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.

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

16. Some quick demos
16

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

21. decoupled async functions pattern
21

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

32. Real Production Numbers @ mabl
32

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>”
...

38. so, in conclusion
38

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

40. (TL;DR) embrace serverless to
40

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

42. Questions?