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Choreography vs Orchestration in serverless microservices (short version)

Choreography vs Orchestration in serverless microservices (short version)

We went from a single monolith to a set of microservices that are small, lightweight, and easy to implement. Microservices enable reusability, make it easier to change and scale apps on demand but they also introduce new problems. How do microservices interact with each other toward a common goal? How do you figure out what went wrong when a business process composed of several microservices fails? Should there be a central orchestrator controlling all interactions between services or should each service work independently, in a loosely coupled way, and only interact through shared events? In this talk, we’ll explore the Choreography vs Orchestration question and see demos of some of the tools that can help.

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Mete Atamel

April 20, 2021
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  1. Choreography vs Orchestration in serverless microservices Mete Atamel Developer Advocate

    at Google @meteatamel atamel.dev speakerdeck.com/meteatamel
  2. Choreography vs Orchestration

  3. Imagine a simple e-commerce transaction Services calling each other directly

    Frontend App Engine Order request Payment Processor Cloud Run Authorize & charge CC Shipper Cloud Functions Prepare & ship items Notifier Cloud Run Notify user
  4. Simple REST: Pros and Cons Pros ➕ Better than a

    single monolith ➕ Easy to implement: Services simply call each other Cons ➖ Too much coupling ➖ Each service can be a single point of failure ➖ Each service needs its own error / retry / timeout logic ➖ Who ensures the whole transaction is successful?
  5. Choreography (event-driven) Event-driven services Frontend App Engine Order request Payment

    Processor Cloud Run Authorize & charge CC Shipper Cloud Functions Prepare & ship items Notifier Cloud Run Notify user Message Broker
  6. Choreography: Pros and Cons Pros ➕ Services are loosely coupled

    ➕ Services can be changed/scaled independently ➕ No single point of failure ➕ Events are useful to extend the system Cons ➖ Difficult to monitor ➖ Errors / retries / timeouts are hard ➖ The business flow is not captured explicitly ➖ Who ensures the whole transaction is successful?
  7. Imagine a more complex transaction

  8. Orchestration Orchestrated services Frontend App Engine Order request Payment Processor

    Cloud Run Authorize & charge CC Shipper Cloud Functions Prepare & ship items Notifier Cloud Run Notify user Orchestrator
  9. Orchestration: Pros and Cons Pros ➕ Business flow captured centrally

    and source controlled ➕ Each step can be monitored ➕ Errors / retries / timeouts are centralized ➕ Use simple REST, no need for events ➕ Services are still independent Cons ➖ A new orchestrator service to learn and maintain ➖ Orchestrator could be a single point of failure ➖ Loss of eventing flexibility
  10. Choreography or Orchestration?

  11. It depends... Choreography Services are not closely related Services can

    exist in different bounded contexts Orchestration Can you describe the business logic in a flow chart? Are services closely related in a bounded context? Do you want to stay in REST?
  12. Hybrid approach Orchestrated bounded contexts communicating via events Orchestrated Bounded

    Context Message Broker Orchestrated Bounded Context Orchestrated Bounded Context
  13. Landscape

  14. Choreography (event-driven) AWS: SQS, SNS, EventBridge Azure: Event Grid, Event

    Hubs, Service Bus Google Cloud: Pub/Sub, Eventarc Other: Kafka, Pulsar, Solace PubSub+, RabbitMQ, NATS...
  15. Orchestration AWS: Step Functions Azure: Logic Apps Google Cloud: Workflows,

    Cloud Composer Other: Apache Airflow
  16. Serverless Workflow Specification serverlessworkflow.io A sandbox-level project at CNCF for

    a specification Defines a declarative and domain-specific workflow language for orchestrating events and services
  17. AWS Step Functions Azure Logic Apps Workflows Organization AWS Microsoft

    Google Data serialization JSON JSON JSON and YAML Workflow metadata structure Yes? Yes? No? Named steps Yes Yes Yes Basic jumps Yes Yes Yes Step execution states Yes? Yes? No? Workflow execution state Yes Yes Yes Conditional jumps Yes Yes Yes HTTP Requests No Yes Yes Runtime parameters Yes Yes Yes Static variables definitions Yes Yes Yes Pass variable between steps Yes Yes Yes Data types supported JSON type String,Int,Double,Bool,Array,Ob ject,SecureString,SecureObject String, Int, Double, Bool, Array, Map/Object,Null Data type definition Implicit (JSON) Implicit Implicit JSON -> Dictionary Yes Yes Yes Arrays Yes Yes Yes Dictionaries Yes Yes Yes Array iterations Yes Yes Yes Parallel Array iteration Yes Yes No Built-in for(i =0;i<10;i++) Yes Yes Preview Parallel step execution Yes Yes No
  18. Subworkflows No Yes Yes Expressions - math operators Yes Yes

