Cloud Native Microservices Architecture

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The organizational match and technical carrying capacity of traditional monomer applications can be significantly reduced as the enterprise evolves from a single product line + monostructure to a multi-product line + multi-team development phase.The longer the deployment cycle, the higher the costs of regression testing, the need for minor changes to a non-core module to be reissued for the entire application— These signals suggest that the introduction of micro-service structures is no longer a “over-designed” but a necessary condition for continued efficient delivery by teams.

Typical turning point signals include: application start times of more than 30 seconds leading to a deterioration of the rolling update experience; significant variations in the frequency of changes in different business modules (core transaction modules are updated once a week and back-office management modules are likely to move only once a month), but single-body forces all modules to maintain the same release rhythm; multiple development teams collaborate on the same code warehouse to consolidate conflicts and return risks with the growth of the team size index; and a module's memory leak or death cycle can slow down the entire application, and the lack of resource isolation makes the failure radius equal to the entire system.

The scenarios described here apply to enterprises with high rates of business development, multi-team collaboration, high demand for system resilience and efficient delivery.ZhiHua TechProvide complete microservice transformation services from structured counselling to implementation of the Impromation, not representing specific client data.

Typical operational challenges

1. Delivery-efficiency bottlenecks in single-form structures

• Release a coupling:30 developers share a code warehouse and a CI/CD streaming line. Any combination of codes can block the release of others. Urgent fixes for a payment Bug, which must wait until the previous backlog of 5 MRs are all consolidated and returned to the test is completed -- these MRs have nothing to do with the payment module.
• Test explosions.: The regression test range for a single application is about the "full application". Even if SQL is modified with only one query interface, it takes 40 minutes to 2 hours to run the complete end-to-end test package. The length of the test feedback cycle slows down the iterative speed.
• Technical LockThe whole application is tied to a technology warehouse (such as Java 8+ Spring), a new business scenario that is more suitable for Go or Node.js, but the introduction of a new language means a new construction, deployment and monitoring system, and teams often choose to "work together."

2. Complexity brought about by distribution

• The network is unreliable.: The uni-body is called by method, and micro-services are network-connected. Networks overtime, unpacked, partitioned -- these failure patterns that do not exist in a single-body are routine under micro-service structures. Without reasonable timeout, re-testing and melting strategies, a service vibrates like a domino domino.
• Data consistency: ACID is the basis for single-sourced database services. Under microservices each service has its own database, and cross-service operations (the following is = order service + inventory service + payment service) must rely on distributed service programs such as Saga or TCC to ensure final consistency. This shift in thinking – from "business completion" to "compensation may be possible for every step" – is the most difficult cognitive threshold for a team to cross.
• Debugging and Deterrence: A request may cross 5-8 micro-services. When a user reports a "down order failed" you need to collide a full call chain from a gateway log, order service log, inventory service log, payment service log, without a distributed tracking (e.g. Jaeger, SkyWalking), the problem of positioning is like a needle.

3. Lack of infrastructure and mobility

• Containerization and organizationMicroservices are natural for containerization deployment, but Kubernetes itself has a steep learning curve. Pod Network, Service Discovery, Ingress Route, ConfigMap, Secret Management, HPA Resilient sprawl - concepts that are zero for traditional teams.
• CI/CD Complexity: From a stream line to a line N (one for each service), mirror construction, push, deployment, rollback requires standardization. Without a uniform stream line template and product management, the fragmentation of the delivery process can be caused by separate teams.
• ObservabilityLogging, indicators, and tracking – the three pillars are one. Under microservice structures, the absence of any one of the pillars can lead to a significant reduction in the ability to extricate.

Programme design thinking

1. Progressive split instead of Big Bang rewrite

ZhiHua Tech insisted on micro-service transformationHangler Fig Patterson• Progressively moving towards functional modules in the new architecture while maintaining the normal functioning of the old system, which coexists through route layers until the old system is completely replaced:

• First, we remove the HF change module.Priority is given to the separation of the most frequent and independent modules of the business (e.g. user centres, commodity centres). Once they are dismantled, they enjoy the dividends of independent deployment — changes are no longer limited by the pace of release of other modules.
• API Gateway Unified EntryThe gateway is responsible for route distribution, authentication of clearance, flow restriction and log log records. Request to forward the gateway to the corresponding mono or microservice by prefixing the path, with no sense of the front end.
• Database follows split: Each detached microservice has an independent database, Schema (even an example of a stand-alone database), which is ultimately consistent with the single database by synchronizing data or calling API. Dismantling in stages reduces risk by using the "Dub-Book +-Top-Top-Read" strategy.

2. Services gridding communication governance

When the number of micro-services exceeds 10, the traditional SDK service governance (SDK for each service introduced into the RPC framework) begins to expose maintenance costs - the upgrade of SDK requires all services to be re-structured and published, different SSDK services need different delivery and change of governance strategy requires code changes.

