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Async Python Microservices with gRPC
·
Module 2
Distributed Systems Fundamentals
1
Distributed System Challenges: Latency & Partial Failure
Explain network latency and partial failure as inherent challenges in distributed systems.
Understanding the CAP Theorem
State the CAP theorem and explain its implications for system design choices between consistency, availability, and partition tolerance.
Strong vs. Eventual Consistency
Differentiate between strong consistency and eventual consistency with practical examples (e.g., banking vs. social media feed).
4
Idempotent API Design for Robust Retries
Design idempotent API endpoints to handle duplicate requests safely during network retries.
5
Python Client Retry with Exponential Backoff
Implement a retry strategy with exponential backoff for transient network failures in a Python client.
6
Timeout and Retry Strategies for Resilient Services
Analyze how service timeouts interact with retries to prevent cascading failures.
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From Monolith to Microservices
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Microservice Design and Decomposition