Topic
Performance & Scale
Caching and evidence-based performance decisions for systems under real traffic.
Why Pagination Count Queries Slow Down Databases
Understand why exact totals can cost more than limited queries and when to consider slices, cached metadata, estimates, or product changes.
Separate Core Data from Like-Count Aggregates
Design scalable like-based ranking with separate aggregates, explicit freshness goals, async updates, reconciliation, and search boundaries that follow real demand.
Solve Small Problems with Proportional Engineering
Measure what existing systems can do, solve small problems proportionally, and add caches or distributed infrastructure only when evidence demands it.
Paginated Like Counts: Start with a Simple Query
Count likes for the current page with a bounded query before adding counter columns or Redis and their added management costs.
Separate Domain Learning from Technology Experiments
Use familiar tools to launch domain projects, and isolate unfamiliar infrastructure in minimal experiments with focused performance tests.
Measure Before Replacing Outbox Polling
Evaluate outbox polling with realistic load tests, then consider a simpler application-level delivery path before adopting log tailing.
Separate Shared Counts from Personalized Cache State
Compose a reaction status from reusable queries, then cache the shared count without leaking one member’s personalized state to everyone else.
Modeling Reactions from Requirements to Scale
Turn a vague like-button request into a reaction model by clarifying policy, estimating data growth, and planning when count queries must change.
Modernize Legacy Systems Gradually—and Treat Caches as Operations
Choose legacy boundaries carefully, change them in small steps, and add a shared cache only after DB work and deployment failure modes are understood.
Design Performance Tests from Real Traffic Shapes
Estimate volume and arrival patterns before choosing a load test, add deliberate headroom, and require evidence only where traffic risk justifies it.
You Can Practice High-Traffic Engineering Without Real Traffic
Build and operate a small service, create load deliberately, and show how you found and fixed bottlenecks without pretending a load test equals production experience.