Table of Contents
- 1 1. Ignoring Network Latency
- 2 2. Over-Complicating Service Boundaries
- 3 3. Inconsistent Data Management
- 4 4. Neglecting Fault Tolerance
- 5 5. Insufficient Monitoring and Observability
- 6 6. Overhead from Synchronous Communication
- 7 7. Overlooking Security Requirements
- 8 8. Lack of Proper Service Discovery
- 9 9. Data Duplication and Inconsistency
- 10 10. No Strategy for Deployment and Updates
- 11 11. Underestimating Distributed Transactions Complexity
- 12 12. Inadequate Resource Management
- 13 13. Mismanaging Configuration and Secrets
- 14 14. Failure to Handle Partial Failures
- 15 15. Underestimating Testing Complexity
- 16 16. Assuming Same Deployment and Scaling Strategies
- 17 17. Not Accounting for Data Migration
- 18 18. Overuse of Shared Libraries
- 19 19. Ignoring the Learning Curve for New Tools
- 20 20. Insufficient Load Testing
1. Ignoring Network Latency
- Pitfall: Assuming inter-service calls will be as fast as local function calls.
- Solution: Minimize network calls, use batching, and implement caching.
2. Over-Complicating Service Boundaries
- Pitfall: Splitting services too granularly without clear boundaries.
- Solution: Use domain-driven design (DDD) to define meaningful service boundaries.
3. Inconsistent Data Management
- Pitfall: Relying on synchronous data consistency across services.
- Solution: Design for eventual consistency where feasible and use proper data replication strategies.
4. Neglecting Fault Tolerance
- Pitfall: Assuming all services and network calls will always succeed.
- Solution: Implement retries, timeouts, circuit breakers, and fallback mechanisms.
5. Insufficient Monitoring and Observability
- Pitfall: Lack of visibility into how services behave and interact.
- Solution: Use logging, metrics, and distributed tracing to monitor system performance and diagnose issues.
6. Overhead from Synchronous Communication
- Pitfall: Relying heavily on synchronous, blocking service-to-service calls.
- Solution: Use asynchronous communication patterns, such as message queues or event-driven architecture.
7. Overlooking Security Requirements
- Pitfall: Assuming that security measures in a monolithic system are sufficient.
- Solution: Implement secure communication (e.g., TLS), API gateways, authentication, and authorization for each service.
8. Lack of Proper Service Discovery
- Pitfall: Hardcoding service endpoints and configurations.
- Solution: Use service discovery mechanisms (e.g., Consul, Kubernetes DNS) to dynamically resolve service locations.
9. Data Duplication and Inconsistency
- Pitfall: Copying data between services without synchronization mechanisms.
- Solution: Define ownership for data and synchronize updates through events or distributed transactions.
10. No Strategy for Deployment and Updates
- Pitfall: Deploying updates to all services simultaneously without a rollback plan.
- Solution: Use techniques like blue-green deployments and canary releases to minimize risk.
11. Underestimating Distributed Transactions Complexity
- Pitfall: Expecting distributed transactions to work like local database transactions.
- Solution: Use compensating transactions or the Saga pattern to handle multi-service workflows.
12. Inadequate Resource Management
- Pitfall: Not accounting for the increased resource overhead of distributed systems.
- Solution: Monitor and manage resource usage (CPU, memory, network) across all services.
13. Mismanaging Configuration and Secrets
- Pitfall: Storing sensitive configuration data in source code.
- Solution: Use secure secret management solutions and environment-based configuration.
14. Failure to Handle Partial Failures
- Pitfall: Assuming all components will either succeed or fail together.
- Solution: Implement error handling strategies for partial failures, such as retries and timeouts.
15. Underestimating Testing Complexity
- Pitfall: Relying on traditional unit tests without considering integration points.
- Solution: Implement integration, contract, and end-to-end testing to verify inter-service behavior.
16. Assuming Same Deployment and Scaling Strategies
- Pitfall: Deploying and scaling distributed services in the same way as a monolithic system.
- Solution: Use container orchestration tools (e.g., Kubernetes) and adopt horizontal scaling for services.
17. Not Accounting for Data Migration
- Pitfall: Failing to plan for data migration when splitting databases.
- Solution: Implement a phased migration plan, including data export/import and synchronization.
- Pitfall: Using tightly-coupled shared libraries across services.
- Solution: Prefer well-defined service contracts (e.g., API interfaces) to reduce coupling.
19. Ignoring the Learning Curve for New Tools
- Pitfall: Assuming developers can quickly adapt to new distributed tools and frameworks.
- Solution: Provide training, documentation, and gradually introduce new tools.
20. Insufficient Load Testing
- Pitfall: Not testing how the system performs under real-world load and failure scenarios.
- Solution: Conduct load and stress testing to evaluate performance and resilience under peak traffic conditions.
By being aware of these pitfalls, developers can better navigate the complexities of transitioning from monolithic to distributed systems, ensuring a smoother migration and more stable architecture.
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