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.
QuestDB supports ingesting records using InfluxDB line protocol. This means that you can benefit from a simple, lightweight, and convenient message format to add data points to tables. We've further improved support for this feature by adding authentication, so your endpoint is more secure. This post describes how we added this functionality and how to enable it via QuestDB configuration.
Every cab I have ever ridden has been complaining about how hard it is to make ends meet as a driver. Using a dataset of over 1.6 billion taxi rides, 700 million FHV rides (Uber, Lyft, etc.), and 10 years of weather and gas prices data, I examine whether the antiquated meter system impacts NYC cabbies' livelihood, rather than competition from the likes of Uber.
Good data from the past helps us make better decisions in the present. Most of today's data were created within the past ten years, and human data output will only grow exponentially from here on. This sudden pervasiveness of data means that we need new ways to store and process information focusing on efficiency and sustainability. This article describes why speed and performance in a time-series database is the key to staying afloat in a sea of data.
A few weeks ago, I posted the story of how I started QuestDB on Hacker News. Several people found the story interesting, so I thought I would post it here and describe the passage from working at a large energy trading company, discovering memory-mapping approaches in Java, the beginnings of building the system as a side-project, and how we got to where we are today with companies relying on production instances of our time-series database.