HPA in practice: why CPU-based autoscaling kept scaling at the wrong times
Three months ago, our crypto market data ingestion pipeline hit a wall. During high-volatility events, execution latency spiked from 15ms to over 800ms. O…
Read entryThree months ago, our crypto market data ingestion pipeline hit a wall. During high-volatility events, execution latency spiked from 15ms to over 800ms. O…
Read entryAt my algorithmic trading firm, our execution algorithms and alpha research pipelines depend on rapid feedback loops. If a quantitative researcher has to…
Read entryA backup is nothing more than an unverified promise. In the world of stateful Kubernetes workloads, relying on untested backups is a classic operational f…
Read entryThe worst part of building distributed execution systems is the time wasted waiting for Docker images to compile, push, pull, and deploy. When you are wri…
Read entryWhen you are routing high-frequency order flows and real-time market data enrichment requests, milliseconds are not just metrics—they are PnL. In our algo…
Read entryEighteen months ago, my team transitioned our core algorithmic execution engine from a tightly coupled Django monolith to twelve specialized microservices…
Read entryDuring a high-volatility trading session last quarter, our execution system experienced a silent failure. We were running a rolling deployment of our core…
Read entryOur algorithmic execution engine was costing us $14,800 a month in raw EC2 compute across three Kubernetes clusters. The irony was painful: our average CP…
Read entryEvery second added to a feedback loop is a tax on engineering velocity. For my algorithmic trading platform, our deployment pipeline was suffering under a…
Read entryIn algorithmic trading, "maintenance windows" are a luxury we cannot afford. When your systems route orders to global financial exchanges operating across…
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