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Serverless Apps on Google Cloud: more dev, less ops
The document discusses the advantages and implementation of serverless applications on Google Cloud Platform (GCP), highlighting features such as transparent scaling, reduced operational burdens, and cost efficiency. It outlines the benefits of using event-driven architectures and microservices, emphasizing the importance of minimizing deployment times and optimizing resource allocation. Additionally, it provides practical tips on managing costs, performance tuning, and tools available for building serverless applications.
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Serverless Apps on Google Cloud: more dev, less ops
- 1. Serverless Apps onGCP more dev, less ops Joseph Lust mabl engineer @lustcoder
- 2. About Me: JosephLust 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 serverlessfuture ■ Serverless solutions GCP ■ Serverless demos ■ Taming serverless @ mabl 3
- 4. startups must pushor perish 4
- 5. serverless just works ■No Provisioning ■ Transparent Scaling ■ Event Driven ■ Pay only for Use 5
- 6. it’s all aboutopportunity cost ■ Provisioning takes time ■ Boilerplate takes time ■ You’re not running a hosting company 6
- 7. how developers deploycode 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 GCPservices ■ 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 allthe 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 dofunctions cost? 12
- 13. gcf is thecheapest 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 countingfree monthly allowances 14 * Also see execution time, bandwidth, and memory costs
- 15. gcf differentiators ■ GuaranteedCPU per memory allotment ■ Easy to use UI and tools ■ Stackdriver Logging & Error Reporting Source: GCP Pricing, FB Pricing, AWS Lambda Pricing, Azure Pricing 15
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- 17. but, what aboutMicroservices? 17
- 18. but, what aboutMicro Nanoservices? 18
- 19. microservices solve manypains ■ 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 youdecouple 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
- 21. decoupled async functionspattern 21
- 22. how do youhandle 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 makeslow 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 youhandle GCP outages? ■ Durable Pubsub subscriptions ■ No microservices on fast path ▲ Backend processing ▲ Keep the UI and lights on via Firebase 24
- 25. request costs roundup ■ 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 requestcosts ■ 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 requestcosts 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
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- 29. maximizing performance ■ Tuneyour 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’sthis cost you? 30
- 31. track everything, frombits 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
- 32. Real Production Numbers@ mabl 32
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- 35. ■ scaling tozero 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 tozero 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 ■ Labelfunctions 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>” ...
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- 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
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- 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
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