opteryx VS influxdb3-python

Thanks for sharing.

I have a SQL Engine in Python too (https://github.com/mabel-dev/opteryx). I focused my initial effort on supporting SQL statements and making the usage feel like a database - that probably reflects the problem I had in front of me when I set out - only handling handfuls of gigabytes in a batch environment for ETLs with a group of new-to-data-engineering engineers. Have recently started looking more at real-time performance, such as distributing work. Am interesting in how you've approached.

InfluxDB

Scalability and Performance: Consider the scalability and performance capabilities, especially if your organization handles large volumes of data or has a growing user base. Ask yourself, "Is this tool capable of scaling with our organization's growth and maintaining performance under increased data loads?" You can utilize a tool like InfluxDB for scalable time-series data storage.

In this tutorial, you'll learn how to build a Java web application with Spring Boot that collects metrics via the Micrometer library and automatically sends them to an instance of InfluxDB, the ideal database for storing this type of data.

InfluxDB — Timeseries database, free up to 3MB/5 minutes writes, 30MB/5 minutes reads and 10,000 cardinalities series

We wanted a robust way to test the newly created InfluxDB 3.0 Python Client library, as you will see most of the tooling and functionality in use.