XOxojoc·Dec 17, 2021Submission
Postgrespro/rum: RUM access method – inverted index with additional information
2 points · Open discussionpostgrespro
rum
C2016–2024
postgrespro / rum
rum
RUM access method - inverted index with additional information in posting lists
13Distinct people
33Mentions
1Explicit recommenders
849GitHub stars
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What it is
RUM access method - inverted index with additional information in posting lists
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
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.…
johnthescott used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 7:02:50 PM UTC.
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Mentions over time
HN mentions by year33 dated mentions
Observed mentions: 2016-10-02 14:16:46 UTC to 2024-08-07 01:25:25 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 6 | 0 | 6 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 7 | 1 | 6 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 2 | 0 | 2 |
What Hacker News says
7 sourcesYou 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. The GIN tsvector indices are quite expensive to build, so don't add one if postgres can't make use of it!
In Postgres Full-Text Search: A search engine in a databaseThat why this really needs to get merged:
https://github.com/postgrespro/rum
In Postgres Full-Text Search: A search engine in a databaseKeep wondering if RUM Indexes [1] will ever get merged for faster and better ranking (TF/IDF). Really would make PG a much more complete text search engine.
https://github.com/postgrespro/rum
In Postgres Full-Text Search: A search engine in a databaseMy 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.
Edit: if any of the Crunchy Data people are reading this: support for RUM indexes would be super cool to have in your managed service.
In Postgres Full-Text Search: A search engine in a databaseWe 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 maint
Read the rest
ainance_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 (eg. 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 fluctuation 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 aggressively 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] - video 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.
In Show HN: Full text search Project Gutenberg (60m paragraphs)