⚠️ Distributed System Anti-Patterns

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore common anti-patterns that can lead to failures, performance issues, and maintenance nightmares in distributed systems.

1. God Service

A single service doing too much becomes a bottleneck and hard to maintain.

• Solution: Break services into smaller, focused microservices

2. Tight Coupling Between Services

Services that rely heavily on each other reduce flexibility and increase failure risk.

• Solution: Use asynchronous communication, APIs, and events

3. Shared Database Anti-Pattern

Multiple services writing to the same database creates coordination issues.

• Solution: Each service should manage its own database; use events or sagas for consistency

4. Ignoring Failures

Not handling partial failures or retries can cause cascading issues.

• Solution: Implement fault tolerance patterns: retries, circuit breakers, bulkheads

5. Overusing Distributed Transactions

Using 2-phase commit everywhere can hurt performance and availability.

• Solution: Prefer eventual consistency and Saga patterns where possible

6. Not Monitoring or Observing

Without observability, detecting and debugging issues is slow and unreliable.

• Solution: Implement logging, metrics, tracing, and alerting

Best Practices

• Design for failure from the start
• Keep services small, decoupled, and independent
• Automate testing and monitoring
• Educate your team on distributed system pitfalls

Next in the Series

In the next post, we’ll discuss Real-Time Data Processing in distributed systems with streaming platforms like Kafka and Flink.

Label for this post: Distributed Systems