Project Name
Scalable Kafka Load Testing Framework for High-Volume Financial Messaging Systems
A large-scale communication platform required a reliable way to validate the performance, scalability, and reliability of its Kafka-based messaging ecosystem. The platform processes high-volume, batch-driven communications across multiple channels such as Email, SMS, and WhatsApp, while relying on external vendors for delivery and acknowledgment statuses.
End-to-end testing was challenging because real vendor integrations are costly, unpredictable, and unsuitable for high-load simulations. To address this, a containerized, Kubernetes-based performance testing and mock framework was designed to simulate real-world message lifecycles, acknowledgments, and delivery behaviors at scale.
- Lack of End-to-End Testing Capability: Existing testing was limited to unit or partial integration tests. True end-to-end validation was not feasible due to reliance on external vendor systems.
- High-Volume Load Simulation: The platform needed to validate performance under lakhs of messages per batch while maintaining data integrity, ordering, and consistency.
- Vendor Dependency & Cost Constraints: Using real vendors for performance testing introduced cost, rate limits, and unpredictable behavior.
- Complex Message Lifecycle Tracking: Messages go through multiple intermediate states (SEND, DELIVERY, OPEN, READ, COMPLAINT, failures, etc.), all of which need accurate simulation and persistence.
- Observability Gaps: There was no unified visibility into message throughput, latency, batch health, or status progression across services.
- Scalability & Failure Testing: The system needed to be validated against negative scenarios such as Kafka downtime, slow consumers, database unavailability, and duplicate or corrupted messages.
The solution enabled full lifecycle validation from message production to Kafka, downstream consumption, vendor-like acknowledgment simulation, persistence in databases, and real-time analytics without dependency on actual third-party providers. A generic, vendor-agnostic Kafka load testing and mock service framework was implemented using a microservices architecture deployed on Kubernetes. Key aspects of the solution include:
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Dynamic Load Generation
- Batch-wise message generation using a Python-based load testing tool (Locust).
- Configurable TPS, batch size, delays, and execution parameters.
- Unique batch IDs and payload IDs for traceability.
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Kafka-Centric Architecture
- Producers publish payloads to outbound Kafka topics.
- Mock consumers simulate vendor-side processing.
- Acknowledgment producers publish delivery statuses back to Kafka.
- Feedback consumers persist acknowledgments for reporting.
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Generic Mock Service
- Channel-agnostic (Email, SMS, WhatsApp).
- Vendor-agnostic with configurable behavior.
- Simulates real-world vendor timing, retries, delays, and failure patterns.
- Supports positive and negative scenarios (DELIVERED, UNDELIVERED, BOUNCE, REJECT, READ, COMPLAINT, etc.).
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End-to-End Status Persistence
- All lifecycle stages are recorded in a relational database.
- Maintains a full audit trail per payload and per batch.
- Supports historical and real-time analysis.
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Automated Status Scheduling
- A scheduler determines the next logical state in the message lifecycle.
- Status transitions are executed with controlled delays to mimic real delivery timelines.
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Observability & Analytics
- Integrated dashboards provide:
- Throughput and latency metrics
- Batch-level performance insights
- Message lifecycle timelines
- Status distribution and error trends
- Separate views for technical teams and business users.
- Integrated dashboards provide:
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Scalability & Reliability
- Horizontally scalable mock services using Kubernetes pod replicas.
- Log rotation and persistence across restarts.
- Designed to test resilience under component failures and degraded conditions.
The solution delivered a scalable, vendor-agnostic performance testing framework that enables reliable end-to-end validation of Kafka-based messaging systems. Simulating real-world delivery behavior without external dependencies, it improved system confidence, observability, and readiness for high-volume production workloads.
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