BRACES

A shark tracking tool using drones (not including laser equipped sharks in the first version)

Aim

• have drones patrol shark infested waters and take pictures that are sent to backend
• backend performs image analysis and if there is a suspected shark the drone is instructed to track it
• keep track of battery life of drones and plan when drones must be switched to fully charged ones

Tech

• use JS to simulate (naive) drones
• use Akka Http for F/E
• use Akka Cluster (sharding) to enable multiple drone sites (one backend should be able to support n client sites)
• use Akka Persistence to persist positions of drones/etc to survive server outages (resilience)

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Plan and to-do-list

-1. Introduction - presenters - overview of today's training

0. Overview of Reactive Applications (HE)

   • reactive manifesto
   • actor model
     • message passing
     • supervision
     • distribution
   • Power of actor model: Distributed programming

1. Start off by explaining the domain model for Braces. Walk through an architecture diagram with all pieces we want to create (HE)

2. Monitoring of system (HE) -> use sandbox and show off dashboards

3. Set up HTTP service : ping service to show how easy it is to get started (KM) -> explain the bits required : understanding of basic Akka Http building blocks -> Akka Http has actors and streams available by default -> Explain what actors short comings are and why streams is the remedy

4. Create WS connection and stream messages (KM) -> introduce Akka Streams and some of its basic functionality

   • Source
   • Sink
   • Flow
   • Graphs -> handleWebsocketMessages -> Spray JSON intro / Circe -> show how to debug stages in the flow that throws exceptions -> show a couple of FlowOps methods to simulate various scenarios (without having a connected backend) (point out similarities with Scala collections):
   • filter
   • map
   • mapConcat (flatMap)
   • mapAsync
   • takeWhile
   • dropWhile
   • throttle
   • batch
   • buffer

5. Implement clients (drones) (HE) -> connect to backend (hello, I am alive and kicking) -> send image (store locally if under back-pressure) -> send status (battery power, position, velocity, etc.) -> receive instruction

   • cover area using specific pattern
   • goto position
   • return to base
   • track item
   • send status -> simulate slow backend and show how this has effect on the clients (back-pressure in effect) -> would be cool to draw coast line and show drone positions and movement (air traffic control view) per site (Hawaii/Australia/SA)

6. Akka cluster + persistence (HE) -> clustering and persistence overview -> use cluster sharding to determine what actors to use when storing the images (each drone site has its own domain) -> real-time analysis of incoming images -> simulate back-up of the analyzing process and discuss alternative solutions, e.g. temp store (Kafka?), process and delete -> simulate shark found and send instructions to drone(s)

7. Testing (KM) -> unit testing -> integration testing (multi-jvm) -> pull the plugs/chaos monkeys in da house!

8. Show off what's coming soon -> transparent HTTP/2 -> server push -> Akka HTTP backing Play Framework -> Akka Typed (!) -> Akka Distributed Data is stable (!)

FOR THE FAILOVER: make Main be able to run on more ports so we can start a new one