ZhiHua Tech recommends introduction after service scale has reached a certain level Service Mesh (e.g. Istio + Envoy), downside service governance capacity to Sidecar agent:

• Flow managementGrayscale release (by weight/Header/Cookie diversion), fail injection testing, request mirrors - these capabilities can be achieved through Istio's Design Rule and Vital Services configurations without the need to modify business codes.
• Secure communications: MTLS (two-way TLS authentication) is automatically enabled for inter-service communications, the issuance, rotation and revocation of certificates is automatically managed by Citadel, and the business developers do not need to perceive the bottom-up security mechanism.
• Observability: Sidecar automatically collects telemetry data (delayed, successful, error rate) for all incoming and outgoing stations, and output to Prometheus (indicator) + Jaeger (link) + ELK (log), forming a complete observable triangle.

CI/CD and Gitoops delivery line

ZhiHua Tech helps clients build a standardized CI/CD system, the core principle beingA template to cover the construction and deployment of all services：

• Waterline Templateing: All micro-services share the same set of CI/CD templates (Jenkinsfile or GitHub Actions workworkwork template), which require only a few variables (language, port, resource quotas) to be accessed. Avoid fragmentation by each team.
• Gitops Deployment: A statement of all Kubernetes resources (Deployment, Service, Ingress, ConfigMap) is stored in Git Repository, where the ArgoCD continuously monitors changes in Git Repository and synchronises them automatically to clusters. Any manual modification of a cluster is rolled back by the GitOps controller to ensure that "Git Repository = Group Real."
• Canary release: Progressive delivery through Argo Rollouts - new version of Pod deployed 5% first, observation error rate and delay 5 minutes, and normal expansion of indicator to 25% 50% and 100%. Indicators at any stage automatically trigger rollback.

Scope of system capacity

Zip, containerized base.

• Kubernetes Cluster PlanningMulti-environmental (development/test/pre-production/production) cluster architecture design, node specification selection, network plugin (Calico/Cilium) selection, storage programme (CSI) design.
• Porter mirror managementThe following are some of the examples of the recent developments in the area of mirroring: Harbor private mirror warehouse construction, mirror security scanning (Trivy), mirror thinness strategy (multistage construction, dissolacy basic mirror).
• Flexible stretching: HPA (based on CPU/RAM Pod horizontal scalding) + Cluster Autoscaler (node level scalding) + KEDA (based on event driven scalding of custom indicators such as message queue depth).

Service governance and communications

• API Gateway: Harmonize the route, limit flow, authentication, log, cross-domain processing. Supports pluginization extension to customized logic.
• Registration and Discovery of Services: Service discovery based on Kubernetes DNS + Service, managing external service metadata in conjunction with Consul/Nacos.
• Configure CenterNacos/Apollo centrally manages the environment configurations, configuration changes are sent in real time, supporting greyscale distribution and rollback.

• Observational system

• Log: Fluentd/Filebeat collect →Kafka buffer → Elasticsearch store → Kibana display. Logs are associated by TraceID.
• IndicatorsPrometheus + Grafana, covering infrastructure indicators (node/container/Pod) and application indicators (QPS/delayed/wrong/operational indicators).
• Linkage tracking: OpenTelemetry + Jaeger, displaying the complete call chain and time per jump between services requested.
• Call the police.: Alertmanager Classified Alert (emergency/warning/notification), sent via Enterprise Micro-Credits/Pertification/Flying Book, with a Grafana panel screenshot.

CI/CD Delivery

• Code repository standardization (branch policy, CODEOWNERS, Merge Request template)
• Automation, unit testing, code scan (SonarQube), mirror build delivery
• Gitoops Deployment (ArgoCD)+Cyancant/Blue Green Release Strategy

Distributional data architecture

• Database Split Policy: Split vertically by field + Split horizontally by time/ID (SharingSphere). Read and write separate (main library writing, reading from library).
• Distributional servicesThis is a small number of local newssheets based on the information received and the local newssheets.
• Cache Structure: Redis Cluster Multi-level Cache (local Cafsee + Distribution Cache + Database), Cache Aside / Writer Behind.

Deliverables

Intended value orientation

• Significant efficiency gains in deliveryThe service is independently constructed, tested and deployed, and the single service release cycle is reduced from a "weekly" to an "hourly" level. Emergency repairs are no longer blocked by other modules.
• Fault isolation and elasticity: The memory leakage of a service does not bring down the system. Automatic amplification (HPA/KEDA) ensures that there are sufficient resources under peak flow to reduce the cost of automatic resource recovery at low valleys.
• Technology warehouse freedom: Different services can select the best technology compartments from the scene, and the introduction of new technologies will no longer require full re-engineering.
• Observable coverageThe blogger says that the government has been able to “show the problem” from “no problem” to “no anomaly indicators seen on Grafana before the user complained”.