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Going Microserverless on Google Cloud @ mabl
The document discusses the benefits and challenges of using Google Cloud Functions (GCF) for serverless microservices, highlighting its event-driven architecture, cost-effectiveness, and ease of deployment. It emphasizes strategies for optimizing performance, handling microservice updates, and managing costs through effective resource tracking and event management. Ultimately, it encourages adopting GCF microserverless architecture for improved scalability, cohesion, and operational efficiency.
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Going Microserverless on Google Cloud @ mabl
- 1. Microserverless on GCP millionsof requests, dozens of dollars Joseph Lust mabl engineer @lustcoder
- 2. overview ■ Our serverlessfuture ■ Serverless microservices on GCP & GCF ■ Serverless Pain ■ Taming GCF microservices @ mabl 2
- 3. startups must pushor perish 3
- 4. serverless just works ■No Provisioning ■ Transparent Scaling ■ Event Driven ■ Pay only for Use 4
- 5. how developers deploycode 2014 2007 2013 VPS Machine Image Docker Containers war File AWS Lambda 5 2000 Container Era Serverless Era Firebase CF Serverless functions GCF 2016 2017 AF
- 6. 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 6
- 7. exports.myService = function(event){ console.log(‘hello world’); } mvf
- 8. exports.myService = ()=> console.log(‘hello world’); mvf es6+
- 9. but what dofunctions cost? 9
- 10. 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 10 See full details in Google Spreadsheet calculator * Also see execution time, bandwidth, and memory costs
- 11. gcf cheapest countingfree monthly allowances 11 * Also see execution time, bandwidth, and memory costs
- 12. gcf differentiators ■ Largestpossible function memory: 2 GBytes ■ Guaranteed CPU per memory allotment ■ Node first focus ■ Easy to use UI and tools ■ Stackdriver Logging & Error Reporting Source: GCP Pricing, FB Pricing, AWS Lambda Pricing, Azure Pricing 12
- 13. but, what aboutMicroservices? 13
- 14. the bane ofclassic monoliths: tomcat to fat cats ■ Large code bases ■ Larger surface area ■ Server memory bloat ■ Deploy rarely, if ever 14
- 15. microservices solve manypains ■ Minimize code size ▲ Can scale servers, not developers’ minds ■ Minimize deploy speed/code ■ Minimize blast radius ■ Decoupling, Coherence, Singleness of Purpose 15
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- 17. but, there aresome speed bumps... 17
- 18. functions are notdesigned for failure* 18 * retry
- 19. functions require totaltrust 19
- 20. blazing fast functiondeployment 20
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- 22. work arounds tothe rescue 22
- 23. how we solvedthese issues @ mabl 23
- 24. 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 24
- 25. decoupled async functionspattern 25
- 26. 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 26
- 27. how do makeslow deploys fast? ■ Multi-repo git layout ■ File change exclusion masks ■ Deploy functions in parallel ■ Harness CI & CD (e.g. Codeship) 27
- 28. how do youhandle GCG outages? ■ Durable Pubsub subscriptions ■ No microservices on fast path ▲ Backend processing ▲ Keep the UI and lights on via Firebase ■ It’s Beta ;) 28
- 29. request costs roundup ■ Requests billed per 100ms ■ Increments round up ■ Faster functions are not always cheaper ▲ 17ms → 100ms ▲ 101ms → 200ms 29 Run Time: 17ms Billed Time: 100ms Wasted Time
- 30. minimizing your requestcosts ■ Batch multiple fast work units to fill the time (e.g. 100ms chunks) 30 Work Unit: 17ms Billed Time: 100ms Work Unit: 17ms Work Unit: 17ms Work Unit: 17ms Work Unit: 17ms Wasted Time
- 31. minimizing your requestcosts 31 ■ 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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- 33. 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 33
- 34. but seriously, what’sthis cost you? 34
- 35. 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 Big Query export ▲ Easy to explore and query (demo) ▲ Easy to visualize with Data Studio (demo) 35
- 36. BigQuery and DataStudioDemo 36
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- 39. 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 39 gcloud beta functions deploy <your_fn> --labels=”app_name=<your_fn>” ...
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- 41. embrace gcf microserverless ■For low latency, event driven demands ■ For decoupled, cohesive, independent services ■ For painless deployment and auto-scaling ■ For reduced opex 41
- 42. 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 2017 CF Presentations ▲ Building the Fire!sale demo app: youtu.be/G-MBeEW92v4 ▲ FB and ML with CF: youtu.be/RdqV_N0sCpM ▲ FB CF and Testability: youtu.be/SnWwkURpwxs ▲ Data Pipelines with CF: youtu.be/guo-4IOqx2M 42 @lustcoder
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- 44. Meetup, Coming up... ■Google speakers ■ Need your great talks ■ Need your favorite topics ▲ Survey to follow 44 Thanks for coming!