Building distributed systems is both challenging and rewarding. Here are some key lessons I’ve learned while working on my distributed task scheduler project.

The CAP Theorem in Practice

When you’re designing a distributed system, you’ll inevitably face the CAP theorem trade-offs. In my task scheduler, I prioritized Availability and Partition tolerance over strict Consistency.

type TaskScheduler struct {
    nodes       []Node
    algorithm   SchedulingAlgorithm
    healthCheck *HealthChecker
}

func (ts *TaskScheduler) Schedule(task Task) error {
    // Find available nodes
    healthyNodes := ts.healthCheck.GetHealthyNodes()
    
    // Apply scheduling algorithm
    selectedNode := ts.algorithm.Select(healthyNodes, task)
    
    return selectedNode.Assign(task)
}

Scheduling Algorithms Matter

I implemented three different scheduling algorithms:

  1. Round-Robin: Simple and fair, but doesn’t account for node capacity
  2. FCFS (First Come, First Served): Predictable but can lead to head-of-line blocking
  3. Least-Loaded: Optimal for heterogeneous workloads

Each has its trade-offs depending on your use case.

Health Checking is Critical

Your system is only as reliable as your health checking mechanism. I learned to:

  • Use exponential backoff for retries
  • Implement circuit breakers to prevent cascade failures
  • Have multiple health check endpoints (liveness vs readiness)

What’s Next

I’m planning to explore:

  • Raft consensus for leader election
  • Better observability with distributed tracing
  • Multi-region deployment patterns

Stay tuned for more deep dives into distributed systems!