Database Performance and Scaling
The data-systems material favors workload-specific design over universal database rankings. It repeatedly connects performance to data layout, execution model, access pattern, and operational boundaries such as pool contention, replication, or migration safety.
Synthesis
- High-cardinality and analytical workloads foreground schema design, sorted keys, sparse or skipping indexes, batching, vectorization, and reduced data movement. [src] [src] [src]
- The OpenAI PostgreSQL account shows a complementary operational pattern: a single write primary, broad read replication, sharding only for appropriate writes, pooling, and isolation of workloads. [src]
- Shopify’s reservation migration is a reminder that the observed bottleneck can sit outside the query itself; attribution, connection-hold time, shadow writes, and gradual cutover were part of the performance result. [src]
Interpretation boundary
Reported speedups are source- and benchmark-specific. Reuse their mechanisms and measurement methods, not their headline multipliers.