DuckDB Internals: Why is DuckDB Fast?
A walk through why DuckDB, an in process analytical database, runs SQL so fast. It follows a query from parsing to execution and shows how each step avoids extra work: running as a library instead of a server cuts out network and serialization overhead, the optimizer pushes filters down and picks join order with dynamic programming, and columnar storage with zone maps lets whole chunks of data be skipped when they cannot match a filter. It also explains how a query is split into pipelines so the work can run in parallel across threads, each with its own local state. The result is a full picture of the system rather than a list of buzzwords.
There is also a second part from the series which covers how those plans actually run: vectorized execution in batches of 2048 rows, selection vectors that filter without copying, and a push based model that spreads work across CPU cores.