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Kubernetes and serverless technologies for high-performance applications

Kubernetes and serverless technologies for high-performance applications

Michael Hausenblas

November 06, 2018
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  1. Kubernetes and serverless technologies

    for high-performance applications
    Michael Hausenblas, Developer Advocate at Red Hat

    2018-11-06, OpenADx workshop, Stuttgart

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    admin
    SRE
    developer
    info sec
    architect
    PM
    PHB

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  3. From monoliths to functions

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    Some terminology …
    boringis.cool/#lets-talk-about-serverless
    serverless
    query

    BigQuery, Amazon Athena, etc.
    databases & datastores

    Redshift, Azure Data Lake, etc. object storage

    S3, Google Cloud Storage, etc.
    Function-as-a-Service (FaaS)

    AWS Lambda, knative, etc.
    message queues

    SQS, Azure Service Bus, etc

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    The compute evolution

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    Function-as-a-Service concept

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    Function-as-a-Service concept
    • event-driven (i.e. needs trigger)
    • short-running (practically minutes)
    • stateless (externalize state/integrations)
    • cold start characteristics very important

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    Latency vs access frequency

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    • cron job replacements
    • backup & restore jobs
    • short-running ETL jobs
    • sensor readings
    • image processing/conversion
    • bots (Slack, Twitter, etc.)

    Sources: AWS Lambda, IBM, serverless framework, 

    Contino, TNS, programmableweb.com
    Use case categories
    twitter.com/patrickdebois/status/734459440603275264

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    Use case examples
    • Migration of an email marketing tool for small business
    • Continuous Deployments
    • A ticketing system
    • The iRobots IoT service
    • Replacing cron jobs with AWS Lambda
    • Fetching nearby Pokemon Go data
    • Teletext.io
    • Calculating lineups for a fantasy game

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    Overview
    Source: landscape.cncf.io

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    AWS Lambda
    Source: aws.amazon.com/lambda

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    Azure Functions
    Source: azure.microsoft.com/en-us/documentation/articles/functions-overview/

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    Google Cloud Functions
    Source: cloud.google.com/functions/docs/

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    FaaS on Kubernetes landscape
    project backed by claim to fame started in
    Apache OpenWhisk ASF, Adobe, IBM, Red Hat large community, mature 2015
    Azure Functions Runtime Microsoft ease of use late 2017
    Dispatch VMware meta-framework late 2017
    Fission Platform9 performance 2016
    Fn Oracle supported languages/envs late 2017
    Funktion Red Hat/Fabric8 integration 2017
    Kubeless Bitnami UX, monitoring 2017
    Nuclio iguazio performance mid 2017
    OpenFaaS VMware large community, UX late 2016
    Project Riff Pivotal event-centric late 2017

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    Kubernetes
    kubernetes.io
    • Container lifecycle management
    • Declarative API + control loops
    • Robust, flexible, scalable
    • Extensible through plug-ins and
    custom resources/controllers
    Kudos to Lucas Käldström for this figure (source)

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    • Extension to Kubernetes
    • Provides low-level building blocks:
    • knative/build: pluggable model for building
    artifacts from source code
    • knative/serving: event-driven compute model
    for serving containers
    • knative/eventing: universal subscription,
    delivery and management of events (triggers)
    Knative
    Kubernetes
    service mesh & operators
    Knative
    FaaS
    Kubeless, OpenWhisk, OpenFaaS, Azure Functions, Riff,…
    github.com/knative

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    • CloudEvents (specification by the
    CNCF Serverless Working Group)
    • TriggerMesh (lifecycle management)
    • Gloo (function gateway)
    Other ecosystem components

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  19. Observability

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    Metrics
    node
    container runtime
    app
    alerts
    dashboards
    storage
    event router

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    22 kudos to demo.robustperception.io

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    23 kudos to linkerd.io/2

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    24 kudos to linkerd.io/2 and grafana.com

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    Distributed tracing and debugging
    • Roots: need to overcome limitations of “time-synced logs”
    • Specifications: OpenCensus and OpenTracing
    • Tooling: Zipkin, Jaeger, Stackdriver
    • A must-have in a microservices and/or FaaS setup

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  26. Challenges and opportunities

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    Does non-public cloud FaaS make sense?
    Most of production deployments currently using proprietary (public cloud) offerings:
    • New Relic survey 11/2017
    • TNS blog post 12/2017

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    Uptake
    thenewstack.io/serverless-roadmaps-platforms/

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    Uptake
    thenewstack.io/serverless-roadmaps-platforms/

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    Developers on call!?
    Serverless: who's on call now?

    Medium post, 2018
    ServerlessOps

    O'Reilly book, 2016

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    Handling many functions
    • A handful of functions? No problem
    • Breaking up a monolith into 200 functions? Hmmm …
    • So far, a rather under-developed space (e.g. ibm-functions/composer, TriggerMesh)

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    • Platform-level
    • Kubernetes
    • public cloud providers
    • on-premises
    • Framework level (leaks, authn/authz)
    • DoS attacks
    • Integrations/data leaks
    Security considerations

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    • Function-as-a-Service (FaaS) gives you
    • high development velocity
    • auto-scaling
    • pay-what-you-use
    • One tool in the toolbox, not a silver bullet (know your use cases)
    • Tradeoffs: convenience (lock-in) vs. control (effort)
    • Consider Function-as-a-Service as the “VBA of cloud native computing”?
    Summing up

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    • The CNCF takes steps toward serverless computing
    • CNCF Serverless Working Group
    • FaaS on Kubernetes inventory
    • The State of OpenWhisk
    • Knative: Serving your Serverless Services
    • Many-faced threats to Serverless security

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  36. plus.google.com/+RedHat
    linkedin.com/company/red-hat
    youtube.com/user/RedHatVideos
    facebook.com/redhatinc
    twitter.com/RedHatNews
    learn.openshift.com

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