Four times Microservices: REST, Kubernetes, UI Integration, Async - - PowerPoint PPT Presentation

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Four times Microservices: REST, Kubernetes, UI Integration, Async - - PowerPoint PPT Presentation

Oct 03, 2023 129 likes •1.03k views

Four times Microservices: REST, Kubernetes, UI Integration, Async Eberhard Wolff @ewolff http://ewolff.com Fellow http://continuous-delivery-buch.de/ http://continuous-delivery-book.com/ http://microservices-buch.de/

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Four times Microservices: REST, Kubernetes, UI Integration, Async

Eberhard Wolff @ewolff http://ewolff.com Fellow

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http://continuous-delivery-buch.de/ http://continuous-delivery-book.com/

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http://microservices-buch.de/ http://microservices-book.com/

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http://microservices-book.com/ primer.html http://microservices-buch.de/ ueberblick.html

FREE!!!!

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What are Microservices?

> Modules providing interfaces > Processes, Containers, virtual machines

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What are Microservices?

> Independent Continuous Delivery Pipeline including tests > Standardized Operations (configuration, log analysis, tracing, monitoring, deployment) > Resilient (compensate failure, crashes …) therefore separate processes, VM, Docker containers

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Microservices = Extreme Decoupling

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Operational Complexity Extreme Decoupling

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Micro Service Micro Service Link Sync Async

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Synchronous REST Microservices

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Customer Order Catalog REST internal HTTP external

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Synchronous Microservices

> Service Discovery: IP / port? > Load Balancing: Resilience and independent scaling > Routing: Route external request to microservices > Resilience

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Synchronous Microservices with Java, Spring Boot / Cloud & the Netflix Stack

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https://github.com/ ewolff/microservice

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Service Discovery Eureka

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Why Eureka for Service Discovery?

> REST based service registry > Supports replication > Caches on the client > Resilient > Fast > Foundation for other services

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Eureka Client

> @EnableDiscoveryClient: generic > @EnableEurekaClient: specific > Dependency on spring-cloud-starter-eureka > Automatically registers application

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Eureka Server

@EnableEurekaServer @EnableAutoConfiguration public class EurekaApplication { public static void main(String[] args) { SpringApplication.run(EurekaApplication.class, args); } }

Add dependency to spring-cloud-starter-eureka-server

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Load Balancing Ribbon

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Client

Ribbon: Client Side Load Balancing

> Decentralized Load Balancing > No bottle neck > Resilient

Load Balancer Server

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Ribbon Example

private LoadBalancerClient loadBalancer; … ServiceInstance instance = loadBalancer.choose("CATALOG"); String url = "http://" + instance.getHost() + ":” + instance.getPort() + "/catalog/";

Via Dependency Injection Eureka name Need dependency to spring-cloud-starter-ribbon

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Resilience

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Timeout

> Do call in other thread pool > Won’t block request handler > Can implement timeout

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Circuit Breaker

> Called system fails -> open > Open -> do not forward call > Forward calls after a time window

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Hystrix & Spring Cloud

> Annotation based approach > Annotations of javanica libraries > Simplifies Hystrix > No commands

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@SpringBootApplication @EnableDiscoveryClient @EnableCircuitBreaker public public class class OrderApp OrderApp { public public static static void void main(String[] main(String[] args args) { ) { SpringApplication.run(OrderApp.class class, , args args); ); } }

Enable Hystrix Need spring-cloud-starter-hystrix

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@HystrixCommand(fallbackMethod = "getItemsCache", commandProperties = { @HystrixProperty(name = "circuitBreaker.requestVolumeThreshold", value = "2") } ) public public Collection<Item> Collection<Item> findAll findAll() { () { … this this.itemsCache itemsCache = = pagedResources pagedResources.getContent .getContent(); (); return return itemsCache itemsCache; } private private Collection<Item> Collection<Item> getItemsCache getItemsCache() { () { return return itemsCache itemsCache; }

Fallback

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Zuul Routing

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Routing

> One URL to the outside > Internal: Many Microservices > REST > Or HTML GUI > Hides internal structure (sort of) > Might add filters

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Customer Order Catalog Zuul Proxy

Automatically maps route to server registered on Eureka i.e. /customer/** to CUSTOMER No configuration

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Netflix Stack

> Service Discovery: Eureka > Load Balancing: Ribbon > Routing: Zuul > Resilience: Hystrix > Non-Java microservice: specific clients or sidecar

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Consul

> Service Discovery by Hashicorp > DNS interface > Platform independent > Consul Template can fill out config file templates > e.g. for load balancer > …unaware of service discovery

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https://github.com/ ewolff/microservice-consul

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Kubernetes

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https://github.com/ ewolff/microservice- kubernetes

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Kubernetes

> Docker container on a single machine: Not resilient enough > Kubernetes: Run Docker container in cluster > …and much more!

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Pods

> Kubernetes runs Pods > Pods = 1..n Docker containers > Shared volumes > …and ports

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Replica Sets

> Deployment creates replica set > Replica sets ensure that a certain number of Pods run > ...for load balancing / fail over

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Services

> Services ensure access to the Pods > DNS entry > Cluster-wide unique IP address for internal access > Node port … > … or external load balancer for external access

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Replica Set Pod Pod Service

starts DNS Cluster IP address Load Balancer

Deployment

creates

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Load Balancer

Kubernetes Cluster Server Service 1 Service 2 Kubernetes Cluster Server Service 1 Service 2

Service adds a load balancer

Load Balancer (e.g. Amazon Elastic Load Balancer)

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Kubernetes

> Service Discovery: DNS > Load Balancing: IP > Routing: Load Balancer or Node Port > Resilience: Hystrix? envoy proxy? > Can use any language

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No code dependencies on Kubernetes!

