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Fintech / payments · 2025
Cut checkout p99 latency 85% before a 9× launch
A payments checkout service buckled under marketing spikes. I traced the hot paths, fixed the data layer and queueing, and added SLO-driven autoscaling — so launch day was a non-event.
Reliability & performance lead (contract)Fixed-scope project7 weeksScale & Reliability →
measured outcome
−85%
p99 latency (1900ms → 280ms)
9×
launch-day traffic, 0 incidents
40k
requests/sec sustained
−38%
compute cost per transaction
// The problem
Checkout p99 latency hit 1,900ms under load and timeouts spiked during campaigns. A major launch was projected to bring 9× normal traffic, and the team had no confidence it would hold.
// The approach
client
Client
service
API gateway
rate-limit + auth
service
Checkout svc
p99 280ms
store
Redis
hot lookup cache
store
Postgres
primary + read replica
queue
Fraud queue
async + idempotent
external
Fraud svc
off critical path
Checkout request path, after — fraud check moved off the critical path.
Load-tested the real checkout path with k6 and reproduced the failure at 12k rps.
Found the bottleneck: a synchronous fraud-check call and an unindexed hot query under contention.
Moved fraud checks behind an async queue with idempotency keys and a strict timeout + fallback.
Reworked the data access path: added covering indexes, a read replica, and a Redis cache for the hot lookup.
Defined SLOs and wired autoscaling + backpressure so the service sheds load gracefully instead of falling over.
stack
GoPostgreSQLRedisKafkak6AWSTerraformOpenTelemetry
“
Halden found the actual bottleneck in days, not weeks, and left us with dashboards we still use. Launch day was the calmest one we’ve had.
Dana Whitfield, VP Engineering — Series-B payments platform
Case studies, metrics, prior companies, and testimonials in this template are illustrative and anonymized for demonstration; they do not name or represent any real client or employer and are not a guarantee of future results. Outcomes vary by system, team, and constraints.
next step
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