Reliability

Users expect systems to work. Reliability is about meeting that expectation even when the real world intrudes: a disk dies, a deploy introduces a bug, or an operator misconfigures a firewall rule. The goal is not perfection — it is building systems where individual faults do not cascade into user-visible failures.

Hardware Faults

Hard disks fail, RAM corrupts, power supplies die, and network links flap. In large fleets, failures are routine — not exceptional. Cloud platforms offer VMs with automatic restart, but that does not help if the data on disk is lost or if all replicas share the same power domain.

• Single-server redundancy (RAID, dual power supplies) reduces but does not eliminate risk.
• Distributed systems replicate data across machines so one failure does not mean data loss.
• Correlated failures (shared rack, region outage) require multi-zone or multi-region strategies.

Software Errors

Software bugs can be subtle and long-dormant. A leap second, an edge case in input validation, or a race condition under load can trigger cascading failures across services. Unlike hardware, software faults often affect many machines simultaneously because they all run the same code.

Human Errors

Studies of large outages consistently find human error near the root: a typo in a config, a migration run against production, a certificate that expired unnoticed. Good systems minimize opportunities for error and provide safety nets — staged rollouts, easy rollbacks, and thorough monitoring.

class CircuitBreaker {
  private failures = 0;
  private open = false;

  async call<T>(fn: () => Promise<T>): Promise<T> {
    if (this.open) throw new Error("Circuit open — fail fast");
    try {
      const result = await fn();
      this.failures = 0;
      return result;
    } catch (err) {
      this.failures++;
      if (this.failures >= 5) this.open = true;
      throw err;
    }
  }
}