How to deploy microservices: Hot topics and practical guides on the entire network in the past 10 days
With the popularity of cloud computing and DevOps, microservice architecture has become the core choice for enterprise digital transformation. This article will systematically explain the complete solution for microservice deployment based on hot technical topics across the Internet in the past 10 days, along with key data comparisons.
1. Top 5 recent hot topics in the field of microservices
| Ranking | topic | heat index | Main discussion platform |
|---|---|---|---|
| 1 | Integration of K8s and Service Mesh | 9.2 | GitHub, StackOverflow |
| 2 | Serverless microservice practice | 8.7 | AWS community, nuggets |
| 3 | Multilingual microservice communication | 8.5 | Reddit, Zhihu |
| 4 | Microservice monitoring system construction | 8.3 | CSDN、Twitter |
| 5 | Application of GitOps in microservices | 7.9 | LinkedIn, Bilibili |
2. Comparison of mainstream microservice deployment solutions
| Deployment method | Applicable scenarios | Advantages | Disadvantages | learning curve |
|---|---|---|---|---|
| Traditional virtual machine | Traditional enterprise migration | Good resource isolation | Slow startup | ★☆☆☆☆ |
| Containerization (Docker) | CI/CD pipeline | High environmental consistency | Need orchestration tools | ★★★☆☆ |
| Kubernetes | mass production environment | Automatic expansion and contraction | Complex configuration | ★★★★★ |
| Serverless | event-driven services | Zero operation and maintenance costs | Cold start problem | ★★☆☆☆ |
3. Six core steps of microservice deployment
1.Environmental preparation: Choose a deployment platform based on business scale. Small and medium-sized teams are recommended to start with Docker Swarm, and large projects directly use K8s clusters.
2.Image build: Use multi-stage construction to optimize the image size. It is recommended to use jib-maven-plugin for Java services and poetry for Python services to manage dependencies.
3.service orchestration: When using Kubernetes, pay attention to configuring a reasonable Resource Quota. It is recommended that the CPU request be set to 70% of the limit value, and 30% of the memory buffer space must be reserved.
4.Network configuration: Recent popular solutions include the service mesh architecture of Istio+Envoy. East-west traffic is encrypted by mTLS, and north-south traffic is controlled through API Gateway.
5.Data management: Each microservice has an independent database. Popular choices include PostgreSQL + sub-database middleware, or directly use NoSQL solutions such as MongoDB.
6.Monitor deployment: Prometheus+Grafana+ELK technology stack is recommended. OpenTelemetry has recently become the new standard for instrumentation.
4. Microservice deployment technology selection trends in 2023
| Technical field | emerging technologies | adoption growth rate | Representative manufacturer |
|---|---|---|---|
| service mesh | Linkerd 2.12 | 42% | Buoyant |
| API gateway | Kong 3.0 | 35% | Kong Inc |
| Service registration | Consul 1.15 | 28% | HashiCorp |
| Configuration center | Nacos 2.2 | 56% | Alibaba |
5. Typical deployment architecture examples
Recent popular projects on GitHub show that production-level microservices mostly adopt the following layered architecture:
•infrastructure layer: AWS EKS or Alibaba Cloud ACK Kubernetes cluster
•orchestration layer: ArgoCD implements GitOps continuous deployment
•runtime layer：Containerd+CNI network plug-in
•observation layer：Prometheus-Operator+AlertManager
•security layer：Aqua Security+Falco runtime protection
Conclusion:Microservice deployment requires selecting an appropriate solution based on the team's technical reserves and business characteristics. It is recommended to start with the minimum viable solution and gradually introduce advanced features such as service grid and chaos engineering. In the near future, you can focus on the Sidecar container improvements in the new features of Kubernetes 1.28, which will significantly improve service deployment efficiency.
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