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Serverless Apps on Google Cloud: more dev, less ops

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From mabl Software Engineer Joseph Lust. Serverless on GCP is a perfect match to do more dev and less ops. We discuss the many GCP serverless services used @ mabl and how they reduce both time to market and operating expenses. We focus on the nuances of Google Cloud Functions and many way to optimize your serverless apps.

Serverless Apps on Google Cloud: more dev, less ops

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Serverless Apps on GCP
more dev, less ops
Joseph Lust mabl engineer @lustcoder
About Me: Joseph Lust
Engineer
■ Building the web for two decades
■ Cloud native since 2011
■ Currently building mabl ML cloud on GCP
■ Co-organizer GDG Cloud Boston Meetup
me@joelust.com
overview
■ Our serverless future
■ Serverless solutions GCP
■ Serverless demos
■ Taming serverless @ mabl
3
startups must push or perish
4
serverless just works
■ No Provisioning
■ Transparent Scaling
■ Event Driven
■ Pay only for Use
5
it’s all about opportunity cost
■ Provisioning takes time
■ Boilerplate takes time
■ You’re not running a hosting company
6

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Serverless Apps on Google Cloud: more dev, less ops

  • 1. Serverless Apps on GCP more dev, less ops Joseph Lust mabl engineer @lustcoder
  • 2. About Me: Joseph Lust Engineer ■ Building the web for two decades ■ Cloud native since 2011 ■ Currently building mabl ML cloud on GCP ■ Co-organizer GDG Cloud Boston Meetup me@joelust.com
  • 3. overview ■ Our serverless future ■ Serverless solutions GCP ■ Serverless demos ■ Taming serverless @ mabl 3
  • 4. startups must push or perish 4
  • 5. serverless just works ■ No Provisioning ■ Transparent Scaling ■ Event Driven ■ Pay only for Use 5
  • 6. it’s all about opportunity cost ■ Provisioning takes time ■ Boilerplate takes time ■ You’re not running a hosting company 6
  • 7. how developers deploy code 2014 2007 2013 VPS Machine Image Docker Containers war File AWS Lambda 7 2000 Container Era Serverless Era Firebase CF Serverless functions GCF 2016 2017 AF
  • 8. exports.myService = (req, res) => res.send(‘hello world’); mvf es6+
  • 9. other serverlessish GCP services ■ Serverless ▲ Scaling Clusters - Kubernetes Engine ▲ Kafka - Pub/Sub ▲ Mongo/EventSource - Firebase ▲ File Server - Cloud Storage ■ Not purely serverless, but close ▲ Tomcat/Jetty - App Engine ▲ Spark Streaming - Dataflow 9 Cloud Pub/Sub Cloud Dataflow Kubernetes Engine ML Engine
  • 10. event driven all the way ■ Only run code on events ▲ Cloud Storage blob changes ▲ Pubsub message arrival ▲ HTTP endpoint requests ▲ DB/Auth/Conversion changes ■ No machines consuming 2% CPU all day ■ Very low latency operations ■ No more corn jobs! 10
  • 11. exports.imageToJPG = functions.storage.object().onFinalize( async (object) => processImage() ); example: image processor es6+
  • 12. but what do functions cost? 12
  • 13. gcf is the cheapest function provider Vendor FREE req/mo req/$ Google Cloud Functions 2M 5M Firebase Cloud Functions 2M 2.5M AWS Lambda 1M 5M Azure Functions 1M 5M 13 See full details in Google Spreadsheet calculator * Also see execution time, bandwidth, and memory costs
  • 14. gcf cheapest counting free monthly allowances 14 * Also see execution time, bandwidth, and memory costs
  • 15. gcf differentiators ■ Guaranteed CPU per memory allotment ■ Easy to use UI and tools ■ Stackdriver Logging & Error Reporting Source: GCP Pricing, FB Pricing, AWS Lambda Pricing, Azure Pricing 15
  • 17. but, what about Microservices? 17
  • 18. but, what about Micro Nanoservices? 18
  • 19. microservices solve many pains ■ Minimize code size ▲ Can scale servers, not developers’ minds ■ Minimize deploy speed/code ■ Minimize blast radius ■ Decoupling, Coherence, Singleness of Purpose 19
