Posted on Sat 16 May 2026 | 30 min read
High-throughput services spend much of their time waiting on I/O. Go's runtime addresses this by multiplexing large numbers of lightweight tasks through goroutines, an internal scheduler, and coordinated synchronization primitives.
Posted on Sun 03 May 2026 | Part 5 of Building Real Trading Systems | 19 min read
Small trading systems optimize code paths. Larger ones optimize data movement. Scaling beyond Pandas means treating storage layout, ingestion, and derived datasets as part of the backtesting engine.
Posted on Sun 12 April 2026 | Part 7 of Distributed Systems in Finance | 14 min read
Schema changes don't roll out atomically in real systems. Old and new versions coexist across services, making backward compatibility and long-term correctness unavoidable constraints.
Posted on Sat 21 March 2026 | Part 6 of Distributed Systems in Finance | 26 min read
Financial streaming systems must remain correct when reality intervenes. This article dissects crash mid-window recovery, checkpoint corruption, idempotent effects and deterministic replay when failures occur.
Posted on Sat 07 March 2026 | Part 4 of Building Real Trading Systems | 20 min read
A production-ready async runtime architecture with explicit supervision and restart discipline, built to keep trading systems correct under failure, stress and load spikes.
Posted on Sat 21 February 2026 | Part 4 of DeFi Engineering | 17 min read
Step inside DeFi's hidden economy: how mempools, MEV and Flashbots turn transaction ordering into a latency-driven execution game where speed and visibility decide outcomes long before settlement.
Posted on Sat 07 February 2026 | Part 3 of DeFi Engineering | 18 min read
Blockchains are often explained through protocol-specific concepts like blocks or slots. This article reframes them as distributed state-transition systems, where ambiguity and delayed agreement exist to varying degrees across chains.
Posted on Sat 24 January 2026 | Part 5 of Distributed Systems in Finance | 17 min read
Modern trading systems rely on directed acyclic graphs (DAGs) that branch, merge, and transform real-time feeds into many parallel consumers: matching engines, risk checks, analytics, surveillance, and storage. These ingestion DAGs exist to isolate failure, control fan-out, and preserve latency and correctness under extreme market conditions.
Posted on Sat 10 January 2026 | Part 3 of Low-Latency Fundamentals | 15 min read
Low-latency systems fail when work becomes unbounded. Batching, conflation, and backpressure are mechanisms that keep systems stable under bursty, adversarial load. Without them, tail latency and cascading failures are inevitable.
Posted on Sat 13 December 2025 | Part 4 of Distributed Systems in Finance | 22 min read
How do trading systems observe themselves in real time? This article breaks down the telemetry architecture that keeps distributed systems visible under extreme latency pressure.