When Furner’s father, Steve, joined Walmart’s operations team in 1977, the retailer had just fewer than 100 stores—far less than its nearly 11,000 locations today. His son, just four years old at the time, was far too young to start bagging groceries, but old enough to remember the lessons his father passed on.
We often have new self-managed users asking us to provide recommendations around orchestration and how to scale to dozens, if not hundreds, of nodes. While technologies such as Kubernetes have made the deployment of multiple instances of stateless applications relatively simple, this pattern should, in nearly all cases, not be required for ClickHouse. Unlike other databases, which may be restricted to a machine size due to inherent limits, e.g., JVM heap size, ClickHouse was designed from the ground up to utilize the full resources of a machine. We commonly find successful deployments with ClickHouse deployed on servers with hundreds of cores, terabytes of RAM, and petabytes of disk space. Most analytical queries have a sort, filter, and aggregation stage. Each of these can be parallelized independently and will, by default, use as many threads as cores, thus utilizing the full machine resources for a query.
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