D: a Practitioner's Perspective - Productivity _and_ Performance
summary
Argues that D is practical for finance: prototypes compile fast with DMD, production uses GCC/LLVM back ends, and processed tens of GB without choking where Python hit limits. Built a market-data stack using HDF5 and nanomsg, used optional GC plus Andre’s STD experimental allocator for low‑latency paths, and claims 0.35s JSON parsing for a million records. Notes growing adoption—daily compiler downloads rising and sociomantic $200M acquisition plus a D foundation—so tooling and libraries are improving.
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