rum
react-virtualized
rum | react-virtualized | |
---|---|---|
11 | 40 | |
693 | 25,968 | |
0.7% | - | |
4.0 | 1.6 | |
4 months ago | 4 months ago | |
C | JavaScript | |
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.
react-virtualized
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The Secret Weapon of Top Developers: 7 React JS Libraries You Can't Afford to Ignore
You may increase the rendering efficiency of tabular and huge list data by using the React Virtualized module. React apps perform better overall when the quantity of requests and DOM elements is limited. React Virtualized is comparable to many other tools; however, what sets it apart from the competition is the sheer volume of features and excellent upkeep.
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33 React Libraries Every React Developer Should Have In Their Arsenal
17.react-virtualized
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React Virtualisation from scratch
If you have been using React for awhile, you may have heard of the infamous virtualisation library react-window or it's predecessor react-virtualized
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13 Must Know Libraries for a React Developer
React Virtualized is a React library that helps you work with large lists and tabular data efficiently in React. It has more than 25K stars on GitHub and more than 2.5 million weekly downloads on NPM as of August 2023.
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Faster re-rending of table when only inserts are needed
Use virtualization (e.g. react-virtualized) to prevent off-screen components from actually rendering.
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5 Tips for Optimizing ReactJS Performance and Building Lightning-Fast Applications
Virtualization can be achieved using third-party libraries like react-window or react-virtualized. These libraries provide a way to render only the visible data and load more data as needed, resulting in faster application performance.
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Phoenix Dev Blog - Streams
You can implement the same pattern on the web when dealing with large amount of data. There are some libraries for React that trivialize this, like https://github.com/bvaughn/react-virtualized
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Introducing Suspense: APIs to simplify data loading and caching, for use with React Suspense.
Oh, right. I totally forgot to mention that– but the idea of "less rendering" in this case seems less like a Suspense concern and more like a windowing concern. I've written a few libraries for that stuff (react-window and react-virtualized) although there are others that may fit your needs better. Their main focus is limiting what you're rendering to more or less only what's on the screen at any given point. Combine that with memoized filtering and I would imagine you're set.
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Best infinity scroll?
I've used the InfiniteLoader from react-virtualized in combination with useInfiniteQuery from @tanstack/react-query and it was relatively painless.
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Thoughts on this Timeline design I've been working on?
Here’s a react library https://github.com/bvaughn/react-virtualized
What are some alternatives?
postgres-elasticsearch-fdw - Postgres to Elastic Search Foreign Data Wrapper
react-lazyload - Lazy load your component, image or anything matters the performance.
recoll - recoll with webui in a docker container
react-window - React components for efficiently rendering large lists and tabular data
zombodb - Making Postgres and Elasticsearch work together like it's 2023
react-virtual - 🤖 Headless UI for Virtualizing Large Element Lists in JS/TS, React, Solid, Vue and Svelte [Moved to: https://github.com/TanStack/virtual]
pgvector - Open-source vector similarity search for Postgres
streamlit - Streamlit — A faster way to build and share data apps.
pg_search - pg_search builds ActiveRecord named scopes that take advantage of PostgreSQL’s full text search
react-virtuoso - The most powerful virtual list component for React
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.
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