Skyrocketing Micronaut microservices into Google Cloud

Instead of spending too much time on infrastructure, take advantage of readily available serverless solutions. Focus on your Micronaut code, and deploy it rapidly as a function, an application, or within a container, on Google Cloud Platform, with Cloud Functions, App Engine, or Cloud Run.

In this presentation, you’ll discover the options you have to deploy your Micronaut applications and services on Google Cloud. With Micronaut Launch, it’s easy to get started with a template project, and with a few tweaks, you can then push your code to production.

Thanks to its performance, its low memory consumption, and its lightning-fast startup time, Micronaut is particularly well-suited for services that run on serverless solutions.

Transcript

1. Skyrocketing
   Micronaut
   µ-services in
   Google
   Cloud
   Guillaume Laforge
   Google Cloud
   Developer Advocate
   @glaforge
   Java Champion
   Apache Groovy co-founder
   Member of the Micronaut
   technical advisory board
   

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2. Chapter 1
   —
   Micronaut,
   a microservices
   & serverless
   focused framework
   

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3. Micronaut is...
   A microservices & serverless
   focused framework
   A complete application framework
   for all kinds of apps
   An annotation processor
   that hooks into your compiler
   to wire everything at compile time
   The results of lessons learned
   building Spring and Grails
   

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4. You can build...
   ● Microservices
   ● Serverless applications
   ● Message-driven applications
   with Kafka or RabbitMQ
   ● Command-line tools
   ● Android mobile applications
   ● Anything with a static void main
   ● In Java, Groovy or Kotlin
   

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5. Features at a glance
   ● Familiar annotation-driven
   programming model
   ● Non-blocking Netty HTTP server
   ● Declarative reactive compile-time
   HTTP clients
   ● Cloud native (service discovery,
   distributed tracing, cloud support)
   ● Easy data-access layer
   ● Instantaneously spin up
   servers & clients for unit tests
   ● Open API to expose your APIs
   

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8. Problem with current frameworks
   Classical application frameworks
   offers lots of productivity thanks to
   annotation-based programming models…
   But make extensive use of techniques bad for
   memory consumption and startup performance:
   ● Reflection
   ● Runtime bytecode generation
   ● Runtime generated proxies
   ● Dynamic class loading
   

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9. Java’s own problems
   ● Limited annotation API
   ● Type erasure
   ● Slow reflection
   ● Reflective data caches
   ● Classpath scanning
   ● Slow dynamic class loading
   

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10. Micronaut ❤ GraalVM
    Increase app throughput by 20-30%
    and reduce latency
    Faster startup time (up to 20ms)
    Lower memory consumption (as low as 18MB)
    Lightweight self-contained native binaries
    (great for cloud deployment)
    Less work to configure native image, as Micronaut
    eliminates: reflection, runtime proxies, runtime
    bytecode generation, and dynamic class loading
    

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11. Chapter 2
    —
    Serverless solutions on
    Google Cloud Platform
    

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12. cloud.google.com/
    about/locations
    A Big Global Network of Data Centers
    

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13. Panorama
    Machine Learning
    Vertex AI Platform
    Vision API
    Video Intelligence API
    Speech API
    Translate API
    NLP API
    ...
    Compute
    Compute Engine
    App Engine
    Kubernetes Engine
    Container Registry
    Cloud Run
    Cloud Functions
    ...
    Networking
    Cloud Virtual Network
    Cloud Load Balancing
    Cloud CDN
    Cloud Interconnect
    Cloud DNS
    ...
    Storage & Databases
    Cloud Storage
    Cloud Bigtable
    Cloud Firestore
    Cloud SQL
    Cloud Spanner
    ...
    Big Data
    BigQuery
    Cloud Dataflow
    Cloud Dataproc
    Cloud Dataprep
    Cloud Datalab
    Cloud Pub/Sub
    Genomics
    ...
    Management
    Monitoring
    Logging
    Error Reporting
    Debugger
    Deployment Manager
    ...
    Developer
    Cloud SDK
    Cloud Source
    Repositories
    Cloud Endpoints
    IDE plugins
    Cloud Test Lab
    Cloud Build
    ...
    Identity & Security
    Cloud IAM
    Cloud IAP
    Cloud KMS
    Cloud Secret Manager
    Cloud Resource
    Manager
    Cloud Security Scanner
    Cloud Platform Security
    ...
    

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14. The compute spectrum
    FaaS
    App platform
    Container
    Virtual machine
    Dedicated server
    Physical
    hardware
    Virtual
    hardware
    Operation
    system
    Application
    runtime
    Application
    Function
    Virtual
    hardware
    Operation
    system
    Application
    runtime
    Application
    Function
    Operation
    system
    Application
    runtime
    Application
    Function
    Application
    Function Function
    Serverless Zone
    

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15. The serverless models and characteristics
    Operational
    Model
    Programming
    Model
    No Infra Management Managed Security Pay only for usage
    Service-based Event-driven Stateless
    

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16. Serverless compute options on Google Cloud
    Cloud Functions
    Serverless functions
    App Engine
    Web applications and APIs
    Cloud Run
    Containerized HTTP workloads
    Source-based deployments
    of event-driven functions
    Source and compiled deployments
    of web apps and web API backends
    Containerized applications serving HTTP
    requests, fully managed or on GKE clusters
    

