A brief introduction to consistency models, and how they apply to your business.

1. Reasoning about data and consistency in systems
Daniel Norman
CTO, güdTECH
unba.se contributor
Twitter: @DreamingInCode

2. Caveat emptor!
There is no silver bullet.

3. TL;DR
● Systems model the physical world
● Don’t annoy the humans
● Many of our systems are global
● We have to be available 24x7x365
● Mind-bending conceptual models of systems
● A view of a plausibly modern system
● No refunds

4. ● Create a model of reality.
● Solve problems by performing computations thereon.
● Profit!
What are we trying to achieve?

5. Humans have certain expectations
We don’t like it when weird stuff happens – They get irritated.
● Well, do I have a new message or not?
● Why didn’t that save? Oh, it did save? Arghhh!
● Why is this so slow?
● What kind of lousy product is this?
● This should always be available, time is money!

6. Consistency model:
“A set of all histories of operations allowable under a system”1
In other words:
A contract between the programmer (or agent) and the system, which
provides a set of invariants to which the system will conform.
1. https://aphyr.com/posts/313-strong-consistency-models

7. ● Linearizable
● Serializable
● Sequential
● Causal
● Eventual
● PRAM
● Read Your Writes
● Repeatable Read
● Monotonic Write
● Monotonic Read
● Write Follows Reads...
Some common consistency models

8. Linearizable Consistency

9. Eventual Consistency

10. Casual ConsistencyCausal Consistency

11. Reality is Causal

12. Hey, why not use wallclock?
I can use my system clock (AKA wallclock) to order my operations, right?
No.
Definitely NOT.
● NTP is notoriously unreliable
● