specialized in Google Cloud
- Scale code
- Scale teams
(unless you know what you are doing)
it means you understand the tradeoffs
What does it mean to scale
village (100+) -> city (10k+) -> megacity (1m+)
same same but different
When to scale
If NO, not a scaling problem.
👉 scaling “people” problems.
For a startup with a product,
a serverless architecture
is a really good place to start (UYKWYAD)
It is serverless the same way WiFi is wireless. At some point, it will hit a wire.—
" we should build our own X "
where "X" is anything not related to the business
Example: framework, in-memory database, queue system, new programming language, etc.
We could build it
spending time on the business
makes more sense financially
Developer time (build)
Use a product (buy)
On premise: fixed price and capacity
Cloud: pay for what you use
latency versus throughput
cold start ?
Should we use kubernetes ?
The questions people ask me the most
after “can I get a free subscription?”
Containers: lightweight VMs
- 12 factor app
- easier deploy
- reproducible build
- Scaling up and down
- Scheduling / Orchestration
- Service Discovery
- Rolling out and back
- Health checks
- Secret and config
- Dependency visualisation
- Service identity and Auth
- Circuit breaking
- Traffic flow and policies
- Fault injection
➡️ ️ use code?
Logging and microservices
Don’t do it
in Distributed System, logging is not debugging
💸 : # app x $ (network + storage) x rentention day
- Logging (immutable) event. (Selfish traces)
- Metrics just statistics over time
- Tracing traces provide context in the life of a transaction
They help to narrow down a problem, they will guide you where to investigate.
- Site Reliability engineering
- Chaos/Resilience engineering
- ⚠️ Languages proliferation
code containing business logic
all the rest
Total cost of ownership
build OR buy
Restaurants buy, cook and sell food.
Very few do farming and even less are good at both.
What is missing ?
The real cloud lock-in.
Level of Access
Going IPO ?
What is missing ?
- Add more CPU/RAM
- Optimize queries
- READ replica
- Spanner: transactions at scale
- BigQuery: analytics at scale
- Data Studio: data visualization at scale
- AI at scale: AI platform / kubeflow
- Cloud Asset Inventory / Forseti
- Hiring & culture
- Learning new skills
- Communication tools & process
- Management & clear objectives
- A tool won’t fix a bad process!
- Automating a bad process makes it automatically worse
- Time is money: build vs buy
- Document architecture decisions
- Microservices are meant to ship your organization charts
- Know your bottlenecks
- Securing managing access from the beginning
and I'm sorry 🙏
If you had to maintain my code
I hope you learned more by maintaining it
than me by writing it
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