Empowering Scalability: A Backend Engineer’s Self-Study Journey to Cost-Effective Auto-Scaling

As a backend engineer driven by the desire to optimize performance while mindful of budget constraints, my recent side project involved creating a scalable web application hosted on a Virtual Private Server (VPS). The journey not only enabled me to implement an efficient auto-scaling model but also showcased the advantages of self-study, cost savings, and reducing reliance on cloud services.

Dockerization for Portability and Consistency

The first step in my journey was to containerize the application using Docker. This provided portability, allowing me to run the application consistently across various environments. With a well-defined Dockerfile and Docker Compose configurations, the application’s dependencies and settings were encapsulated, ensuring a consistent deployment process.

version: '3'
services:
  web:
    image: my-web-app:latest
    ports:
      - "8080:80"
    deploy:
      replicas: 3  # Initial number of replicas

Self-Study: A Key Driver

Embracing a self-study approach allowed me to delve into the intricacies of containerization and orchestration. By understanding Docker fundamentals, I gained insights into resource management, efficient deployment, and improved collaboration within development teams. This hands-on learning not only deepened my technical expertise but also empowered me to make informed decisions tailored to the project’s unique requirements.

Auto-Scaling on a Budget

One of the standout benefits of this self-study approach was the cost-effectiveness of the auto-scaling solution. Instead of relying on cloud-based auto-scaling services that often come with a price tag, I crafted a custom bash script (scale.sh) to monitor and adjust the number of replicas based on CPU usage. This not only saved on cloud service costs but also allowed for a more tailored and budget-friendly solution.

#!/bin/bash

# Define scaling conditions based on metrics (e.g., CPU usage)
SCALE_UP_THRESHOLD=80
SCALE_DOWN_THRESHOLD=20

# Get current CPU usage
CPU_USAGE=$(docker stats --format "{{.CPUPerc}}" $(docker ps --format "{{.Names}}" | grep "my-web-service") | awk -F. '{print $1}')

# Scale up if CPU usage is above the threshold
if [ "$CPU_USAGE" -gt "$SCALE_UP_THRESHOLD" ]; then
  docker service scale my-web-service=$(($(docker service inspect --format='{{.Spec.Mode.Replicated.Replicas}}' my-web-service) + 1))
  echo "$(date): Scaling up due to high CPU usage: $CPU_USAGE%" >> scaling.log
fi

# Scale down if CPU usage is below the threshold
if [ "$CPU_USAGE" -lt "$SCALE_DOWN_THRESHOLD" ]; then
  docker service scale my-web-service=$(($(docker service inspect --format='{{.Spec.Mode.Replicated.Replicas}}' my-web-service) - 1))
  echo "$(date): Scaling down due to low CPU usage: $CPU_USAGE%" >> scaling.log
fi

Monitoring on Your Terms

Incorporating Prometheus and Grafana for monitoring provided a cost-effective alternative to cloud-based monitoring services. Docker Compose orchestrated these monitoring tools, giving me the flexibility to customize dashboards and gain insights into crucial performance metrics without the need for a third-party cloud service.

version: '3'
services:
  prometheus:
    image: prom/prometheus
    volumes:
      - ./prometheus:/etc/prometheus
    ports:
      - "9090:9090"

  grafana:
    image: grafana/grafana
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin
    ports:
      - "3000:3000"
    depends_on:
      - prometheus

Limiting Dependency on Cloud Services

By opting for a VPS and self-hosted solutions, I limited the project’s dependency on external cloud services. This not only contributed to cost savings but also provided a more hands-on, in-depth understanding of the infrastructure’s inner workings. The self-sufficiency achieved through this approach empowered me to make informed decisions based on the project’s unique needs.

Testing the Auto-Scaling Script

Ensuring the reliability of the auto-scaling script is crucial for the smooth operation of your application. Here, I’ll outline how I approached testing to verify that the script performs as expected.

1. Unit Testing:

Before deploying the script in a live environment, I conducted unit testing to validate its core functionality. This involved creating a test environment with a simulated workload to mimic varying CPU usage scenarios.

Sample Unit Test Script (test_scale.sh):

#!/bin/bash

# Mock CPU usage for testing
mock_cpu_usage() {
  echo "50"  # Adjust as needed for different scenarios
}

# Source the auto-scaling script
source scale.sh

# Set the CPU usage to simulate a scenario
CPU_USAGE=$(mock_cpu_usage)

# Test scaling up
SCALE_UP_THRESHOLD=80
SCALE_DOWN_THRESHOLD=20

echo "Testing scaling up..."
$CPU_USAGE=85
./scale.sh
# Validate that the script scaled up the replicas appropriately

# Test scaling down
echo "Testing scaling down..."
$CPU_USAGE=15
./scale.sh
# Validate that the script scaled down the replicas appropriately

echo "Unit tests completed successfully!"

2. Integration Testing:

Integration testing involved deploying the application and the auto-scaling script in a controlled environment that closely resembled the production setup. This allowed me to observe how the script interacted with the Docker services in a more realistic scenario.

Sample Integration Test Script (integration_test.sh):

#!/bin/bash

# Set up the test environment
# Deploy your application using Docker Compose
docker-compose up -d
docker-compose -f docker-compose-monitoring.yml up -d

# Source the auto-scaling script
source scale.sh

# Monitor the logs to observe scaling activities
tail -f scaling.log &

# Trigger the script manually or wait for the cron job to execute

# Observe the logs and verify that scaling activities are logged correctly

# Clean up the test environment
docker-compose down
docker-compose -f docker-compose-monitoring.yml down

echo "Integration tests completed successfully!"

3. Continuous Integration (CI) Pipeline:

To automate testing in my development workflow, I integrated the script testing into a continuous integration pipeline. This pipeline would execute the unit and integration tests whenever changes were pushed to the version control repository. This ensured that any modifications to the script were thoroughly tested before deployment.

By incorporating these testing practices, I gained confidence in the reliability of the auto-scaling script. Regular testing helped catch potential issues early in the development process, reducing the risk of disruptions in the production environment.

Conclusion

In conclusion, this self-study journey not only delivered a scalable auto-scaling solution but also brought to light the numerous benefits of self-guided learning, cost-effective infrastructure choices, and reduced reliance on cloud services. The amalgamation of Docker, a custom auto-scaling script, and self-hosted monitoring tools showcased the power of hands-on knowledge acquisition and resourcefulness in optimizing backend infrastructure.

As a backend engineer, embracing self-study not only paves the way for technical proficiency but also opens avenues for cost-effective, tailored solutions that align with project goals. This journey serves as a testament to the potential that lies in a proactive, self-driven approach to backend engineering.