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17. Cloud Functions
    

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18. Cloud Functions
    Cloud services “glue”
    Backend code that
    reacts to events
    ● Background events
    ● HTTP events
    Use cases
    ● Integrating with 3rd-party
    services & APIs
    ● Webhooks
    ● IoT backends
    ● Lightweight real-time
    data processing
    ● Virtual assistants & chatbots
    Available runtimes
    ● Node.JS
    ● Python
    ● Go
    ● Java
    ● .Net
    ● Ruby
    

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19. The cloud “glue” — Functions event sources
    Cloud Storage
    Cloud Pub/Sub
    HTTPS
    Firebase
    Cloud Scheduler
    Cloud Tasks
    

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20. ✅ Good fit? — ❌ Constraints?
    ✅ Good fit
    ● Easy to deploy bits of code
    ● React to Pub/Sub & Storage events
    ● Nice for webhooks handlers
    or other asynchronous operations
    ● Don’t want to think about runtime env.
    ● Lightweight data transformation (ETL)
    ● Cloud-based HTTP glue & webhooks
    ❌ Constraints
    ● Limited set of runtime languages
    ● Function granularity might be too small
    ● Must interact via events
    ● No custom domain (not out of the box)
    ● Concurrency level of 1 (more cold starts)
    ● Not for user-facing APIs or web fronts
    

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21. App Engine
    

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22. App Engine Standard
    Available language runtimes:
    ● Python 2.7 / 3.7 / 3.8 / 3.9
    ● Java 8 / 11
    ● Node.js 10 / 12 / 14
    ● PHP 5.5 / 7.2 / 7.3 / 7.4
    ● Ruby 2.5 / 2.6 / 2.7
    ● Go 1.11 / 1.12 / 1.13 / 1.14 / 1.15
    Features:
    ● Multiple services, multiple versions
    ● Traffic splitting
    (A/B testing, blue/green, canary deploym.)
    ● Concurrency (80 concurrent requests)
    ● Custom domains
    ● Built-in CDN for static asset serving
    ● Batteries included
    (Memcache, Datastore…)
    

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23. ✅ Good fit? — ❌ Constraints?
    ✅ Good fit
    ● For stateless applications
    ● For HTTP request/response workloads
    ● Scaling high traffic, down to zero
    (good enough for Snapchat!)
    ● Built-in CDN static asset serving
    ● Traffic splitting
    (across services & versions)
    ❌ Constraints
    ● Must use one of the available runtimes
    ● No support for WebSockets, HTTP/2 &
    gRPC streaming, Server-Sent Events
    

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24. Cloud Run
    

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25. Containers
    Any language Rich ecosystem
    Any library of base images
    Any binary
    .js .rb .go
    .py .sh …
    0 1 0
    1 0 0
    1 1 1
    Containers
    Flexibility
    Serverless
    Velocity
    

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26. Serverless containers with Knative and Cloud Run
    Cloud Run
    Fully managed, deploy your
    workloads and don’t see the
    cluster.
    Cloud Run on Anthos
    Deploy into Anthos, run
    serverless side-by-side with
    your existing workloads.
    Knative everywhere
    Use the same APIs and
    tooling anywhere you run
    Kubernetes with Knative.
    

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27. Container contract & resources
    ● Listen on 0.0.0.0 on port $PORT
    (default 8080)
    ● HTTP server must start < 4 min
    (timeout → 504)
    ● Request time < 15 min
    (default → 5 min, up to 60 min)
    ● Stateless
    (in-memory file system)
    ● Computation only within request
    (No background activity)
    ● 2 vCPU per container instance
    (configurable to 4 vCPU)
    ● 256 MiB of memory up to a max of 8 GiB
    (configurable, soon 16 GiB)
    ● 250 concurrent requests per container
    (configurable 1-1000)
    ● 100 max containers by default
    (configurable 1-1000, support overridable)
    ● Access to a Metadata Server
    ● Sandboxed by gVisor
    

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28. ✅ Good fit? — ❌ Constraints?
    ✅ Good fit
    ● For stateless workloads
    ● For HTTP request / response
    ● Supports WebSockets, gRPC,
    Server-Sent events
    ● Scale up quickly, and down to zero
    ● Specific runtime needs
    (language, dependencies, binary…)
    ● Custom domains
    ❌ Constraints
    ● Must use containers, adds complexity
    ● Decide on build process
    (Cloud Build, Cloud Native Buildpacks…)
    ● No built-in CDN, or Memcache
    

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29. Chapter 3
    —
    Deploy Micronaut on
    Cloud Functions,
    App Engine,
    & Cloud Run
    

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30. Demonstrations
    HTTP and
    background functions
    with Micronaut
    Micronaut web application
    on App Engine
    (also GraalVM native image)
    Containerized Micronaut
    services on Cloud Run
    (also GraalVM native image)
    

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31. Thank you and Q & A
    Thank you
    Questions & Answers
    Guillaume Laforge
    @glaforge
    

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