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.
How does QuestDB get the kind of performance it does, and how are we continuing to squeeze another 50-60% out of it? This post will look at a code change we thought would create a negative performance impact, which actually brought a substantial boost in the system's overall performance and demonstrates that we are constantly learning more about performance improvements.
In the world of databases, benchmarking performance has always been the hottest topic. Who is faster for data ingestion and queries? About a month ago we announced a new release with SIMD aggregations on HackerNews and Reddit. Fast. But were those results numerically accurate?
Inter-thread messaging is a fundamental part of any asynchronous system. It is the component responsible for the transportation of data between threads. Messaging forms the infrastructure, scaffolding multi-threaded applications, and just like real-world transport infrastructure, we want it to be inexpensive, fast, reliable, and clean. For QuestDB, we wrote our own messaging system, and this post is about how it works and how fast it is.