    Yes Expressions - string functions Yes Yes Yes Expressions - other functions (SHA..) No Yes No Embedded JS No Yes No Environment Variables No Yes Limited Callbacks Yes Yes Preview Secrets No Yes Yes Bash commands No No No Delay Yes Yes Yes OnError Yes Yes Yes Retry Yes Yes Yes Return output Yes Yes Yes Input / output from a local file No No No echo to stdout No No No Build and run docker container steps No No No Open source runtime No No No User Interface - visualization Yes Yes Yes User Interface - visual editing Yes Yes No Connectors library Some Yes Yes AWS Step Functions Azure Logic Apps Workflows
  19. Orchestration: Google Cloud Workflows

  20. Serverless Compute External API’s Google API’s etc... Workflows - orchestrate

    & integrate SaaS API’s Private API’s Other Clouds
  21. - processPayment: params: [paymentDetails] call: http.post args: url: https://payment-processor.run.app/... body:

    input: ${paymentDetails} result: processResult - shipItems: call: http.post args: url: https://.../cloudfunctions.net/ship body: input: ${processResult.body} result: shipResult - notifyUser: call: http.post ... Payment Processor Cloud Run Authorize & charge CC Notifier Cloud Run Notify user Shipper Cloud Functions Prepare & ship items YAML or JSON syntax
  22. Payment Processor Cloud Run Authorize & charge CC Notifier Cloud

    Run Notify user Shipper Cloud Functions Prepare & ship items Payment Processor Cloud Run Authorize & charge CC Notifier Cloud Run Notify user Shipper Cloud Functions Prepare & ship items WAIT Payment Processor Cloud Run Authorize & charge CC Notifier Cloud Run Notify user Shipper Cloud Functions Prepare & ship items shipmentDetails userDetails Step Sequencing Serverless Pause Variable passing JSON Parsing Steps
  23. Errors and retries Payment Processor Cloud Run Authorize & charge

    CC Notifier Cloud Run Notify user Shipper Cloud Functions Prepare & ship items MAX: 5 times BACKOFF Payment Processor Cloud Run Authorize & charge CC Notifier Cloud Run Notify user Shipper Cloud Functions Prepare & ship items Pager Cloud Run Escalate to support SUCCESS ERROR Configurable retries Configurable exception handling
  24. Conditionals and 3rd party calls Notifier Cloud Run Notify user

    Shipper Cloud Functions Prepare & ship items Pager Cloud Run Escalate to support SUCCESS ERROR Out of Stock? No Request from the supplier Yes Read inventory Inventory DB Update inventory Inventory DB Supplier API
  25. Other useful features Subworkflows to encapsulate common reusable flows Connectors

    ßeta to connect to other Google Cloud services & APIs
  26. Deploy, execute, manage workflows # Deploy a workflow gcloud workflows

    deploy my-workflow --source=workflow.yaml # Execute a workflow gcloud workflows execute my-workflow # See the result gcloud workflows executions describe <your-execution-id> --workflow my-workflow
  27. None
  28. Case study: Pic-a-daily, A microservice-based picture sharing application

  29. Pic-a-daily: A photo sharing application g.co/codelabs/serverless-workshop

  30. Choreographed (event-driven) architecture

  31. 3 different event formats

  32. Orchestrated architecture

  33. Lessons learned

  34. Lessons Learned ➕ Simple REST was refreshing (vs. 3 eventing

    formats) ➕ Less code (eg. no event parsing, no Image Analysis & Garbage Collector functions) ➕ Less setup (eg. no Pub/Sub, no Scheduler, no Eventarc) ➕ Easier error handling (eg. the whole chain stops on error)
  35. Lessons Learned ➖ New service to learn with its quirks

    and limitations ➖ Code vs. YAML, a single YAML file ➖ Debugging / testing / logging, no IDE support ➖ Loss of parallelism & eventing flexibility
  36. Cloud Workflows cloud.google.com/workflows Pic-a-Daily Serverless Workshop g.co/codelabs/serverless-workshop Pic-a-Daily Code github.com/GoogleCloudPlatform/serverless-photosharing-workshop

    Thank you Mete Atamel @meteatamel atamel.dev speakerdeck.com/meteatamel