rum
Toshi
rum | Toshi | |
---|---|---|
11 | 12 | |
693 | 4,118 | |
0.7% | 0.4% | |
4.0 | 6.1 | |
4 months ago | 4 months ago | |
C | Rust | |
GNU General Public License v3.0 or later | MIT License |
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rum
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Code Search Is Hard
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
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Is it worth using Postgres' builtin full-text search or should I go straight to Elastic?
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.
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Features I'd Like in PostgreSQL
>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
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Ask HN: Books about full text search
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
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Postgres Full Text Search vs. the Rest
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.
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Postgres Full-Text Search: A Search Engine in a Database
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.
- Postgrespro/rum: RUM access method – inverted index with additional information
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Debugging random slow writes in PostgreSQL
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
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Show HN: Full text search Project Gutenberg (60m paragraphs)
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.
Toshi
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Tantivy 0.20 is released: Schemaless column store, Schemaless aggregations, Phrase prefix queries, Percentiles, and more...
I don't think you have an active project that addresses all those use cases. There was an attempt in Rust with Toshi that is built on top of tantivy, but the project seems to have stalled.
- An alternative to Elasticsearch that runs on a few MBs of RAM
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Postgres Full Text Search vs. the Rest
I wish we had an extension like ZomboDB but using a lighter search engine like https://github.com/quickwit-oss/quickwit, https://github.com/toshi-search/Toshi and https://github.com/mosuka/bayard
Here I'm listing engines based on https://github.com/quickwit-oss/tantivy - tantivy is comparable to Lucene in its scope - but I'm sure there are other engines that could tackle ElasticSearch.
Another thing that could happen is maybe directly embed tantivy in Postgres using an extension, perhaps this could be an option too.
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Ask HN: Does anybody still use bookmarking services?
I do something similar, though I index the page myself via a little browser extension I wrote. I click a button, the content gets POSTed to a server that throws it in Toshi[1]. I hacked it together on a Saturday, and it's been pretty handy; as you describe, much more useful than any bookmarking approach I've tried before.
[1] https://github.com/toshi-search/Toshi
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*set Edge as default browser*
There is some incredible work being done in the web department, frameworks like rocket.rs and actix.rs are amazing. To get the latest info on web development in Rust, check arewewebyet.org. It doesn't list Toshi though, which is weird.
- Zinc Search engine. A lightweight alternative to elasticsearch that requires minimal resources, written in Go.
- Zinc Search engine. A lightweight alternative to Elasticsearch written in Go
- AWS releases forked Elasticsearch code. Announces new name: OpenSearc
What are some alternatives?
postgres-elasticsearch-fdw - Postgres to Elastic Search Foreign Data Wrapper
elasticsearch-rs - Official Elasticsearch Rust Client
recoll - recoll with webui in a docker container
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
zombodb - Making Postgres and Elasticsearch work together like it's 2023
narg - A tool to generate LC/AP formulas for a given seed in Noita.
pgvector - Open-source vector similarity search for Postgres
sonic - 🦔 Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.
pg_search - pg_search builds ActiveRecord named scopes that take advantage of PostgreSQL’s full text search
lnx - ⚡ Insanely fast, 🌟 Feature-rich searching. lnx is the adaptable, typo tollerant deployment of the tantivy search engine.
pg_cjk_parser - Postgres CJK Parser pg_cjk_parser is a fts (full text search) parser derived from the default parser in PostgreSQL 11. When a postgres database uses utf-8 encoding, this parser supports all the features of the default parser while splitting CJK (Chinese, Japanese, Korean) characters into 2-gram tokens. If the database's encoding is not utf-8, the parser behaves just like the default parser.
OpenSearch - 🔎 Open source distributed and RESTful search engine.