The document discusses the advantages and challenges of using Google Cloud Functions (GCF) and Firebase Cloud Functions within a serverless architecture. It highlights GCF's cost-effectiveness, ease of deployment, and performance while addressing limitations such as deployment speed and configuration management issues. It also emphasizes strategies for optimizing performance and minimizing costs when utilizing serverless functions.

1. Embracing Serverless w/ Google
the Good, the Beta, and the Ugly
Joseph Lust mabl engineer @lustcoder

3. “The secret of change is to focus all of your
energy, not on fighting the old, but on building
the new.”
–Socrates

4. startups must push or perish
4

5. serverless just works
■ No Provisioning
■ Transparent Scaling
■ Event Driven
■ Pay only for Use
5

6. how developers deploy code
2014
2007 2013
VPS
Machine
Image
Docker
Containers
war File
AWS
Lambda
6
2000
Container Era
Serverless Era
Firebase
CF
Serverless functions
GCF
2016 2017
AF

7. event driven all the way
Trigger Events Firebase CF GCF
Cloud Storage Events ✔ ✔
PubSub Topics ✔ ✔
HTTP Endpoint ✔ ✔
Auth Events ✔ ✘
Database Events ✔ ✘
Google Analytics Events ✔ ✘
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8. google function offerings have different features
Features Firebase CF GCF
Stackdriver Error Reports ✔ ✔
Cloud Logging ✔ ✔
Environment Configs ✔ ✘
Code Based Wireup ✔ ✘
Rich Function Dashboard ✘ ✔
Deployment Wireup ✘ ✔
SCM Base Deploy ✘ ✔
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9. function clarity through dashboards
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10. live function health tracking
10

11. what about the competition?
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12. costs: gcf vs the competition
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13. google 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
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See full details in Google Spreadsheet calculator
* Also see execution time, bandwidth, and memory costs

14. costs vary by memory, cpu, & bandwidth
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* Also see execution time, bandwidth, and memory costs

15. gcf cheapest due to free monthly allowances
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* Also see execution time, bandwidth, and memory costs

16. gcf differentiators
■ Largest possible 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
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18. http://bit.ly/2vb2DHW
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let’s test that autoscaling. traffic please

19. but, there are some speed bumps...
19

20. the ugly: firebase cloud functions
■ All functions run as FB project Editor
■ No failure retry semantics
■ Cannot access resources cross project
■ CLI deploy requires rigid project layout
■ Automatic undeploy of all functions for non-standard layouts
■ Slow deployment ~ 5min
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21. the uglier: gcf
■ Cannot pass configs to function
■ No failure retry semantics
■ All functions run as project Editor
■ Complex multi-module deploy
▲ Impossible from SCM deploy
■ Slow deployment ~ 2min
■ Non-Deterministic Deployment Failures
■ Single Supported Region (Iowa US-Centra1)
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22. functions are not designed for failure*
22
* yet

23. functions require total trust
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24. blazing fast function deployment
24

25. lossy pubsub deployments
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26. work arounds to the rescue
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27. work around the warts
■ Load ServiceAccount key from KMS encrypted CS file
■ Cache resources between function calls
■ Use Firebase Admin lib from GCF
▲ Best of most worlds
■ Use the lowest sensible memory and timeout limits
■ Use --local-path and --include-ignored-files for multi-module deploy
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28. a few tips and tricks
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29. request costs round up
■ Requests billed per 100ms
■ Increments round up
■ Faster functions are not always cheaper
▲ 17ms → 100ms
▲ 101ms → 200ms
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Run Time:
17ms
Billed Time: 100ms
Wasted
Time

30. minimizing your request costs
■ Cache connections, tokens, services between requests
■ Minimize Memory footprint
▲ Defaults to 256MB, will 128MB do?
■ Minimize Timeout to fail fast, retry
▲ Defaults to 60s, will 30s work?
▲ Find request timeout curve, note fast fail, low likelihood
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31. minimizing your request costs
■ Batch multiple fast work units to fill the time (e.g. 100ms chunks)
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31
Work Unit:
17ms
Billed Time: 100ms
Work Unit:
17ms
Work Unit:
17ms
Work Unit:
17ms
Work Unit:
17ms
Wasted
Time

32. maximizing performance
■ 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
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33. so, in conclusion
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34. embrace serverless
■ to focus on shipping code
■ to harness NodeJS microservices
■ to power low latency experiences
■ to provision, deploy, and scale automatically
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35. google cloud serverless resources
■ FB CF Examples Repo: github.com/firebase/functions-samples
■ GCF Examples: github.com/GoogleCloudPlatform/nodejs-docs-samples
■ Jason Polites 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
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@lustcoder

36. Questions?

37. decoupled async functions pattern
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