paradedb
retake
| paradedb | retake | |
|---|---|---|
| 16 | 4 | |
| 3,962 | 757 | |
| 11.0% | - | |
| 9.8 | 10.0 | |
| 3 days ago | 8 months ago | |
| Rust | Rust | |
| GNU Affero General Public License v3.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
paradedb
- Using ClickHouse to scale an events engine
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Code Search Is Hard
Elasticsearch is good, and it does scale, but it is much more cumbersome and expensive to scale and operate than Postgres. If you use the managed service, you'll pay for the operational pain in the form of higher pricing.
The Postgres movement is strong and extensions like ParadeDB https://github.com/paradedb/paradedb are designed specifically to solve this pain point (Disclaimer: I work for ParadeDB)
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Ask HN: Best way to mirror a Postgres database to parquet?
No timeline yet, but we know it's a high-priority feature and are working hard on it. Best way would be to join our Slack (link here: https://github.com/paradedb/paradedb/blob/dev/README.md) to follow along. It will be in the coming weeks/months, though.
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Transforming Postgres into a Fast OLAP Database
You're right. We're working on this currently. You can track the issue here: https://github.com/paradedb/paradedb/issues/717
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We built our customer data warehouse all on Postgres
There are definitely ways to cleanly make Postgres scale for analytics. We didn't discuss in this blog, but we will be writing about them in the future. For example, check out what the folks at ParadeDB are doing. https://github.com/paradedb/paradedb. Neon is doing an awesome job separating compute from storage. Supabase contributed foreign data wrappers make it super easy to read from S3 into Postgres. Lots of great work going out there :)
- Show HN: Pg_analytics – Speed Up Postgres Analytical Queries by 94x
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Multi-Database Support in DuckDB
Check out https://github.com/paradedb/paradedb/tree/dev/pg_analytics, we're shipping this week
- ParadeDB – PostgreSQL for Search
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Postgresql index
Shameless plug, but I'm one of the makers of `pg_bm25` (https://github.com/paradedb/paradedb). We're making a faster tsvector/tsrank as a Postgres extension. Maybe it can help, our benchmarks show much faster performance especially as row count increases
- Building an open source vector database. Looking for advice.
retake
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Show HN: Retake – Open-Source Hybrid Search for Postgres
https://github.com/getretake/retake/pull/198 is a refreshing change given the recent rug pulls, so thank you for that
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We created an open-source semantic search Python package on top of Postgres
We found it difficult to do well with standard vector databases and so we ended up making a nice open-source package to layer semantic search on top of Postgres with just a few lines of code. It supports Python backends right now, always stays in sync with Postgres via Kafka, doubles as a vector store, and can be deployed anywhere.
- Show HN: Open-Source Infrastructure for Vector Data Streams
What are some alternatives?
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
bionicgpt - BionicGPT is an on-premise replacement for ChatGPT, offering the advantages of Generative AI while maintaining strict data confidentiality [Moved to: https://github.com/bionic-gpt/bionic-gpt]
tantivy - Tantivy is a full-text search engine library inspired by Apache Lucene and written in Rust
nfcompose - Build REST APIs/Integrations in minutes instead of hours - NF Compose is a (data) integration platform that allows developers to define REST APIs in seconds instead of hours. Generated REST APIs are backed by postgres and support automatic consumer webhook notifications on data changes out of the box.
prism - Prism is the easiest way to develop, orchestrate, and execute data pipelines in Python.
embedditor - ⚡ GUI for editing LLM vector embeddings. No more blind chunking. Upload content in any file extension, join and split chunks, edit metadata and embedding tokens + remove stop-words and punctuation with one click, add images, and download in .veml to share it with your team.
vectorflow - VectorFlow is a high volume vector embedding pipeline that ingests raw data, transforms it into vectors and writes it to a vector DB of your choice.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
tinyvector - A tiny embedding database in pure Rust.
pgrx - Build Postgres Extensions with Rust!
pgsync - Postgres to Elasticsearch/OpenSearch sync