This post was written by Dean Markwick, who has put together an excellent example using QuestDB as a time series database for high-frequency trading. This post shows how to use QuestDB to calculate the limit order book, price impact, trade sign distribution, and other concepts via the Julia programming language.

The latest QuestDB release introduces support for geospatial data via the addition of geohash types. Geohashes encode geographic areas as base-32 strings, and native support for this type allows for fast and efficient querying and storage of geodata. Also included are helper functions for rounding timestamps, performance improvements for existing functions, alongside other fixes and features. Here's the full roundup of changes that have just landed!

We're happy to announce that QuestDB is available with an official listing on the DigitalOcean marketplace. Deploying QuestDB via 1-click app means it's quick and easy to get started with a high-performance SQL database for time series. In this announcement, we'll show you how to get started and show how you can make use of some free DigitalOcean credit for new users.

We've published the latest QuestDB release, and it focuses on community-driven topics raised with us recently by our users. The features included are performance improvements, increased parallelization of existing code, and calendar alignment for SAMPLE BY queries. Also included is the introduction of a framework for exposing Prometheus metrics by our community member Piotr Rżysko. Here's the full roundup of changes that have just landed!

If you're working with large amounts of data, you've likely heard about high-cardinality or ran into issues relating to it. It might sound like an intimidating topic if you're unfamiliar with it, but this article explains what cardinality is and why it crops up often with databases of all types. IoT and monitoring are use cases where high-cardinality is more likely to be a concern. Still, a solid understanding of this concept helps when planning general-purpose database schemas and understanding common factors that can influence database performance.

The journey to today's version of QuestDB began with the original prototype in 2013, and we've described what happened since in a post published during our HN launch last year. In the early stages of the project, we were inspired by vector-based append-only systems like kdb+ because of the advantages of speed and the simple code path this model brings. We also required that row timestamps were stored in ascending order, resulting in fast time series queries without an expensive index.

We've just published an alpha version for the upcoming 6.0 major release and it includes long-awaited support for ingesting out-of-order records on-the-fly, a complete overhaul of the InfluxDB Line Protocol subsystem, and multiple fixes which provide stability improvements. Here's a roundup of changes that have just landed in the latest and greatest version!

We've just released software version 5.0.6 and it comes with plenty of additional features and functionality, a full refactoring of PostgreSQL wire support, and multiple fixes to improve the stability of the system. Here's a roundup of recent changes that have just landed.

Garbage collection is a type of automatic memory management that's used in many modern programming languages. The point of the garbage collector is to free up memory used by objects which are no longer being used by a program. Although it's convenient for developers not to think about manually deallocating memory, it can be a poisoned chalice that comes with several hard-to-predict downsides.

Like all good superheroes, every company has its own origin story explaining why they were created and how they grew over time. This article covers the origin story of QuestDB and frames it with an introduction to time series databases to show where we sit in that landscape today.