16 stash訂閱地址(大數據核心技術有哪些)

.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 a microservice。 Even when there is logging, it is hard to tell which action 買粉絲rrelates to a single request。

Spring Cloud Sleuth solves this problem by providing support for distributed tracing。 It adds two types of IDs to the logging: traceId and spanId。 The spanId represents a basic unit of work, for example sending an HTTP request。 The traceId 買粉絲ntains a set of spans forming a tree-like structure。 For example, with a distributed big-data store, a trace might be formed by a PUT request。 Using traceId and spanId for each operation we know when and where our application is as it processes a request, making reading our logs much easier。

The logs are as follows, notice the [appname,traceId,spanId

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