Distributed Systems Resilience & Scalability Patterns

In distributed systems, the network is unreliable, latency is non-zero, and bandwidth is finite. Accepting these fallacies is the first step toward resilience. This paper outlines patterns that enable systems to maintain availability and consistency in the face of partial failures.

1. Revisiting the CAP Theorem

The CAP theorem dictates that a distributed data store can only provide two of the following three guarantees: Consistency, Availability, and Partition Tolerance.

Since network partitions are unavoidable in cloud environments (P), we must choose between CP (Consistency) and AP (Availability).
OmniGCloud Strategy: We often favor AP for customer-facing read paths (eventual consistency) while enforcing CP for financial transactions and configuration states (strong consistency).

2. The Circuit Breaker Pattern

Cascading failures occur when a failing service consumes resources (threads, connections) from its callers, eventually bringing them down too. A Circuit Breaker wraps a protected function call and monitors for failures.

• Closed: Standard operation. Request flows through.
• Open: Error threshold exceeded. Request fails fast without calling dependency.
• Half-Open: Trial mode. A few requests are allowed to test if dependency has recovered.

3. Bulkhead Pattern

Just as a ship is divided into watertight compartments, a system should isolate critical resources. By creating separate thread pools or connection pools for distinct services, we ensure that a failure in the "Recommendation Service" does not starve the "Checkout Service."

4. Chaos Engineering

We cannot trust a recovery mechanism until not we have seen it work. Chaos Engineering involves intentionally injecting faults (latency, packet loss, pod kills) into the system to verify resilience.

5. Idempotency & Retry Strategies

Retrying failed requests is necessary but dangerous (retry storms). Smart clients use Exponential Backoff and Jitter. Crucially, the server must support Idempotency—handling the same request multiple times without changing the result beyond the initial application.