Building Micro-Services with Scala
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The document discusses the transition from traditional monolithic architecture to micro-services, highlighting issues such as dependency collisions and scalability. Micro-services are defined as independently deployable services that communicate through uniform interfaces, enabling lower-risk deployments and flexibility in technology choices. It also covers technical aspects like non-blocking servers, the use of Docker for packaging micro-services, and orchestration tools for managing containerized applications.
Building Micro-Services with Scala
- 1. Building Micro-Services with Scala Lior Shapsa, Yardena Meymann September 22, 2014 © 2014 VMware Inc. All rights reserved.
- 4. Problems with Monoliths • Size • Integration (Conway’s law) • Modules Change at Different Rates • Dependencies Collisions • Scalability requirements of modules FEAR OF CHANGE FEAR OF INNOVATION
- 6. Thought Leaders Netflix, Amazon, LinkedIn, Google, ThoughtWorks, Gilt, … Fred George Martin Fowler Kit Colbert Ben Fathi Stefan Tilkov VMware
- 7. What are micro-services? A set of narrowly focused,
- 8. What are micro-services? A set of narrowly focused, independently deployable services,
- 9. What are micro-services? A set of narrowly focused, independently deployable services, talking via uniform interfaces
- 10. What are micro-services? A set of narrowly focused, independently deployable services, talking via uniform interfaces
- 11. Micro-services • Each running in its own process • Communicating with lightweight mechanisms, often an HTTP resource API • Built around business capabilities • Independently deployable – fully automated deployment • May be in a different programming language and use different data storage technologies.
- 12. Micro-services DEPLOYING A CHANGE IS LOW RISK
- 13. #NonBlocking
- 14. Threaded vs. Evented Servers • Monolithic – Most modules communicate in-process • Micro-services – communicate remotely, often over the network – need to support cheap, lightweight remote communication • Threaded servers use thread per connection • Evented servers use non-blocking IO and callbacks – Netty – Node.js – Play – Spray
- 15. services vs. seconds http://www.eecs.berkeley.edu/~rcs/research/interactive_latency.html https://www.youtube.com/watch?v=1-vcErOPofQ
- 16. Threaded servers Service A Service B Service N
- 17. Threaded servers Service A Service B Service N Thread Thread Thread
- 18. Threaded servers Service A Service B Service N Thread Thread Thread Thread Thread Thread
- 19. Threaded servers Service A Service B Service N Thread Thread Thread Thread Thread Thread Thread Thread Thread
- 20. Thread Service A Service B Service N Thread Thread Threaded servers Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread
- 21. Thread Service A Service B Service N Thread Thread Threaded servers Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread Thread
- 22. #NonBlocking – Evented servers Service A Service B Service N Thread Thread Thread callback callback
- 23. #NonBlocking – Play/Spray • Based on Akka using Netty – Non-blocking programming much easier • Easy deployment – Embedded Server • Rich JSON and HTTP support • Plugins – Secure Social – Caching – …. • SCALA!
- 24. #NonBlocking – Play example …
- 25. #AKKA
- 27. Spray Node Dispatch Crawling library Netty Price Engine Head Actor … Pages Level1 Level1 Level2 … Level2 File writer Head Actor … k/v Node Akka Cluster curl http://AkkaCluster/site_dot_com?op=start
- 28. #AKKA • Based on the Actor Model – http://www.reversim.com/2014/04/summit-2014-scale-up-your-thinking.html • Resilience • Location Transparency – Cluster sharding – Cluster clients • Load Balancing • Message Queue Integration
- 29. #Containers
- 30. What is ? • Dockers are like lightweight VM for a single process – Self contained – No hypervisor – Shared kernel, but Isolated and content agnostic – No lib conflict – No OS boot time – Distributing a change is easy, using δ • A Clean, Safe, Isolated and Portable Micro Service
- 31. Docker File • Docker Image is Built From a DockerFile • Consists of Descriptive Set of Instructions – Start from a base image – Run and command – Add a file or directory – Create an environment variable – What process to run on launch
- 32. SBT • Assemble “Dockerized” micro services • We started with sbt-native-packager – Poor Docker functionality • We ended up with sbt-docker plugin – https://github.com/marcuslonnberg/sbt-docker – Still Using sbt-native-packager to package – Ends up with Docker file
- 35. #Containers – Using SBT
- 36. #Containers – Using SBT • Using Sequences to optimize – Reduce build time – Smaller Containers
- 37. #Containers – Orchestration • CoreOS – Linux for massive server deployments – Cluster management with Fleet – Service discovery with etcd • Evaluating – Kubernetes – Flynn – Consul – Ambassadord
- 38. #AKKA Summary #NonBloc king #Containers
- 42. Lior Shapsa lshapsa@gmail.com Twitter: @liorshapsa Yardena Meymann ymeymann@gmail.com Twitter: @ymeymann
Editor's Notes
- #29: Messaging – Kafka Resilience - Handles Failures with Supervision
- #38: We write unit files that tells the CoreOS fleet scheduler how to deploy our Docker containers. We will use the etcd key-value store to communicate between our application containers.
- #40: Logs Monitoring
- #41: Logs Monitoring
- #42: Logs Monitoring