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Async Python Microservices with gRPC
·
Module 11
Deployment and Observability
1
Kubernetes Core Concepts: Pods, Services, Deployments, and ConfigMaps
Explain Kubernetes core concepts: Pod, Service, Deployment, and ConfigMap.
2
Deploy Microservices to Local Kubernetes
Deploy a containerized microservice to a local Kubernetes cluster (e.g., Minikube, Docker Desktop).
3
Kubernetes Rolling Deployments for Zero-Downtime Updates
Configure rolling deployments in Kubernetes for zero-downtime updates.
4
Structured JSON Logging in Python Microservices
Configure structured JSON logging in a Python microservice for easier parsing and analysis.
5
gRPC Metrics: Counting Requests and Measuring Latency
Instrument a gRPC service to generate and export basic application metrics (e.g., request count, latency).
6
Distributed Tracing Explained
Explain the concept of distributed tracing for tracking requests across multiple services.
7
Distributed Tracing: Propagating Trace Context
Implement trace context propagation across gRPC and HTTP calls to enable distributed tracing.
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Microservice Security and Resilience Patterns
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End-to-End Project: E-Commerce System