_Let's%20...

[3] - https://github.com/postgrespro/rum

I suggest to have a look at https://github.com/postgrespro/rum if you haven’t yet. It solves the issue of slow ranking in PostgreSQL FTS.

You can take a look at pg_search if you’re using Postgres

This was a really good read, thanks. I've got into the habit of jumping straight to PgSearch but could definitely apply this approach to some existing projects.

Cleaner, more performant database queries: Definitely don't just leave your query sitting in the controller! For production use cases, you'd want to consider an option like pg_search

If you are using Rails with Postgres you can use pg_search gem to build the named scopes to take advantage of full text search.

https://github.com/Casecommons/pg_search

Hey guys. Looking to release an app for mobile that will be using a rails API. The app will heavily rely on search. I know the go-to is to use elasticsearch but wanted to see if there was enough user demand for the MVP before shelling out $50/mo for the heroku add on. In the mean time I've been using pg_search. From the eye test it's performing okay but will be adding a table that houses over 350K records. With this in mind I was wondering if you all had any tips for increasing the overall speed for search from the model and controller level. Also should note that I'm open to any other free search gems if they deem bette fit.

There are two basic search configurations with pg_search, a Single Model search scope or a multi Model configuration. In my case I am only using the Single Model configuration, but you can read more about multi-search in the documentation.