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.
I have recently made a sizable contribution to QuestDB’s code and wanted to share my experience and feedback while it is still fresh in my head. I am not a complete outsider for the project and know Vlad personally but other than that it was voluntary to add a few lines of code to a project I like.
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.
InfluxDB line protocol is a simple and convenient way to add data points to QuestDB. Now with authentication, your endpoint is more secure.
Every cab I have ever ridden has been complaining about how hard it is to make ends meet as a driver. The public is generally quick to blame unfair competition from the likes of Uber. However, additional forces are also to blame.
Going through more than 10 years worth of NYC taxi data, I analyse how the antiquated meter system impacts the livelihood of NYC cabbies by drawing an analogy with stock options trading. Interestingly, this approach allows us to show that drivers have progressively been worse-off, independently of competition from Uber.
In order to do so, we have loaded a dataset into our database QuestDB. This dataset includes over 1.6 billion taxi rides, 700 million FHV rides (Uber, Lyft etc), and 10 years of weather and gas prices data.
Thoughts on why speed and performance are crucial to time-series databases ingestion and analysis, originally posted in The New Stack.
Note: I wanted you to know that this post is written by one of our contributors, Shan Desai. Shan is a research scientist working for the Bremen Institute for Production and Logistics (BIBA). His work involves the use of IoT devices in order to improve product tracking and transparency in a B2B marketplace. You can find more details on Shan's personnal website.
Thanks a lot for your contribution Shan!
How does QuestDB get the kind of performance it does, and how are we continuing to squeeze another 50-60% out of it? We are constantly learning more about the fundamental concepts of memory performance, and this is one example of how what we at first thought would be worse for performance ended up bringing us a rather substantial boost in overall memory performance.
We will walk you through how some of our initial thoughts on storage and memory-mapping evolved to bring us better performance overall.
If you like QuestDB, please do give us a star on GitHub
A few weeks ago, I posted the story of how I started QuestDB on Hacker News. As it seems several people found the story interesting, I thought I would post it here.
We've been upping our SWAG game a lot lately, and we want to share it with you, the valuable members of our community! We want to give you the chance to show off your projects, show off your love for QuestDB, and to show just off!