Deploying and Scaling Spring Boot Microservices to Kubernetes

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scaling microservices with message queues, spring boot and kubernetes @danielepolencic

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scalability and robustness

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10:00 pm 08:00 pm 06:00 pm 05:00 pm 03:00 pm 01:00 pm 10k req/s 7k req/s 3k req/s

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how do you deal with so much traffic? !"

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backend + frontend

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BACKEND REST API

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BACKEND REST API

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BACKEND REST API

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BACKEND REST API

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BACKEND REST API

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BACKEND REST API

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BACKEND REST API

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planning for launch

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User

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User BACKEND

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API User BACKEND BACKEND BACKEND BACKEND

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API LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND

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API LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND

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performance tests

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BACKEND

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BACKEND • Stateless • Limit: 1000 rq/s

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BACKEND • Stateless • Limit: 1000 rq/s • Stateless • Limit: 250 rq/s

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BACKEND RATIO 1:4

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ready…

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10:00 pm 08:00 pm 06:00 pm 05:00 pm 03:00 pm 01:00 pm 10k req/s 7k req/s 3k req/s NOW

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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too little, too late

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10:00 pm 08:00 pm 06:00 pm 05:00 pm 03:00 pm 01:00 pm 10k req/s 7k req/s 3k req/s NOW

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LOAD BALANCER LOAD BALANCER User User User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User User User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User User User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User User User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User User User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User User User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User User User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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game over

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nice story, but we know how to do scaling. — Smart guy

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the retailer reportedly didn't have enough servers to handle the traffic surge for the day — fortune.com

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amazon had to manually add servers to address the issue (and failed to add them fast enough) — fortune.com

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what could you improve?

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1. automatic scaling?

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2. decoupling scaling?

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND RATIO 1:4

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LOAD BALANCER LOAD BALANCER BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND User RATIO 1:4

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND RATIO 1:4

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LOAD BALANCER LOAD BALANCER User RATIO 1:2 BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND RATIO 1:2 BACKEND BACKEND BACKEND BACKEND

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3. decoupling failures?

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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LOAD BALANCER LOAD BALANCER User BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND BACKEND

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what if…

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message broker

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User BACKEND

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User BACKEND

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User BACKEND

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User BACKEND

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User BACKEND

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User BACKEND

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User BACKEND

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User BACKEND

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User BACKEND

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User BACKEND

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User BACKEND

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User BACKEND

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User BACKEND

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scale idependently

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User BACKEND

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User BACKEND BACKEND BACKEND BACKEND

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User BACKEND BACKEND BACKEND BACKEND

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can handle failures

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User BACKEND

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what about automatic scaling?

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Mesos

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1. open source

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1. open source 2. multi-cloud

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1. open source 2. multi-cloud 3. designed to scale

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scale idependently can handle failures automatic scaling

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scale idependently can handle failures automatic scaling

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scale idependently can handle failures automatic scaling

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scale idependently can handle failures automatic scaling

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sounds like a plan

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demo

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the app

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containers, containers, containers

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JAR XML +

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JAR XML JCE + +

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JAR XML JCE + + CONTAINER

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deploying apps in kubernetes

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FRONTEND FRONTEND BACKEND Incoming traffic

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FRONTEND FRONTEND BACKEND Incoming traffic

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FRONTEND FRONTEND BACKEND ACTIVEMQ Incoming traffic

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FRONTEND FRONTEND BACKEND ACTIVEMQ Incoming traffic Autoscaling Group

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FRONTEND FRONTEND Front-end service BACKEND Backend service Queue service ACTIVEMQ Incoming traffic Autoscaling Group

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FRONTEND Pod 1 FRONTEND Pod 2 Front-end service BACKEND Pod 3 Backend service ACTIVEMQ Pod 4 Incoming traffic Queue service Autoscaling Group

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FRONTEND Pod 1 FRONTEND Pod 2 Front-end service Horizontal Pod Autoscaling BACKEND Pod 3 Backend service ACTIVEMQ Pod 4 Incoming traffic Queue service

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lessons learned

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1. scaling in k8s

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2. best tetris player ever

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3. kubernetes == rest api

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thanks

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wait there's more?

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https://learnk8s.io/blog /scaling-spring-boot-microservices https://github.com/ learnk8s/spring-boot-k8s-hpa slides ⇢ http://bit.ly/ljc-k8s