Types of caching: every layer from browser to database
Browser, CDN, reverse proxy, app memory, Redis and database: what each caching layer stores, who shares it, and how one results-day request moves through them.
Building for 100,000 students at once
37 articles
Browser, CDN, reverse proxy, app memory, Redis and database: what each caching layer stores, who shares it, and how one results-day request moves through them.
Autoscaling is a feedback loop of metrics, policies and limits. How AWS Auto Scaling groups use target tracking, warm-up, warm pools and health checks, and what doesn't scale.
Why opening database connections is expensive, how pools lend and reuse them, two-tier pooling on large systems, Django's pool options, and the settings that matter.
Restore Postgres to the second before a mistake: base backups and WAL archiving, recovery settings, pgBackRest, WAL-G and managed services, retention, and restore testing.
From transcoding and CDNs to live classes and test-day spikes: an engineer's guide to scaling video learning for thousands of students at once.
Load balancing, caching, queues, read replicas, connection pooling, autoscaling and load testing: how to build a platform that survives its busiest minute.
Scale up or scale out? The trade-offs in cost, limits and downtime, what stateless apps need, and why databases usually grow vertically first, with a worked cost example.
How the HorizontalPodAutoscaler calculates replicas, why CPU is often the wrong metric, custom metrics, HPA vs VPA vs KEDA, node autoscaling and tuning.
Session, transaction and statement pooling, what breaks in transaction mode, key pgbouncer.ini settings, PgBouncer vs Pgpool-II and managed proxies, and production tips.
Reactive autoscaling is minutes too late for a 10 a.m. mock test. How to use scheduled actions, predictive scaling and warm pools, and pre-scale databases and caches.
Size database pools, Gunicorn workers and pods from real numbers with Little's Law, see why bigger pools slow Postgres down, and follow a worked exam-day example.
What 'sorry, too many clients already' means, how to find the culprits in pg_stat_activity, quick relief during an incident, and why raising max_connections isn't the fix.
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