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`PERF-PLAN-1.1`):
serving stale data and creating consistency bugs
destroying concurrency benefits
include patch-style diffs or clearly labeled file blocks inside the TODO.
leading to OOM crashes in production
multiplying database round-trips linearly
causing query times to grow linearly with data volume
Index Tuning)
>50ms for OLTP queries)
Zipkin) to identify cross-service latency bottlenecks
JMeter
py-spy for Python
New Relic
Profilers
Lighthouse
speed vs memory)
memory limitations
input/output requirements
not just local improvements
but strategic optimization is essential
p95
data volume