Before I dive into the launch of Cycle’s latest feature (and it’s a big one!) I want to share some context about how we got here. Let’s rewind back to 2015: containers, at least in their modern form, had just begun to take the developer ecosystem by storm. At the same time, we at Cycle were watching everything unfold: from Docker’s meteoric rise to the first few releases of tools like Kubernetes, Rancher, and so on.
Understand the two dimensions of scaling for database query and ingest workloads, and how sharding can make scaling elastic — or not. Scaling throughput and performance are critical design topics for all distributed databases, and sharding is usually a part of the solution. However, a design that increases throughput does not always help with performance and vice versa. Even when a design supports both, scaling them up and down at the same time is not always easy.
Most engineering teams are no strangers to key performance indicators (KPIs), those metrics tracking progress toward critical goals and targets. Ideally, tech leaders design KPIs to focus teams on what matters and prove their contribution to the company’s overall performance. Of course, KPI data should also uncover critical information that guides informed decision-making. For engineering teams tasked with managing the customer experience, KPIs often track availability.
