noria
zombodb
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noria | zombodb | |
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26 | 23 | |
4,874 | 4,608 | |
0.0% | - | |
0.0 | 8.3 | |
over 2 years ago | 16 days ago | |
Rust | PLpgSQL | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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noria
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Relational is more than SQL
> Automatically managed, application-transparent, physical denormalisation entirely managed by the database is something I am very, very interested in.
Sounds a bit like Noria: https://github.com/mit-pdos/noria
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JetBrains Noria
It feels more than a little bit coincidental to call it Noria when https://github.com/mit-pdos/noria exists (and has been posted about here on HN)... especially with the whole bit about incrementally computing changes.
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Uplevel database development with DataSQRL: A compiler for the data layer
Is this similar in spirit to Noria?
https://github.com/mit-pdos/noria
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Dozer: A scalable Real-Time Data APIs backend written in Rust
I assume you have studied Noria? https://github.com/mit-pdos/noria
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What are the Rust databases and their benefits?
If you want to look how databases are implemented in rust try https://github.com/mit-pdos/noria
- Materialized View: SQL Queries on Steroids
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Measuring how much Rust's bounds checking actually costs
Only tangentially related, but I wondered what were the difference between ReadySet and Noria, and they address this exact question in their repository I'm really glad to know that the ideas behind Noria didn't die when Noria was abandoned after /u/jonhoo graduated.
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PlanetScale Boost serves your SQL queries instantly
:wave: Author of the paper this work is based on here.
I'm so excited to see dynamic, partially-stateful data-flow for incremental materialized view maintenance becoming more wide-spread! I continue to think it's a _great_ idea, and the speed-ups (and complexity reduction) it can yield are pretty immense, so seeing more folks building on the idea makes me very happy.
The PlanetScale blog post references my original "Noria" OSDI paper (https://pdos.csail.mit.edu/papers/noria:osdi18.pdf), but I'd actually recommend my PhD thesis instead (https://jon.thesquareplanet.com/papers/phd-thesis.pdf), as it goes much deeper about some of the technical challenges and solutions involved. It also has a chapter (Appendix A) that covers how it all works by analogy, which the less-technical among the audience may appreciate :) A recording of my thesis defense on this, which may be more digestible than the thesis itself, is also online at https://www.youtube.com/watch?v=GctxvSPIfr8, as well as a shorter talk from a few years earlier at https://www.youtube.com/watch?v=s19G6n0UjsM. And the Noria research prototype (written in Rust) is on GitHub: https://github.com/mit-pdos/noria.
As others have already mentioned in the comments, I co-founded ReadySet (https://readyset.io/) shortly after graduating specifically to build off of Noria, and they're doing amazing work to provide these kinds of speed-ups for general-purpose relational databases. If you're using one of those, it's worth giving ReadySet a look to get these kinds of speedups there! It's also source-available @ https://github.com/readysettech/readyset if you're curious.
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PlanetScale Boost
It seems similar to MIT's Noria [1]
> Noria is a new streaming data-flow system designed to act as a fast storage backend for read-heavy web applications based on Jon Gjengset's Phd Thesis, as well as this paper from OSDI'18. It acts like a database, but precomputes and caches relational query results so that reads are blazingly fast. Noria automatically keeps cached results up-to-date as the underlying data, stored in persistent base tables, change. Noria uses partially-stateful data-flow to reduce memory overhead, and supports dynamic, runtime data-flow and query change.
[1] https://github.com/mit-pdos/noria
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OctoSQL allows you to join data from different sources using SQL
Materialize is really neat, also checkout https://github.com/mit-pdos/noria. It inverts the query problem and processes the data on insert. Exactly like what most applications end up doing using a no-sql solution.
zombodb
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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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Create a search engine with PostgreSQL: Postgres vs Elasticsearch
Point 2 is generally solvable via engineering effort and careful dedicated code. From the existing tools, PGSync is an open source project that aims to specifically solve this problem. ZomboDB is an interesting Postgres extension that tackles point 2 (and I think partially point 3), by controlling and querying Elasticsearch through Postgres. I haven't yet tried either of these two projects, so I can't comment on their trade-offs, but I wanted to mention them.
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Creating an advanced search engine with PostgreSQL
Curious, did you try zombodb? [https://www.zombodb.com/]
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💃🏼 Quickwit 0.6 released!🕺🏼: Elasticsearch API compatibility, Grafana plugin, and more....
What about zombodb, do you think that quickwit has all the necessary APIs?
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Write Postgres functions in Rust
No. Haha. Was just the right name for https://github.com/zombodb/zombodb at the time. Software where the only limit is yourself!
- Integrate PostgreSQL and Elasticsearch – ZomboDB
- Postgres Full Text Search vs. the Rest
- ZomboDB: Making Postgres and Elasticsearch work together like it's 2022
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Postgres Full-Text Search: A Search Engine in a Database
> The hardest part of building any search engine is keeping the index up-to-date with changes made to the underlying data store.
This deserves mention, as it solves that problem: https://github.com/zombodb/zombodb
From the README:
> ZomboDB brings powerful text-search and analytics features to Postgres by using Elasticsearch as an index type. Its comprehensive query language and SQL functions enable new and creative ways to query your relational data.
> From a technical perspective, ZomboDB is a 100% native Postgres extension that implements Postgres' Index Access Method API. As a native Postgres index type, ZomboDB allows you to CREATE INDEX ... USING zombodb on your existing Postgres tables. At that point, ZomboDB takes over and fully manages the remote Elasticsearch index and guarantees transactionally-correct text-search query results.
I find other things also hard in search engines: dealing with the plethora of human languages and all the requirements we may have to processing them. A mature solution like ES therefor is almost a must in the more demanding cases.
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State of the art for serde-compatible CBOR encoding/decoding?
You can read more about it on our GitHub repo, but basically it brings most of the power of elasticsearch’s searching and analytics abilities straight into Postgres.
What are some alternatives?
timely-dataflow - A modular implementation of timely dataflow in Rust
pg_search - pg_search builds ActiveRecord named scopes that take advantage of PostgreSQL’s full text search
realtime - Broadcast, Presence, and Postgres Changes via WebSockets
Typesense - Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
TablaM - The practical relational programing language for data-oriented applications
squawk - 🐘 linter for PostgreSQL, focused on migrations
readyset - Readyset is a MySQL and Postgres wire-compatible caching layer that sits in front of existing databases to speed up queries and horizontally scale read throughput. Under the hood, ReadySet caches the results of cached select statements and incrementally updates these results over time as the underlying data changes.
stolon - PostgreSQL cloud native High Availability and more.
mysql-live-select - NPM Package to provide events on updated MySQL SELECT result sets
helium-etl-queries - A collection of SQL views used to enrich data produced by a Helium blockchain-etl
materialize - The data warehouse for operational workloads.
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.