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Stabilizing platform performance while speeding up deployments

Are silent write failures corrupting your employee records under load?

About the company

An HR tech company providing a cloud-based platform used by enterprises to manage employee performance reviews, onboarding workflows, and training programmes — built on a microservices architecture with REST APIs, InfluxDB, and PostgreSQL.
Stabilizing platform performance while speeding up deployments

Industry

HR Tech, SaaS

Key challenge

Performance degradation under growing user base; no automated way to validate microservice latency and data consistency under load

Stack under test

REST APIs (microservices), InfluxDB (service-level latency metrics), PostgreSQL (transactional data)

QALIPSIS deployment

Standalone and CI/CD pipeline via Gradle plugin

Challenges

How to detect silent failures that only surface under load?

  • Thousands of concurrent users caused latency to accumulate across inter-service calls.
  • Occasional data inconsistencies in employee records surfaced after high-traffic periods.
  • Testing was manual, disconnected from CI/CD, and limited to individual service endpoints.

Solution: how QALIPSIS was used

How to simulate a company-wide review cycle?

  • API calls replicated loading review forms, submitting evaluations, and querying dashboards.
  • Stages execution profile reproduced the morning login surge when review deadlines approached.

How to correlate load tests with internal metrics?

  • InfluxDB plugin queried the platform’s own service-level latency metrics during the campaign.
  • Join operators matched each simulated API call against internal latency data points.
  • Compounding latency found: one service called two downstream services in sequence, not in parallel.
  • Fix: both calls refactored to execute concurrently; latency reduced to the slower call’s duration.

How to validate data consistency in the database?

  • Database plugin checked that every employee record created or updated was persisted correctly.
  • Silent write failures found: database lock contention caused some writes to fail without error.
  • Fix: explicit error handling added on writes; bounded back-off retry policy introduced.

How to prevent regressions through CI/CD?

  • Gradle task ran the full performance suite as part of a nightly build.
  • Campaign results published as pipeline artefacts with automated quality gates.
  • Any breach of latency or error-rate thresholds automatically failed the build.

Results

 faster deployment cycles
reduced response times
downtime during usage spikes
improved data integrity

Conclusion

Challenge

Performance degradation and silent data inconsistencies during company-wide review cycles, with no automated way to validate cross-service latency and data integrity.

Solution

QALIPSIS combined HTTP load injection with InfluxDB metrics correlation and database consistency verification in a unified campaign, embedded in CI/CD via Gradle.

Gains

35% faster API response times, 25% faster releases, zero downtime during peak reviews, and silent write failures eliminated before reaching production.

More use cases to explore

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