Revelations!The former Huawei microservice expert wrote 500 pages of practical notes on the landing architecture, which has been open sourced

开篇

微服务治理 体系、架构及实践

• 1.2.1 Monolithic architecture and governance 2

• 1.2.2 企业SOA——EAI/ESBand governance 8

• 1.2.3 Distributed services and governance 17

• 1.2.4 Microservices and governance 23

• 1.3.1 Microservices are a research and development model 25

• 1.3.2 Architecture patterns and governance of microservices 26

• 1.3.3 研发治理 29

• 1.3.4 测试治理 32

• 1.3.5 运维治理 34

• 1.3.6 管理治理 38

• 2.1.1 代理模式 45

• 2.1.2 直连模式 46

• 2.1.3 边车模式 48

• 2.1.4 The architectural features of the direct connection mode 50

• 2.1.5 The overall architecture of the full life cycle of microservices 54

• 2.2.1 Service development quality metrics 56

• 2.2.2 Service test quality metrics 65

• 2.2.3 Service Operation Quality Metrics 68

• 2.2.4 Service online performance metrics 69

• 2.3.1 Internal control of microservices 79

• 2.3.2 Microservice lifecycle management 81

• 2.4.1 Governance indicator system 87

• 2.4.2 Governance measurement and analysis 91

• 2.4.3 Implement governance measures through management 99

• 2.4.4 微服务治理整体架构 100

• 3.1.1 点：Collection of metrics for a single request 103

• 3.1.2 线：One-minute indicator overlay statistics for a single service 104

• 3.1.3 面：Summary statistics for a single service time dimension 106

• 3.1.4 体：Aggregate analysis of service and resource indicators 106

• 3.2.1 治理目标 108

• 3.2.2 Service base view 108

• 3.2.3 Service call relationship view 111

• 3.3.1 治理目标 115

• 3.3.2 Application call relationship view 116

• 3.3.3 A unified view of application-centric operations 118

• 3.4.1 治理目标 119

• 3.4.2 Call time-consuming partition distribution statistics 120

• 3.4.3 Call time-consuming time-sharing distribution statistics 121

• 3.4.4 调用量/Time-sharing distribution statistics of concurrency 122

• 3.4.5 Performance aspect ratio 123

• 3.4.6 Performance aspect ratio 126

• 3.4.7 Comprehensive performance analysis 130

• 3.4.8 容量规划 131

• 3.4.9 动态阈值 136

• 3.4.10 趋势预测 138

• 3.5.1 治理目标 145

• 3.5.2 Real-time exception reporting 146

• 3.5.3 Anomaly distribution report 146

• 3.5.4 Exception list and query 149

• 3.5.5 故障定界定位 150

• 3.5.6 Smart root cause analysis 152

• 3.5.7 业务异常分析 155

• 3.6.1 治理目标 158

• 3.6.2 网络资源 158

• 3.6.3 数据库资源 159

• 3.6.4 其他资源 161

• 3.7.1 Service Importance Metrics 162

• 3.7.2 Service health measure 163

• 4.1.1 冗余 167

• 4.1.2 弹性伸缩 167

• 4.1.3 Single point stateless 168

• 4.1.4 不可变基础设施 168

• 4.1.5 Fault conduction blocking 169

• 4.1.6 基础设施即代码 169

• 4.2.1 随机策略 170

• 4.2.2 轮询策略 173

• 4.2.3 最近最少访问策略 176

• 4.2.4 sticky strategy 177

• 4.2.5 一致性Hash策略 178

• 4.2.6 组合策略 179

• 4.3.1 概念 180

• 4.3.2 限流模式 181

• 4.3.3 Difficulties and precautions of current limiting 186

• 4.4.1 The concept of service cluster fault tolerance 187

• 4.4.2 快速失败 188

• 4.4.3 失败安全 189

• 4.4.4 失败转移 190

• 4.4.5 失败重试 192

• 4.4.6 聚合调用 194

• 4.4.7 广播调用 197

• 4.5.1 概念 198

• 4.5.2 Shield downgrade 199

• 4.5.3 容错降级 200

• 4.5.4 Mock降级 202

• 4.5.5 熔断降级 203

• 4.5.6 延伸阅读：Generalized downgrade operation 206

• 4.6.1 自主授权 207

• 4.6.2 注册中心授权 210

• 4.6.3 Third-party service authorization 211

• 5.2.1 Google Dapper

• 5.2.2调用链跟踪的整体架构

• 5.2.3 Trace日志埋点

• 5.2.4日志采集

• 5.2.5日志收集

• 5.2.6日志存储

• 5.2.7告警

• 5.3.1Service call bottleneck analysis based on call chain tracing

• 5.3.2Service fault delimitation location based on call chain tracing

• 5.3.3From macro to microAPM的综合应用

• 5.3.4Aggregate analysis of call chains

• 5.3.5Dig deeper into the call chain potential:Monitor the health status of the business through the call chain

• 5.1Client code insertion strategy

• 5.4.2采样策略

• 5.3产品选型策略

• 6.1.1治理目标

• 6.1.2Micro-architecture governance

• 6.1.3宏观架构治理

• 6.2.1治理目标

• 6.2.2Development quality governance

• 6.2.3Test quality governance

• 6.2.4Build comprehensive commissioning capabilities

• 6.3.1治理目标

• 6.3.2多环境建设

• 6.3.3通过DevOpsProvide layer capability guarantee for microservice architecture

• 6.4.1治理目标

• 6.4.2小步快跑,高频发布

• 6.4.3Optimize collaborative management with data-driven Lean Kanban

• 6.5.1治理目标

• 6.5.2Business Metrics Collection Framework

• 6.5.3Real-time monitoring and analysis of business indicators

• 6.5.4Risk control and anti-fraud

• 6.5.5Discover business risks in a distributed architecture through data auditing

• 7.1.1功能架构

• 7.1.2系统架构

• 7.2.1使用APIThe interface interceptor collects service performance indicators

• 7.2.2使用DAOThe interceptor collects database access performance logs

• 7.2.3Periodically collect system performance indicators

• 7.2.4Custom collection of business indicators

• 7.3.1日志缓存

• 7.3.2Indicator preprocessing

• 7.3.3Timing indicators are sent

• 7.4.1基于Netty的NIO通道

• 7.4.2消息发送

• 8.2.1 NIO服务器

• 8.2.2消息接收

• 8.2.3消息处理

• 8.3.1Data are regularly summarized

• 8.3.2服务监控台

• 9.1.1整体架构

• 9.1.2技术选型

• 9.2.1服务错误TopN监控

• 9.2.2业务错误TopN监控

• 9.2.3性能最差TopN监控

• 9.2.4Most calls的是TopN监控

• 9.2.5The total resource usage is the mostTopN监控

• 9.2.6Service invocation time total ratio

• 9.3.1错误最多TopN监控

• 9.3.2性能最差TopN监控

• 9.3.3Most callsTopN监控

• 9.3.4total resource usageTopN监控

• 9.4.1One hour system load change curve

• 9.4.2一小时JVMUse the memory change graph

• 9.4.3System point-in-time indicator monitoring

• 9.4.4 JVM垃圾收集统计

• 9.5.1图表配置

• 9.5.2图表展示

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