greptimedb VS numexpr

GreptimeAI, built upon the open-source time-series database GreptimeDB, offers an observability solution for Large Language Model (LLM) applications, currently supporting both LangChain and OpenAI's ecosystem. GreptimeAI enables you to understand cost, performance, traffic and security aspects in real-time, helping teams enhance the reliability of LLM applications.

Continuing to work hard on a new MetricEngine in GreptimeDB. BTW, If you have a keen interest in Rust or database development, GreptimeDB might be a good starting point. Check it out for some good first issues here.

Since the very first day of GreptimeDB going open-source, it embraced the automated software building process with GitHub Actions, and leading to the inaugural Release Pipeline.

Born from the open-source project GreptimeDB, GreptimeCloud serves as a fully-managed, serverless cloud backend for Prometheus, offering integrated support for remote read/write protocols and PromQL as one of our primary query languages.

Recently, while working on our GreptimeDB project, we encountered an issue with calling asynchronous Rust code in a synchronous context.

To explore data stored in GreptimeDB through PromQL, GreptimeDB needs to provide the ability to parse the query into AST (abstract syntax tree), and retrieve data from memory or disk via logical and physical plans. Since there is no ready-to-use PromQL Rust Parser, our team decides to develop it by ourselves. We’re glad to announce that promql-parser v0.1.0 is now available.

This is truly a fantastic combination -- implement the logic in Rust and use it in Python. GreptimeDB also implements a similar functionality that allows writing Python script to do post-process of SQL query results, with the help of RustPython and Arrow. Maybe this combination can bring a sweet point between performance and efficiency.

docs: https://docs.greptime.com/user-guide/coprocessor-and-scripti...

code: https://github.com/GreptimeTeam/greptimedb/tree/develop/src/...

You can just slap numexpr on top of it to compile this line on the fly.

https://github.com/pydata/numexpr

Are you doing any costly chained NumPy operations in your preprocessing? E.g. max(abs(large_ary)), this produces multiple copies of your data, https://github.com/pydata/numexpr can greatly reduce time spent with such operations

What is not entirely obvious here is that under the hood you can install a nice library called numexpr (docs, src) that exists to make calculations with large NumPy (and pandas) objects potentially much faster. When you use query or eval, this expression is passed into numexpr and optimized using its bag of tricks. Expected performance improvement can be between .95x and up to 20x, with average performance around 3-4x for typical use cases. You can read details in the docs, but essentially numexpr takes vectorized operations and makes them work in chunks that optimize for cache and CPU branch prediction. If your arrays are really large, your cache will not be hit as often. If you break your large arrays into very small pieces, your CPU won’t be as efficient.