pg_cjk_parser VS rum

Compare pg_cjk_parser vs rum and see what are their differences.

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. (by freewizard)

rum

RUM access method - inverted index with additional information in posting lists (by postgrespro)
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pg_cjk_parser rum
1 5
4 521
- 1.0%
0.4 6.7
over 1 year ago 14 days ago
C C
- GNU General Public License v3.0 or later
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pg_cjk_parser

Posts with mentions or reviews of pg_cjk_parser. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-07-27.

rum

Posts with mentions or reviews of rum. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-07-27.
  • Postgrespro/rum: RUM access method – inverted index with additional information
    1 project | news.ycombinator.com | 17 Dec 2021
  • Postgres Full-Text Search: A Search Engine in a Database
    10 projects | news.ycombinator.com | 27 Jul 2021
    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.
    10 projects | news.ycombinator.com | 27 Jul 2021
    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.

  • Debugging random slow writes in PostgreSQL
    1 project | news.ycombinator.com | 15 May 2021
    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

  • Show HN: Full text search Project Gutenberg (60m paragraphs)
    5 projects | news.ycombinator.com | 24 Jan 2021
    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.

What are some alternatives?

When comparing pg_cjk_parser and rum you can also consider the following projects:

hn-search - Hacker News Search

zombodb - Making Postgres and Elasticsearch work together like it's 2022

pg_search - pg_search builds ActiveRecord named scopes that take advantage of PostgreSQL’s full text search

recoll - recoll with webui in a docker container

simonwillisonblog - The source code behind my blog