A scalability playbook for VPs of Engineering whose platform is hitting limits - caching, async, partitioning, and the targeted database surgery that buys orders of magnitude of headroom without a year-long rebuild.
Most read-heavy workloads have 60 to 80 percent of traffic that could be cache-served. Listing pages, search results, comparable property analyses - the largest reductions land here, with the lowest risk.
Synchronous request paths that include slow operations are scalability killers. We move slow operations off the request path - email sending, search indexing, analytics events, third-party API calls - so the user-facing latency stops being the slowest dependency in the chain.
Single-tenant databases at scale eventually hit single-machine limits. Partitioning by tenant, by region, or by entity ID buys orders of magnitude of headroom and clears the path for the next 5x without a rebuild.
Most read-heavy workloads have 60 to 80 percent of traffic that could be cache-served. Listing pages, search results, comparable property analyses.
Synchronous request paths that include slow operations are scalability killers. We move slow operations off the request path - email sending, search indexing, analytics events, third-party API calls.
Single-tenant databases at scale eventually hit single-machine limits. Partitioning by tenant, by region, or by entity ID buys orders of magnitude of headroom.
Capture production traffic profiles and replay them at multiples of normal volume. Run live migrations using logical replication or dual-write patterns so the partitioning lands without downtime.
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