Columnar layout is no longer just a database concern. Quant engines are using it directly for OHLCV storage because indicator kernels benefit from predictable access patterns and contiguous memory.
Why It Helps
Indicators often consume one or two fields at a time over long ranges. Storing opens, highs, lows, closes, and volumes in separate contiguous buffers reduces cache waste and enables simpler SIMD-friendly loops.
Design Consequences
Once bar data becomes columnar, downstream APIs start changing as well. Engines expose spans or views into typed buffers rather than row objects, and strategies adapt to a more data-oriented style.
Broader Pattern
The influence of Arrow-style thinking on quant infrastructure is growing. Expect more engines to describe their internals in data-layout terms, not only in strategy-language terms.
