nimsearch VS rum

You are welcome. Thanks are too rarely offered. :-)

You may also be interested in word stemming ( such as used by snowball stemmer in https://github.com/c-blake/nimsearch ) or other NLP techniques, but I don't know how internationalized/multi-lingual that stuff is, but conceptually you might want "series of stemmed words" to be the content fragments of interest.

Similarity scores have many applications. Weights on graph of cancelled downloads ranked by size might be one. :)

Of course, for your specific "truncation" problem, you might also be able to just do an edit distance against the much smaller filenames and compare data prefixes in files or use a SHA256 of a content-based first slice. ( There are edit distance algos in Nim in https://github.com/c-blake/cligen/blob/master/cligen/textUt.... as well as in https://github.com/c-blake/suggest ).

Or, you could do a little program like ndup/sh/ndup to create a "mirrored file tree" of such content-based slices then you could use any true duplicate-file finder (like https://github.com/c-blake/bu/blob/main/dups.nim) on the little signature system to identify duplicates and go from path suffixes in those clusters back to the main filesystem. Of course, a single KV store within one or two files would be more efficient than thousands of tiny files. There are many possibilities.

It's all in the Nim programming language, but if you prefer reading code or running diffs then you might get a vague sense of (some) low level nuts & bolts from: https://github.com/c-blake/nimsearch

the rum index has worked well for us on roughly 1TB of pdfs. written by postgrespro, same folks who wrote core text search and json indexing. not sure why rum not in core. we have no problems.

   https://github.com/postgrespro/rum

If you need ranking, and you have the possibility to install PostgreSQL extensions, then you can consider an extension providing RUM indexes: https://github.com/postgrespro/rum. Otherwise, you'll have to use an "external" FTS engine like ElasticSearch.

>Reduce the memory usage of prepared queries

Yes query plan reuse like every other db, this still blows me away PG replans every time unless you explicitly prepare and that's still per connection.

Better full-text scoring is one for me that's missing in that list, TF/IDF or BM25 please see: https://github.com/postgrespro/rum

for postgres, i highly recommend the rum index over the core fts. rum is written by postgrespro, who also wrote core fts and json indexing in pg.

    https://github.com/postgrespro/rum

My experience with Postgres FTS (did a comparison with Elastic a couple years back), is that filtering works fine and is speedy enough, but ranking crumbles when the resulting set is large.

If you have a large-ish data set with lots of similar data (4M addresses and location names was the test case), Postgres FTS just doesn't perform.

There is no index that helps scoring results. You would have to install an extension like RUM index (https://github.com/postgrespro/rum) to improve this, which may or may not be an option (often not if you use managed databases).

If you want a best of both worlds, one could investigate this extensions (again, often not an option for managed databases): https://github.com/matthewfranglen/postgres-elasticsearch-fd...

Either way, writing something that indexes your postgres database into elastic/opensearch is a one time investment that usually pays off in the long run.

Mandatory mention of the RUM extension (https://github.com/postgrespro/rum) if this caught your eye. Lots of tutorials and conference presentations out there showcasing the advantages in terms of ranking, timestamps...

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.