  • 20. how do you decouple services and handle discovery ■ We Don’t Do Discovery ■ Event Driven Queues ▲ HTTP endpoints for external services ▲ Blob change events for files ▲ Message queue events for everything else ■ No Daemons ▲ Don’t waste time spinning 20
  • 22. how do you handle changing microservice/models? ■ Versioned Payloads over the wire ▲ New service can handle older versions ■ Add Consumers, then Suppliers ■ Upgrade, don’t replace ■ Lazy read repair ▲ Pessimistic data upgrade and transform ▲ Upgrade path from old version 22
  • 23. how do make slow deploys fast? ■ Multi-repo git layout ■ File change exclusion masks ■ Deploy functions in parallel ■ Harness CI & CD (e.g. Cloud Build) 23
  • 24. how do you handle GCP outages? ■ Durable Pubsub subscriptions ■ No microservices on fast path ▲ Backend processing ▲ Keep the UI and lights on via Firebase 24
  • 25. request costs round up ■ Requests billed per 100ms ■ Increments round up ■ Faster functions are not always cheaper ▲ 17ms → 100ms ▲ 101ms → 200ms 25 Run Time: 17ms Billed Time: 100ms Wasted Time
  • 26. minimizing your request costs ■ Batch multiple fast work units to fill the time (e.g. 100ms chunks) 26 Work Unit: 17ms Billed Time: 100ms Work Unit: 17ms Work Unit: 17ms Work Unit: 17ms Work Unit: 17ms Wasted Time
  • 27. minimizing your request costs 27 ■ Use a middle man to shield file events from high memory loads ▲ Filter/light weight checks in middle man ▲ Send heavy lifting to larger function ■ Different inputs to same service have different resource needs ▲ e.g. 90% requests need 128MB ram, 10% need 512GB ▲ e.g. 80% requests complete in 30s, 20s take 2min
  • 28. 28
  • 29. maximizing performance ■ Tune your CPU & Memory to the job ▲ Profile your microservices - CPU or memory bound? ■ More memory runs faster ▲ Greater CPU dedicated to larger memory allocation functions ▲ 128MB → 200 MHz ▲ 2048MB → 2.4GHz ▲ CPU/Memory costs increase linearly ■ Cache expensive objects between requests ▲ PubSub connections ▲ Firebase Admin instances ▲ Login tokens 29
  • 30. but seriously, what’s this cost you? 30
  • 31. track everything, from bits to cents ■ Metrics for every execution ▲ Every Byte of memory use ▲ Every Processor Cycle of runtime ▲ Every Byte of egress ▲ Every Invocation ■ Direct Billing to BigQuery export ▲ Easy to explore and query ▲ Easy to visualize with Data Studio 31
  • 33. 33
  • 34. 34
  • 35. ■ scaling to zero no longer important ■ you have a constant load ▲ Dedicated instance when to move from Serverless to Servers 35 $ Load (rps) Cloud Function Dedicated n1-standard-1
  • 36. ■ scaling to zero no longer important ■ you have dynamic load ▲ Kubernetes Deployment ▲ Knative function ▲ App Engine Service when to move from Serverless to Servers 36 $ Load (rps) Cloud FunctionKubernetes Deployment
  • 37. billing tips ■ Label functions on deploy ▲ Labels propagated to billing ▲ Can track Memory, CPU, Invocation, Ingress, Egress costs per function ■ Don’t forget the egress bandwidth ▲ Keep your data stores in the same region (Iowa) ▲ GCF only deployable to Iowa 37 gcloud beta functions deploy <your_fn> --labels=”app_name=<your_fn>” ...
  • 39. embrace serverless for ■ For low latency, event driven demands ■ For decoupled, cohesive, independent services ■ For painless deployment and auto-scaling ■ For reduced opex and oppex 39
  • 41. gcp serverless resources ■ FB CF Examples Repo: github.com/firebase/functions-samples ■ GCF Examples: github.com/GoogleCloudPlatform/nodejs-docs-samples ■ Jason Polites (great) Examples: github.com/jasonpolites/gcf-recipes ■ I/O 2018 CF Presentations ▲ Get Started Building Serverless Applications: youtu.be/JenJQ6gc14U ▲ Serverless on Google Cloud: youtu.be/iPQUdb0kssE ▲ Serverless Compute on Google Cloud: youtu.be/Y1sRy0Q2qig ▲ CI/CD in a Serverless World: youtu.be/iyGHW4UQ_Ts ▲ Building and Deploying Microservices: youtu.be/oALEthV9z_U 41 @lustcoder