pg_jsonschema
rum
pg_jsonschema | rum | |
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15 | 11 | |
929 | 693 | |
1.6% | 0.7% | |
6.6 | 4.0 | |
20 days ago | 4 months ago | |
Rust | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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pg_jsonschema
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Introducing pgzx: create PostgreSQL extensions using Zig
And lots of interesting extensions use it, like
https://github.com/tembo-io/pgmq
https://github.com/zombodb/zombodb
https://github.com/supabase/pg_jsonschema
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Beyond SQL: A relational database for modern applications
> In other words, there is still a (lossy) translation layer, it just happens to be in the RDBMS rather than in-app.
It's not lossy if your application can guarantee a json <-> datatype roundtrip and the json is validated with jsonschema (generated by your application)
In Rust it's something like this
https://serde.rs/ to do the data type <-> json mapping
https://docs.rs/schemars/latest/schemars/ to generate jsonschema from your types
https://github.com/supabase/pg_jsonschema to validate jsonschema in your database (postgres). with this setup it's interesting (but not required) to also use https://docs.rs/jsonschema/latest/jsonschema/ to validate the schema in your application
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FerretDB, a truly open-source MongoDB alternative
Pretty exciting!
What about optionally validating some columns with jsonschema? Perhaps using https://github.com/supabase/pg_jsonschema - is using other postgres extensions supported in FerretDB? (if not, maybe it's feasible to incorporate the code of pg_jsonschema in FerretDB?)
- Type Constraints in 65 lines of SQL
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Ask HN: Do you use JSON Schema? Help us shape its future stability guarantees
I'm not currently using it, but I'm strongly considering validating json in postgres with https://github.com/supabase/pg_jsonschema - which uses the https://docs.rs/jsonschema/latest/jsonschema/ Rust crate
So I'm not sure if my feedback is valid but, I sure hope that the jsonschema crate follows the spec! Otherwise I'll never use jsonschema but instead something-not-exactly-jsonschema. In other words.. you better not break anything.
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Features I'd Like in PostgreSQL
Sounds dumb, but I want JSON field schema validation. I added a JSON column for flexible data, and although I'm happy with its flexibility, I kinda hope I can validate the JSON data structure. Recently I just found an extension [1] and will try soon.
[1] https://github.com/supabase/pg_jsonschema
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Dynamic JSON schema validation, how can I do that in Postgres?
https://github.com/supabase/pg_jsonschema is new and looks good
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Supabase Beta July 2022
Born as an excuse to play with pgx, pg_jsonschema is a solution we're exploring to allow enforcing more structure on json and jsonb typed postgres columns. Only 10 lines of code 😎
- GitHub - supabase/pg_jsonschema: PostgreSQL extension providing JSON Schema validation
- Show HN: Pg_jsonschema – A Postgres extension for JSON validation
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.
What are some alternatives?
windmill - Open-source developer platform to turn scripts into workflows and UIs. Fastest workflow engine (5x vs Airflow). Open-source alternative to Airplane and Retool.
postgres-elasticsearch-fdw - Postgres to Elastic Search Foreign Data Wrapper
postgres-json-schema - JSON Schema validation for PostgreSQL
recoll - recoll with webui in a docker container
pgx - Build Postgres Extensions with Rust! [Moved to: https://github.com/tcdi/pgrx]
zombodb - Making Postgres and Elasticsearch work together like it's 2023
is_jsonb_valid - Native PostgreSQL extension to validate jsonb
pgvector - Open-source vector similarity search for Postgres
pg_ivm - IVM (Incremental View Maintenance) implementation as a PostgreSQL extension
pg_search - pg_search builds ActiveRecord named scopes that take advantage of PostgreSQL’s full text search
auth - A JWT based API for managing users and issuing JWT tokens
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.