Case Study

Load and Stress Testing of a Video Streaming Application

Problem Statement

A rapidly growing video streaming company experienced performance issues during high-traffic events, resulting in buffering, downtime, and user complaints. As the user base expanded, the existing infrastructure was unable to handle concurrent usage effectively. The company aimed to implement robust load and stress testing to validate system resilience, identify bottlenecks, and ensure seamless streaming performance under heavy demand.

Load and Stress Testing of a Video Streaming Application

Challenge

The primary challenges in executing effective load and stress testing included:

  • High Concurrency Management: Simulating tens of thousands of users streaming simultaneously at various resolutions.

  • System Bottlenecks: Pinpointing resource limits in CPU, memory, CDN, and database performance.

  • Realistic Simulation: Emulating user behavior across different devices and network conditions.

  • Auto-Scaling Validation: Testing whether infrastructure scaled correctly under demand spikes.

Error Handling: Ensuring the platform handled failures gracefully during overload scenarios.

Solution Provided

The team designed and executed a comprehensive performance testing strategy using industry-standard tools. The solution focused on:

  • Load Simulation: Using JMeter and BlazeMeter to emulate realistic streaming loads and concurrent users.

  • Stress Testing: Evaluating how the system handled unexpected surges and identifying breaking points.

  • Monitoring Integration: Leveraging Grafana and New Relic to track system health and resource usage in real-time.

  • Failure Recovery Testing: Assessing how quickly the system recovered from overload or service interruptions.

  • Infrastructure Readiness: Validating CDN performance, backend scalability, and auto-scaling configurations.

Development Steps

data-collection

Data Collection

Identified peak usage times, streaming resolutions, user flows, and content consumption patterns.

Preprocessing

Configured test environments mirroring production setup, including CDN, caching, and backend services.

execution

Model Development

Created scripts for concurrent video playback, navigation, and switching quality under varied network conditions using JMeter and Locust.

Validation

Ran tests under controlled loads to compare against baseline performance metrics and refine scenarios.

deployment-icon

Deployment

Integrated performance tests into the staging pipeline to run on-demand and as part of major release QA.

Continuous Monitoring & Improvement

Analyzed reports post-test, refined scripts, optimized infrastructure, and addressed bottlenecks in subsequent test cycles.

Results

Reduced Buffering by 50%

Optimizations from stress testing reduced stream buffering significantly during peak usage.

Increased Concurrent User Capacity

Platform stably supported 3x the prior maximum user load, handling 60,000+ streams.

Improved API Response Times

Backend API response times improved by 35% after database and CDN tuning.

Resilient Auto-Scaling

Cloud infrastructure automatically scaled to meet demand during spike simulations.

Enhanced Monitoring Visibility

Real-time dashboards empowered DevOps to detect and address issues faster.

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