automerge
ann-benchmarks
automerge | ann-benchmarks | |
---|---|---|
45 | 51 | |
3,134 | 4,604 | |
4.0% | - | |
9.2 | 7.7 | |
7 days ago | 9 days ago | |
JavaScript | Python | |
MIT License | MIT License |
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automerge
- Automerge CRDT
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Flutter offline
I'm not aware of any usable CRDT implementations for Dart, my plan is to use the flutter_rust_bridge to make use of automerge v2, which is a full CRDT implementation written in Rust that has the advantage of having a very simple API to work with (basically a key/value store).
- Ask HN: What is new in Algorithms / Data Structures these days?
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Best local database that works on all platforms including web?
Yes. I asked the devs about ideas for this in this ticket and got an interesting response. It's aimed towards server-side handling, but the same ideas apply to local storage as well.
- Show HN: Pg_CRDT – an experimental CRDT extension for Postgres
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CRDTs: A Beginner's overview for building a collaborative app
There are a lot of implementations of CRDTs out there. In JavaScript, for instance, we have Y.js (https://github.com/yjs/yjs) and automerge (https://github.com/automerge/automerge). There’s also a Y.js demo (https://demos.yjs.dev/prosemirror/prosemirror.html) that allows you to play around with them and have your own collaborative app running in just a few seconds. All messages are exchange via webRTC and manages the state via CRDTs. This can be a great sandbox to understand how CRDTs work and see.
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Evan Wallace CRDT Algorithms
Anyone unsure of what a CRDT is, this is the perfect intro: https://www.inkandswitch.com/peritext/
The two most widely used CRDT implementations (combining JSON like general purpose types and rich text editing types) are:
- Automerge https://github.com/automerge/automerge
- Yjs https://github.com/yjs/yjs
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Should I Move From PHP to Node/Express?
For instance, practicing "local first web" using automerge with all it's Distributed Persistence Primitives on CRDT's and Vector Clocks - i.e. when the Browser View is treated like a Database replica, essentially; or adopting a real data mapper that's giving you an API from your database Schema, using Prisma or Hasura... or even implementing a custom codegenereted one, as a babel plugin, on top of TSED and Micro-ORM.
- Maintaining Referential Integrity During Insertions And Deletions
- Muse 2.0
ann-benchmarks
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Using Your Vector Database as a JSON (Or Relational) Datastore
On top of my head, pgvector only supports 2 indexes, those are running in memory only. They don't support GPU indexing, nor Disk based indexing, they also don't have separation of query and insertions.
Also with different people I've talked to, they struggle with scale past 100K-1M vector.
You can also have a look yourself from a performance perspective: https://ann-benchmarks.com/
- ANN Benchmarks
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Approximate Nearest Neighbors Oh Yeah
https://ann-benchmarks.com/ is a good resource covering those libraries and much more.
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pgvector vs Pinecone: cost and performance
We utilized the ANN Benchmarks methodology, a standard for benchmarking vector databases. Our tests used the dbpedia dataset of 1,000,000 OpenAI embeddings (1536 dimensions) and inner product distance metric for both Pinecone and pgvector.
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Vector database is not a separate database category
Data warehouses are columnar stores. They are very different from row-oriented databases - like Postgres, MySQL. Operations on columns - e.g., aggregations (mean of a column) are very efficient.
Most vector databases use one of a few different vector indexing libraries - FAISS, hnswlib, and scann (google only) are popular. The newer vector dbs, like weaviate, have introduced their own indexes, but i haven't seen any performance difference -
Reference: https://ann-benchmarks.com/
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How We Made PostgreSQL a Better Vector Database
(Blog author here). Thanks for the question. In this case the index for both DiskANN and pgvector HNSW is small enough to fit in memory on the machine (8GB RAM), so there's no need to touch the SSD. We plan to test on a config where the index size is larger than memory (we couldn't this time due to limitations in ANN benchmarks [0], the tool we use).
To your question about RAM usage, we provide a graph of index size. When enabling PQ, our new index is 10x smaller than pgvector HNSW. We don't have numbers for HNSWPQ in FAISS yet.
[0]: https://github.com/erikbern/ann-benchmarks/
- Do we think about vector dbs wrong?
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Vector Search with OpenAI Embeddings: Lucene Is All You Need
In terms of "All You Need" for Vector Search, ANN Benchmarks (https://ann-benchmarks.com/) is a good site to review when deciding what you need. As with anything complex, there often isn't a universal solution.
txtai (https://github.com/neuml/txtai) can build indexes with Faiss, Hnswlib and Annoy. All 3 libraries have been around at least 4 years and are mature. txtai also supports storing metadata in SQLite, DuckDB and the next release will support any JSON-capable database supported by SQLAlchemy (Postgres, MariaDB/MySQL, etc).
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Vector databases: analyzing the trade-offs
pg_vector doesn't perform well compared to other methods, at least according to ANN-Benchmarks (https://ann-benchmarks.com/).
txtai is more than just a vector database. It also has a built-in graph component for topic modeling that utilizes the vector index to autogenerate relationships. It can store metadata in SQLite/DuckDB with support for other databases coming. It has support for running LLM prompts right with the data, similar to a stored procedure, through workflows. And it has built-in support for vectorizing data into vectors.
For vector databases that simply store vectors, I agree that it's nothing more than just a different index type.
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Vector Dataset benchmark with 1536/768 dim data
The reason https://ann-benchmarks.com is so good, is that we can see a plot of recall vs latency. I can see you have some latency numbers in the leaderboard at the bottom, but it's very difficult to make a decision.
As a practitioner that works with vector databases every day, just latency is meaningless to me, because I need to know if it's fast AND accurate, and what the tradeoff is! You can't have it both ways. So it would be helpful if you showed plots showing this tradeoff, similar to ann-benchmarks.
What are some alternatives?
yjs - Shared data types for building collaborative software
pgvector - Open-source vector similarity search for Postgres
crdt-benchmarks - A collection of CRDT benchmarks
faiss - A library for efficient similarity search and clustering of dense vectors.
y-websocket - Websocket Connector for Yjs
Milvus - A cloud-native vector database, storage for next generation AI applications
FluidFramework - Library for building distributed, real-time collaborative web applications
tlsh
slate-yjs - Yjs binding for Slate
vald - Vald. A Highly Scalable Distributed Vector Search Engine
SyncedStore - SyncedStore CRDT is an easy-to-use library for building live, collaborative applications that sync automatically.
pgANN - Fast Approximate Nearest Neighbor (ANN) searches with a PostgreSQL database.