Decree 2018-062 and Kubernetes: The Gap Between the Text and the Implementation

Decree 2018-062, which implements Togo's law 2017-007 on electronic transactions, imposes obligations on security, traceability, retention and continuity. But those obligations were written with a physical server or VM in mind. Kubernetes introduces ephemeral pods, dynamic scheduling, volumes provisioned on the fly, and Operators. This article maps each obligation to its native Kubernetes equivalent, exposes the real gaps where the tool does not answer the text, and proposes implementation patterns. A legal-technical angle, not a tutorial.

Load Balancing Algorithms: Each One Fixes the Last One's Flaw

Spreading requests across N servers sounds like a one-liner: pick a server, send the request. Then one backend is slower, or bigger, or holds a session, and the naive choice falls apart. This walks the classic algorithms as a chain where each one exists to fix the previous one's blind spot: round robin, weighted, least connections, power of two choices, and consistent hashing, in Go, ending with the failure every tutorial forgets.

From RBAC to ReBAC: Migrating a Role System to OpenFGA Without Downtime

Roles work until someone says 'share just this document with just this person.' That is the day RBAC runs out of road. This is the practical migration: mapping role tables to relation tuples, running OpenFGA in shadow next to your SQL checks, backfilling safely, and only then unlocking the per-object sharing and hierarchy that roles never could. With the traps nobody warns you about.

Zanzibar Demystified: How Google Answers 'Can This User Do This?'

Authorization looks like a one-line if statement until you run it ten million times a second across every product Google ships. Zanzibar is the system that made that question fast, consistent and global. This walks from the naive permission check to relationship-based access control, the tuple model, consistency with zookies, and the open-source heirs like OpenFGA you can actually run today.

Retry Storms: How Good Clients Take Down Healthy Servers

A retry looks harmless: the request failed, so try again. Multiply that by every client, add one slow dependency, and retries turn into a self-inflicted DDoS. This walks from the naive retry loop to exponential backoff, jitter, retry budgets and circuit breakers, the caller-side half of resilience that pairs with rate limiting on the server.

Why Redis Rate Limiting Breaks at Scale (and What Uber Does Instead)

A token bucket in memory is trivial. Put it behind Redis and it works, until it doesn't. This walks through rate limiting from one node, to a shared Redis, to why that model collapses at millions of requests per second, and the shift Uber made: enforce locally, coordinate globally, and drop by probability.