Algorithmic sharding distributes data by its sharding function only. It doesn’t consider the payload size or space utilization. To uniformly distribute data, each partition should be similarly sized. Fine grained partitions reduce hotspots — a single database will contain many partitions, and the sum of data between databases is statistically likely to be similar. For this reason, algorithmic sharding is suitable for key-value databases with homogeneous values.
…on as possible, there are certain other forms of testing that are best performed with real traffic. A good example would be soak testing, which is a form of verification of a service’s reliability and stability over long period of time under a realistic level of concurrency and load, used to detect memory leaks, GC pause times, CPU utilization and so forth over a period of time.
While it’s fair enough to suggest that all of the aforementioned differences aren’t so much arguments against staging environments per se than anti-patterns to be avoided, it also follows that “doing it right” often entails investing a copious amount of engineering effort to attain any semblance of parity between the environments. With production being ever changing and malleable to a wide variety of forces, trying to attain sai…
Those untouched places will become spoiled over time, by a thousand footprints, digital and physical. But their beauty will live on. As long as someone maintains the servers, where their memories exist as marketing collateral for lifestyle brands.