postgresml VS db-benchmark

The Python client SDK is so small, because it's just a wrapper around the Rust client SDK: https://github.com/postgresml/postgresml/tree/master/pgml-sdks/rust/pgml. Currently we also support JS/Typescript SDKs as well, all generated from the same safe and efficient underlying Rust implementation, using some fancy Rust macros.

I don't think you'd replace a materialized view with pg_later, but it might help you populate or update your materialized view if you are trying to do that asynchronously. pglater.exec() works with DDL too!

I use it a lot for long running queries when doing data science and machine learning work, and a lot of times when executing queries from a jupyter notebook or CLI. That way if my jupyter kernel dies, my query execution continues even if the network or my environment has an issue. I've started using it a bit more with https://github.com/postgresml/postgresml for model training tasks too, since those can be quite long running depending on the situation.

PostgresML comes with pgvector as a vector database. The cool thing is it can run your models in the same memory space as a database extension. We’re also working on ggml support for huggingface transformers, but could use some help testing more LLMs for compatibility. https://github.com/postgresml/postgresml/pull/748

We've been working on a Python SDK[1] for PostgresML to make it easier for application developers to get the performance and scalability benefits of integrated memory for LLMs, by combining embedding generation, vector recall and LLM tasks from HuggingFace in a single database query.

This work builds on our previous efforts that give a 10x performance improvement from generating the LLM embedding[2] from input text along with tuning vector recall[3] in a single process to avoid excessive network transit.

We'd love your feedback on our roadmap[4] for this extension, if you have other use cases for an ML application database. So far, we've implemented our best practices for scalable vector storage to provide an example reference implementation for interacting with an ML application database based on Postgres.

[1]: https://github.com/postgresml/postgresml/tree/master/pgml-sd...

We've been working on a Python SDK for PostgresML to make it easier for application developers to get the performance and scalability benefits of integrated memory for LLMs, by combining embedding generation, vector recall and LLM tasks from HuggingFace in a single database query.

Links:

[1]: https://huggingface.co/spaces/mteb/leaderboard

[2]: https://postgresml.org/blog/generating-llm-embeddings-with-o...

[3]: https://postgresml.org/blog/tuning-vector-recall-while-gener...

[4]: https://postgresml.org/blog/personalize-embedding-vector-sea...

Github: https://github.com/postgresml/postgresml

Github: https://github.com/postgresml/postgresml

I'd encourage you to do inference outside of PostgreSQL (use TF serving and make requests against it, or do batch inference), but if you're determined to do so, they have an extension that integrates with the transformers library and allows for calling models directly from SQL.

Real-world performance is complicated since data science covers a lot of use cases.

If you're just reading a small CSV to do analysis on it, then there will be no human-perceptible difference between Polars and Pandas. If you're reading a larger CSV with 100k rows, there still won't be much of a perceptible difference.

Per this (old) benchmark, there are differences once you get into 500MB+ territory: https://h2oai.github.io/db-benchmark/

I do think it was important for duckdb to put out a new version of the results as the earlier version of that benchmark [1] went dormant with a very old version of duckdb with very bad performance, especially against polars.

[1] https://h2oai.github.io/db-benchmark/

https://news.ycombinator.com/item?id=33270638 :

> Apache Ballista and Polars do Apache Arrow and SIMD.

> The Polars homepage links to the "Database-like ops benchmark" of {Polars, data.table, DataFrames.jl, ClickHouse, cuDF, spark, (py)datatable, dplyr, pandas, dask, Arrow, DuckDB, Modin,} but not yet PostgresML? https://h2oai.github.io/db-benchmark/ *

LLM -> Vector database: https://en.wikipedia.org/wiki/Vector_database

/? inurl:awesome site:github.com "vector database"

I agree with your conclusion but want to add that switching from Julia may not make sense either.

According to these benchmarks: https://h2oai.github.io/db-benchmark/, DF.jl is the fastest library for some things, data.table for others, polars for others. Which is fastest depends on the query and whether it takes advantage of the features/properties of each.

For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.

Same. Numba does wonders for me in most scenarios. Yesterday I've discovered pola-rs and looks like I will add it to the stack. It's API is similar to pandas. Have a look at the benchmarks of cuDF, spark, dask, pandas compared to it: Benchmarks

The syntax has similarities with dplyr in terms of the way you chain operations, and it’s around an order of magnitude faster than pandas and dplyr (there’s a nice benchmark here). It’s also more memory-efficient and can handle larger-than-memory datasets via streaming if needed.

If interested in benchmarks comparing different dataframe implementations, here is one:

https://h2oai.github.io/db-benchmark/

Bad examples. Both numpy and pandas are notoriously un-optimized packages, losing handily to pretty much all their competitors (R, Julia, kdb+, vaex, polars). See https://h2oai.github.io/db-benchmark/ for a partial comparison.