pg_hint_plan VS rum

Okay so it isn't entirely clear to me, can the pg_hint_plan extension (linked in the OP) do the simple thing where we specify, for each table, which index to use?

I can't find it here

https://github.com/ossc-db/pg_hint_plan/blob/master/docs/hin...

Because, the mssql WITH(INDEX()) is simple and intuitive. This hint table stuff seems complicated, and it's unclear to me if they can do the simple thing

pg_hint_plan —— Give PostgreSQL ability to manually force some decisions in execution plans. https://github.com/ossc-db/pg_hint_plan

I really wish the PostgreSQL core team would acknowledge that their stance on that hurts more than helps. Even Oracle with decades of engineering behind it doesn't get execution plans correct 100% of the time and provides a way to tune query execution via hints.

However, TIL that https://github.com/ossc-db/pg_hint_plan exists so that will probably become a standard thing I deploy.

With PostgreSQL, the extension to do it, pg_hint_plan is really good, but not widely used because not included in the core, not even in contrib. The consequence is that people install it only when needing it, without the time to learn hot to hint properly, may think that "my hint is not used" and give up.

cat > Dockerfile <<'DOCKERFILE' # install pg_hint_plan from rpm FROM docker.io/postgres:14 ADD https://github.com/ossc-db/pg_hint_plan/releases/download/REL14_1_4_0/pg_hint_plan14-1.4-1.el8.x86_64.rpm . RUN apt-get update -y ; apt-get install -y alien wget ; alien ./pg_hint_plan*.rpm ; dpkg -i pg-hint-plan*.deb # copy the minimal files to a postgres image FROM docker.io/postgres:14 COPY --from=0 /usr/pgsql-14/share/extension/pg_hint_plan.control /usr/share/postgresql/14/extension COPY --from=0 /usr/pgsql-14/share/extension/pg_hint_plan--1.4.sql /usr/share/postgresql/14/extension COPY --from=0 /usr/pgsql-14/lib/pg_hint_plan.so /usr/pgsql-14/lib/pg_hint_plan.so /usr/lib/postgresql/14/lib ENV PGPASSWORD=postgres CMD ["postgres","-c","shared_preload_libraries=pg_hint_plan,pg_stat_statements"] DOCKERFILE docker build -t pachot/pg_hint_plan --platform=linux/amd64 . docker push pachot/pg_hint_plan

there is a maintained index hint extension: https://github.com/ossc-db/pg_hint_plan - at least as far as 13 (and likely 14).

if we're going to talk about index functionality that would be good and effective for Postgres, an index across all partitioned tables (both normal and unique) would be very much welcomed.

the problem is finding someone to maintain it for life.

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.