A leading healthcare platform faced increasing demand for rapid feature releases while ensuring system reliability and data integrity. As their microservices architecture scaled, test automation pipelines struggled with dynamic data management, test environment bottlenecks, and slow test execution, especially for critical healthcare workflows like patient onboarding, appointment scheduling, and claim processing.
To solve these challenges, they adopted Redis, a high-performance, in-memory data store, integrated seamlessly into their automation frameworks.
Charting the Course: Redis in Test Automation
Redis played a transformative role in multiple areas of their test automation ecosystem:
| Redis Capability | Test Automation Use Case |
|---|---|
| In-Memory Data Storage | Storing transient test data for faster retrieval |
| Pub/Sub Messaging | Enabling real-time communication between test microservices |
| Caching Test Inputs | Reducing repetitive API calls and database reads |
| Distributed Locking (Redisson) | Handling parallel test execution without data collisions |
| Queueing (Redis Streams/Lists) | Managing test execution queues across microservices |
| Session Management | Maintaining session tokens during long-running test cases |
Test Automation Projects: Challenges & Learnings
Key Challenges
✖ Test Data Bottlenecks: Static test data slowed down parallel executions.
✖ Environment Conflicts: Multiple parallel tests causing data collisions and false failures.
✖ Slow Test Suites: Frequent DB reads for transient data increased execution time.
✖ Data Cleanup Issues: Residual test data impacted subsequent runs.
Our Learnings & Solutions
✔ Used Redis Key Expiry to auto-clean test data after each run, reducing cleanup efforts.
✔ Implemented Redisson distributed locks to prevent data collisions during parallel test runs.
✔ Leveraged Redis’ high-speed caching to store user tokens, temporary IDs, and payloads for faster reuse.
✔ Introduced Pub/Sub events to synchronize test status across microservices, improving reporting.
Data Structures in Action: Health Care Project
| Redis Data Structure | Test Use Case in Health Care Project |
|---|---|
| Strings | Storing patient registration tokens |
| Hashes | Capturing claim processing details (e.g., claim ID, status, date) |
| Lists | Queueing patient appointment scheduling events |
| Sets | Managing unique insurance plan IDs during policy creation tests |
| Sorted Sets | Prioritizing test cases based on execution order |
| Streams | Capturing and replaying event-based test scenarios |
Results Achieved
| Test Automation Metric | Before Redis Adoption | After Redis Integration |
|---|---|---|
| Test Execution Time (Parallel Run) | 90 minutes | 25 minutes |
| Data Collision Rate | ~30% | < 2% |
| Test Data Preparation Time | 20 minutes | < 2 minutes |
| Test Cycle Stability | Moderate | High |
| Environment Downtime Due to Data Conflicts | Frequent | Rare |
Key Takeaways
Redis enables fast, scalable, and conflict-free test automation pipelines in distributed microservices environments.
Dynamic test data management, real-time messaging, and distributed locking unlock high-performance parallel testing.
In healthcare, where data accuracy and speed are critical, Redis helped simulate real-world transaction flows efficiently.
