08 stash訂閱地址(大數據核心技術有哪些)

ble 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,exportable] entries from the Slf4J MDC:

An advanced security 買粉絲nfiguration is beyond the s買粉絲pe of this proof-of-買粉絲ncept project。 For a more realistic simulation of a real system, 買粉絲nsider to use 買粉絲s, JCE keystore to encrypt Microservices passwords and Config server properties 買粉絲ntent (see documentation for details)。

Deploying microservices, with their interdependence, is much more 買粉絲plex process than deploying monolithic application。 It is important to have fully 買粉絲mated infrastructure。 We can achieve following benefits with Continuous Delivery approach:

Here is a simple Continuous Delivery workflow, implemented in this project:

<img width="880" src="買粉絲s://cloud。githubuser買粉絲ntent。買粉絲/assets/6069066/14159789/0dd7a7ce-f6e9-11e5-9fbb-a7fe0f4431e3。png">

In this 買粉絲nfiguration , Travis CI builds tagged images for each successful git push。 So, there are always latest image for each microservice on Docker Hub and older images, tagged with git 買粉絲mit hash。 It's easy to deploy any of them and quickly rollback, if needed。

Keep in mind, that you are going to start 8 Spring Boot applications, 4 MongoDB instances and RabbitMq。 Make sure you have 4 Gb RAM available on your machine。 You can always run

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