InfluxDB 3 & Rust

Nov 8, 2023

In this podcast, the team behind the InfluxDB 3.0 rewrite discusses their decision to rebuild the database for better memory management and performance. They explore the challenges of handling different types of observability data and aim to make InfluxDB the go-to solution for storing and querying observational data. The podcast also covers the challenges of working with logs, tracing, and structured events in time series databases, as well as the differences between InfluxQL and SQL. Additionally, they dive into the license changes in open source projects and the implications for the developer community.

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Introduction

00:00 • 2min

InfluxDB Rewrite and the Move to Rust

01:47 • 1min

The Architecture and Future of InfluxDB

03:08 • 17min

Challenges of Storing and Querying Data

20:11 • 14min

Differences between InfluxQL and SQL, Investment in Flux language, API bridge for InfluxDB 3

33:47 • 11min

License Changes and Implications for the Open Source Community

44:44 • 11min

InfluxDB 3.0 Rewrite

InfluxDB, a time series database, underwent a major rewrite to create InfluxDB 3.0, also known as IOx. The decision to rewrite the database was driven by the need for strict control over memory management and high performance. The project started as a research endeavor and gradually gained traction within the company. The team decided to build around projects under the Apache Foundation, such as Apache Arrow and Apache Data Fusion. In April 2022, InfluxDB 3.0 was officially announced, aiming to improve performance, scalability, and cost-effectiveness for users.

IOx Database Engine

The new database engine, IOx, is designed to handle various types of observability and monitoring data, including metrics, traces, and logs. It aims to provide a single store for all these signals, eliminating the need for separate databases. However, querying the data efficiently is still a challenge that the team is working on. The goal is to make IOx the go-to solution for storing and querying observational data, not only for server infrastructure monitoring but also for sensor data use cases.

Challenges and Considerations

Working with logs, tracing, and structured events in time series databases poses challenges. The dynamic and inconsistent nature of schemas in logs and tracing use cases can make extracting structured fields difficult. Time series databases also have limitations in handling tracing front ends and require an index to map trace IDs to individual traces. While metrics, logs, and traces are the gold standard for observability, there is room for improvement in terms of usability and performance.

Flux and Data Fusion

Flux, a scripting language developed for InfluxDB 2.0, addresses user requests for more complex query logic and integration with third-party systems. InfluxDB 3.0 incorporates a parser in Rust to translate SQL queries into a Data Fusion query plan, benefiting from the performance optimizations of Data Fusion. However, bringing Flux to InfluxDB 3.0 proved challenging due to the large surface area of Flux and limited time and resources. Updating the Flux engine to use the 3.0 native API could potentially resolve these issues.

InfluxDB Development and Open Source Licensing

InfluxData is focused on improving the core query engine of InfluxDB and enhancing its capabilities and performance. They have created a separate community fork of Flux to allow collaboration on its development. Paul Dix, the co-founder, believes that true open source should be about freedom and expresses his intention to keep InfluxDB 3 as a permissively licensed project. He discusses the recent license change by HashiCorp and the growing distrust in the developer community towards VC-backed open source projects. Putting InfluxDB into a foundation may not be feasible due to the lack of multiple contributors.