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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesAbubakar Ahmed reports that CrimeLens’s stress-test p95 latency fell from 36,640 ms to 2,800 ms after a bundle of backend and deployment changes. The result is a substantial improvement in his rerun, but it did not meet the configured two-second p95 threshold—and the final spike test still recorded 31 connection-refused errors. This is a single, author-reported case study, not a production benchmark or a guarantee that the same changes will produce the same results elsewhere.
What CrimeLens does and how it was tested
CrimeLens is a full-stack crime reporting, mapping, and analytics platform. Its workflows connect citizens, public users, police officers, and administrators; approved crime data feeds maps, statistics, trends, and geospatial searches. Ahmed describes it as a PERN modular monolith built with React, TypeScript, Node.js, Express, PostgreSQL, PostGIS, and Supabase.
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Ahmed used k6 to exercise ordinary API traffic, gradually increasing stress, sudden traffic spikes, authentication, crime-report submission, statistics and geospatial endpoints, and recovery after heavy traffic. The same k6 scripts were reused for the before-and-after runs, but the architecture and deployment setup changed between them.
What the baseline exposed
In the baseline normal-load run, overall p95 response time was 5.13 seconds. Under stress, p95 rose to 36.64 seconds and p99 to 41.61 seconds. The /api/stats/summary endpoint was one of the main bottlenecks; Ahmed also identified database connection-pool contention and request queueing.
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These findings point to different parts of the request path rather than one isolated defect: expensive or frequently repeated database work, limited connection capacity, and requests waiting behind other work. The article describes a bundle of changes, so its results cannot establish how much any individual measure contributed.
What changed between the runs
Ahmed reports changes across query execution, request handling, caching, deployment, and operational visibility. The account does not isolate a controlled experiment for each intervention.
- Database and API work: added PostgreSQL indexes, optimized frequently executed queries, improved PostGIS and geospatial query handling, and added pagination.
- Connections and caching: increased and monitored the database connection pool, and introduced Redis cache-aside caching with TTL and invalidation, plus Redis-backed rate limiting.
- Request and response handling: added request validation and sanitization, tightened Helmet and CORS settings, and enabled gzip and Brotli compression.
- Observability: added Pino structured logs and request IDs, along with Prometheus metrics and Grafana dashboards.
- Deployment and background work: containerized the application with Docker, added Nginx as a reverse proxy, tested multi-instance load balancing, and used BullMQ for Cloudinary deletion jobs.
- Delivery and security checks: added CI/CD checks involving GitHub Actions, CodeQL, npm audit, Docker scanning, and Trivy.
The useful engineering lesson is the measurement loop: establish a baseline, identify bottlenecks, choose targeted interventions, rerun comparable tests, inspect the results, and investigate what remains. Ahmed summarizes the cycle as: “Measure the existing system → identify bottlenecks → introduce targeted improvements → observe the system → rerun the same tests → compare the results -> find New Problems -> repeat”.
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The figures below are reported by Ahmed in his DEV Community article. The page displays “Posted on Sep 22” without a year; 2026 is inferred from its site footer, which shows copyright 2016–2026. They are results from Ahmed’s runs, not independently reproduced measurements.
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Normal load
| Measure | Before | After |
|---|---|---|
| Requests processed | 24,667 | 53,972 |
| Throughput | 25.5 req/s | 55.4 req/s |
| p50 latency | 1,230 ms | 25 ms |
| p95 latency | 5,130 ms | 655 ms |
| p99 latency | 7,690 ms | 1,540 ms |
| HTTP error rate | 0.10% | 0.006% |
Ahmed’s normal-load rerun reports more than twice the throughput alongside lower latency percentiles and a lower HTTP error rate. Those figures describe the tested workload and setup, not an expected result for another installation.
Stress
| Measure | Before | After |
|---|---|---|
| Requests processed | 33,106 | 289,121 |
| Throughput | 32.3 req/s | 282.2 req/s |
| p50 latency | 8,170 ms | 119 ms |
| p95 latency | 36,640 ms | 2,800 ms |
| p99 latency | 41,610 ms | 4,335 ms |
| HTTP errors | 0 | 0 |
The stress run’s final p95 of 2,800 ms remained above the configured two-second threshold. Zero HTTP errors in that run therefore does not mean every performance target was met.
Spike, recovery, and selected endpoints
| Measure | Before | After |
|---|---|---|
| Requests processed in spike/recovery scenario | 4,997 | 51,198 |
| Spike-phase p95 | 45,191 ms | 2,964 ms |
| Recovery-phase p95 | 10,370 ms | 202 ms |
/api/stats/summary p95 |
8,050 ms | 1,104 ms |
| Radius-search endpoint p95 | 2,200 ms | 583 ms |
The final spike run also recorded 31 connection-refused errors against the local Nginx/Docker entry point. Ahmed treats this as a remaining edge-availability problem to investigate, not as a resolved outcome.
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The comparison indicates that Ahmed’s full set of changes coincided with materially higher reported throughput and lower latency in the reruns. It does not show that a specific tool—Redis, Docker, Nginx, or another component—caused a particular improvement. Nor does the same-script detail make the runs identical: the final architecture differed from the baseline.
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Test conditions also limit how far the figures can be generalized. Ahmed ran the tests on a local Windows machine. The baseline backend ran locally in Node.js development mode; the final run used Docker, Nginx, Redis, and application containers. Both used a remote Supabase PostgreSQL/PostGIS free-tier database, and the dataset was relatively small. The article does not establish production-scale behavior or provide independently confirmed raw test artifacts.
For a meaningful comparison with another service or deployment, a reader would also need the hardware and runtime mode, database tier and location, dataset size, concurrency and duration, network conditions, and whether the architecture changed. Ahmed notes that production performance can vary with hosting platform, database tier, deployment region, network latency, CPU and memory, and real dataset size.
Source
Abubakar Ahmed, “Scaling CrimeLens: From Performance Baseline to Measurable Backend Improvements”, DEV Community. The measurements and implementation details in this article are attributed to Ahmed’s account.
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