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A six-year legacy migration can look stable in staging and still buckle under real production traffic. In a first-person account, Senior Software Engineer Krishnankamatchi describes how short Akamai edge-cache lifetimes and frequent Next.js regeneration combined to send unnecessary work to the origin—and what the team learned while moving from a fragile, manual release process to a container-based deployment.
Why the team migrated
In the author’s account, development and staging lived in GitLab while production ran from SVN. Changes were manually compared and copied between the two, and the repositories had drifted: production contained hotfixes missing from staging, while Git held changes not present in production. Releases also required scheduled windows and downtime.
That arrangement made a basic release question difficult to answer: which version was actually in production? The team chose to move the application to AWS after discussing the deployment process. The author took responsibility for the frontend and says the first task was to trace dependencies and boundaries, not immediately rewrite everything.
What the target architecture looked like
The account describes a path from Akamai at the edge through an AWS Application Load Balancer to ECS Fargate containers. The frontend used Next.js for server-side rendering (SSR) and incremental static regeneration (ISR), alongside backend APIs and a data layer.
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This was a search-dependent platform, so migration meant preserving behavior as well as moving code. URL handling, redirects, metadata, rendering, and caching all mattered. A page that loads is not necessarily a successful migration if its URLs change, search metadata disappears, or its rendering and cache behavior overload the origin.
What went wrong in production
The author reports that frontend memory in lower environments sat around 150–200 MB, while production exceeded 2 GiB after real traffic arrived. Some dashboards appeared to show request rates in the tens of thousands per second. These are figures from the author’s incident account, not independently verified measurements or general performance benchmarks.
The team investigated whether bots or an attack explained the spike. They compared IP addresses, user agents, routes, cache hits and misses, response codes, origin request rates, and container memory. The author’s eventual explanation was not a confirmed attack but an interaction between two cache-related settings.
Short edge TTLs met frequent ISR revalidation
During migration work, Akamai edge TTLs had been shortened to make changes propagate faster. Next.js ISR revalidation was also frequent. With less time between edge-cache refreshes, more requests reached the origin; frequent ISR regeneration then caused popular pages to regenerate and fetch data more often. Together, those settings created unnecessary origin work and resource pressure.
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This is the author’s causal account. The post does not provide raw telemetry, exact TTL or revalidation values, or an independent incident report, so it should be read as a production retrospective rather than a universal diagnosis for memory spikes.
How the team responded
The team adjusted ISR revalidation and restored more appropriate edge caching. The author says traffic, memory use, and origin pressure fell afterward, but does not provide before-and-after measurements. For a similar incident, the account’s useful diagnostic approach is to follow the request across layers rather than start with a single assumption:
- Compare edge cache hits and misses with requests reaching the origin.
- Break activity down by route, user agent, IP address, and response code to look for concentrated or anomalous traffic.
- Track origin request rates alongside application logs and container memory.
- Check the interaction between CDN TTLs and application-level regeneration intervals, especially for popular pages.
Those checks help distinguish a traffic source problem from a caching configuration that is multiplying otherwise ordinary demand.
Migration lessons beyond the incident
Map dependencies before replacing the foundation
The author’s frontend work began with tracing dependencies and boundaries. That order helps reveal which services, URL rules, data paths, and rendering behaviors a replacement must preserve. For a platform that depends on search traffic, redirects and metadata are part of the migration, not finishing touches.
Test production-shaped behavior, not just lower-environment health
The gap between the reported lower-environment memory and production memory shows why a clean staging run cannot establish how a system behaves under real traffic. Load patterns, cache behavior, and popular routes can change how often work reaches application containers. Production readiness therefore depends on monitoring the path from edge to origin and on understanding what triggers page regeneration.
Make releases reproducible and recoverable
The article says the team’s cutover preparation included backups, rollback planning, database movement, DNS, load-balancer health checks, monitoring, and a temporary reduction in edge TTL. It also reports that the new container-based deployment made production builds reproducible from source. Reproducible builds address the old uncertainty of manually copying changes between diverged repositories; backups and rollback planning address the separate risk that a cutover can fail.
The author does not claim the migration removed all operational risk. The incident instead illustrates that a cleaner deployment path can still expose configuration interactions that require production monitoring and deliberate recovery plans.
What the account does—and does not—establish
Krishnankamatchi’s DEV Community account says identifying details were generalized and that the technical events and lessons are based on a real production migration. The author profile identifies the author as a Senior Software Engineer. The post provides a useful operational narrative, but not the company’s identity, architecture artifacts, raw monitoring data, precise incident timeline, or independent confirmation of the cause and resolution.
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