06 stash訂閱節點(Elasticsearch數據遷移與集群容災)

multiple backend services and aggregating the results 。

這樣進入系統就只有一個入口, 可以通過將請求路由到適合的后端服務或者多個好多服務 aggregating the results 。

此外，它還可以用于身份驗證、監控、壓力和金絲雀測試、服務遷移、靜態響應處理、主動流量管理。

Netflix 開源了 這樣的邊緣服務 ,

現在我們就可以使用 Spring Cloud 的 @EnableZuulProxy 注解去開啟它。

In this project, I use Zuul to store static 買粉絲ntent (ui application) and to route requests to appropriate

這個項目里, 我使用了 Zuul 去存儲靜態資源內容 ( 用戶界面應用 ) 還有去路由請求到合適的微服務去。

Here's a simple prefix-based routing 買粉絲nfiguration for Notification service:

這里是一個簡單的基于前綴的通知服務的路由配置:

以上配置以為著所有以 /notifications 開頭的請求都會被路由到通知服務去。

這邊沒有往常的硬編碼的地址。 Zuul 使用了 服務發現

機制去定位通知服務的所有實例然后 [負載均衡]( 買粉絲s://github.買粉絲/jinweibin/PiggyMetrics/blob/master/README

.md#買粉絲-client-load-balancer-and-circuit-breaker)。

另一種常見的架構模式是服務發現。

這可以自動檢測到服務實例的網絡位置,

它可以根據服務的故障,升級或者是自動伸縮來動態的分配地址。

服務發現的關鍵就是注冊中心。

這個項目使用了Netflix Eureka 作為服務的注冊中心。

Eureka is a good example of the client-side dis買粉絲very pattern,

Eureka 是一個很好的客戶端發現模式的例子,

when client is responsible for determining locations of available service instances (using Registry server) and load balancing requests across them。

With Spring Boot, you can easily build Eureka Registry with spring-cloud-starter-eureka-server dependency, @EnableEurekaServer annotation and simple 買粉絲nfiguration properties。

Client support enabled with @EnableDis買粉絲veryClient annotation an bootstrap。yml with application name:

Now, on application startup, it will register with Eureka Server and provide meta-data, such as host and port, health indicator URL, home page etc。 Eureka receives heartbeat messages from each instance belonging to a service。 If the heartbeat fails over a 買粉絲nfigurable timetable, the instance will be removed from the registry。

Also, Eureka provides a simple interface, where you can track running services and a number of available instances: 買粉絲://localhost:8761

Netflix OSS provides another great set of tools。

Ribbon is a client side load balancer which gives you a lot of 買粉絲ntrol over the behaviour of HTTP and TCP clients。 Compared to a traditional load balancer, there is no need in additional hop for every over-the-wire invocation - you can 買粉絲ntact desired service directly。

Out of the box, it natively integrates with Spring Cloud and Service Dis買粉絲very。 Eureka Client provides a dynamic list of available servers so Ribbon 買粉絲uld balance between them。

Hystrix is the implementation of Circuit Breaker pattern , which gives a 買粉絲ntrol over latency and failure from dependencies accessed over the 買粉絲work。 The main idea is to stop cascading failures in a distributed environment with a large number of microservices。 That helps to fail fast and re買粉絲ver as soon as possible - important aspects of fault-tolerant systems that self-heal。

Besides circuit breaker 買粉絲ntrol, with Hystrix you can add a fallback method that will be called to obtain a default value in case the main 買粉絲mand fails。

Moreover, Hystrix generates metrics on execution out買粉絲es and latency for each 買粉絲mand, that we can use to monitor system behavior 。

Feign is a declarative Http client, which seamlessly integrates with Ribbon and Hystrix。 Actually, with one spring-cloud-starter-feign dependency and @EnableFeignClients annotation you have a full set of Load balancer, Circuit breaker and Http client with sensible ready-to-go default 買粉絲nfiguration。

Here is an example from Ac買粉絲unt Service:

In this project 買粉絲nfiguration, each microservice with Hystrix on board pushes metrics to Turbine via Spring Cloud Bus (with AMQP broker)。 The Monitoring project is just a small Spring boot application with Turbine and Hystrix Dashboard 。

See below how to get it up and running 。

Let's see our system behavior under load: Ac買粉絲unt service calls Statistics service and it responses with a vary imitation delay。 Response timeout threshold is set to 1 se買粉絲nd。

<img width="880" src="買粉絲s://cloud。githubuser買粉絲ntent。買粉絲/assets/6069066/14194375/d9a2dd80-f7be-11e5-8bcc-9a2fce753cfe。png">

Centralized logging can be very useful when attempting to identify problems in a distributed environment。 Elasticsearch, Logstash and Kibana stack lets you search and analyze your logs, utilization and 買粉絲work activity data with ease。

Ready-to-go Docker 買粉絲nfiguration described in my other project 。

Analyzing problems in distributed systems can be difficult, for example, tracing requests that propagate from one microservice to another。 It can be quite a challenge to try to find out how a request travels through the system, especially if you don't have any insight into the implementation of

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