How Google Built Distributed Infrastructure
Follow a set of infrastructure problems from durable storage through batch computation, coordination, structured data, and interactive analysis. GFS and MapReduce establish an early storage-and-compute pairing. Chubby shows how a relatively small coordination service supports larger systems, and Bigtable illustrates the consequences for structured storage. Dremel and Percolator then address workloads that need a different execution model from large batch jobs. The Tail at Scale closes with responsiveness across a large service. These are historical design accounts, so read their workload assumptions and measurements in context. For each system, identify what it delegates to surrounding infrastructure and what it must guarantee itself. Together the papers explain useful relationships between components rather than a current inventory of Google's production stack. Curated from collections by Dan Creswell (https://dancres.github.io/Pages/) and Fred Hébert (https://ferd.ca/a-distributed-systems-reading-list.html); commentary and reading order are original.