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UI Integration

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UI Integration

> Very powerful > Often overlooked > UI should be part of a microservices > Self-contained System emphasize UI > http://scs-architecture.org/

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Hyperlinks to navigate between systems. System 1 System 2

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Transclusion of content served by another application System 1 System 2

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https://www.innoq.com/ en/blog/transclusion/

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UI Integration

> Extremely decoupled > Here is a link: http://ewolff.com > No need to know anything else about the system > What about more complex transclusion?

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Server-side integration

Browser UI 1 UI 2 Frontend Server Backend 1 Backend 2

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Server-side Integration: Technologies

> ESI (Edge Side Include) > E.g. Varnish cache > SSI (Server Side Include) > E.g. Apache httpd, Nginx

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ESI (Edge Side Includes)

... <header> ... Logged in as: Ada Lovelace ... </header> ... <div> ... a lot of static content and images ... </div> ... <div> some dynamic content </div>

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ESI (Edge Side Includes)

... <esi:include src="http://example.com/header" /> ... <div> ... a lot of static content and images ... </div> ... <esi:include src="http://example.com/dynamic" /> http://www.w3.org/TR/esi-lang

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https://github.com/ ewolff/SCS-ESI

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Client-side integration

Browser UI 1 UI 2 Backend 1 Backend 2

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Client-side Integration: Code

$("a.embeddable").each(fun functi tion(i, link) { $("<div />").load(link.href, fun functi tion(data, status, xhr) { $(link).replaceWith(thi this); }); }); Replace <a href= " " class= "embeddable"> with content of document in a <div> (jQuery)

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https://github.com/ ewolff/SCS-jQuery

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https://github.com/ ewolff/crimson- insurance-demo

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Asynchronous Microservices

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Asynchronous

> Do not talk to other microservices > ...and wait for a reply > …while processing a request. > Either don‘t wait for a reply > ...or don‘t communicate while processing a request.

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Asynchronous: Benefits

> Deals with unreliable systems > Can guarantee delivery > Good fit for events > …and Bounded Context / DDD

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Order Invoice Delivery

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Kafka

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https://github.com/ ewolff/microservice-kafka

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Kafka API

> Producer API > Consumer API > Streams API (transformation)

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Kafka Records

> Key > Value > Timestamp > No headers > Stored forever (!)

Record Key Value Timestamp

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Kafka Topics

> Named > Topics have partitions > Order guaranteed per partition > Consumer commits offset per partition > Consumer group: One consumer per partition

Topic Partition Record Record Record

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Topic Partition Record Record Record Producer Offset

Offset committed per consumer

Offset Partition Record Record Record Partition Record Record Record

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Log Compaction

> Remove all but the latest record with a given key > Idea: Old events not relevant > …if new events override > Efficient storage in the long run

Partition Record id=42 Record id=23 Record id=42

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Kafka Replication

> N Replicas (configurable) > In-sync replicas (configurable): write successful if all written

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Asynchronous Microservices

> Service Discovery: via topic > Load Balancing: via partitions / consumer groups > Routing: ./. > Resilience service fails – latency increases

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Kafka Conclusion

> Provides access to old events > Guaranteed order per key > (Log compaction also per key) > Consumer groups send records to one consumer > Additional (complex) infrastructure

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REST & ATOM

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https://github.com/ ewolff/microservice-atom

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Atom

> Data format > …for feeds, blogs, podcasts etc > Access through HTTP > Idea: Provide a feeds of events / orders

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<feed xmlns="http://www.w3.org/2005/Atom"> <title>Order</title> <link rel="self" href="http://localhost:8080/feed" /> <author><name>Big Money Inc.</name> </author> <subtitle>List of all orders</subtitle> <id>https://github.com/ewolff/microservice- atom/order</id> <updated>2017-04-20T15:28:50Z</updated> <entry> ... </entry> </feed>

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<entry> <title>Order 1</title> <id>https://github.com/ewolff/microservice- atom/order/1</id> <updated>2017-04-20T15:27:58Z</updated> <content type="application/json“ src="http://localhost:8080/order/1" /> <summary>This is the order 1</summary> </entry>

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Atom Feed

> Some unneeded information > …but not a lot > Mostly links to details > ...and timestamps > Can use content negotiation to provide different data formats or details

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Subscribing to Feed

> Poll the feed (HTTP GET) > Client decides when to process new events > …but very inefficient > Lots of unchanged data

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HTTP Caching

> Client GETs feed > Server send data > + Last-Modified header > Client sends get > + If-Modified-Since header > Server: 304 (Not modified) > …or new data

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REST can be asynchronous.

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Atom

> Service Discovery: REST > Load Balancing: REST > Routing: ./. > Resilience failures just increase latency

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Atom Conclusion

> Can provides access to old events if they are stored anyway. > One consumer: idempotency > No additional infrastructure

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Conclusion

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Operational Complexity Extreme Decoupling

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Conclusion: Communication

> Netflix: Java focus, code dependencies > Kubernetes: No code dependencies > UI Integration: more decoupling > Asynchronous deals with challenges in distributed systems

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Links

> List of microservices demos (sync, async, UI): http://ewolff.com/microservices-demos.html > REST vs. Messaging for Microservices https://www.slideshare.net/ewolff/rest-vs- messaging-for-microservices

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EMail bedcon2017@ewolff.com to get: Slides + Microservices Primer + Sample Microservices Book + Sample of Continuous Delivery Book Powered by Amazon Lambda & Microservices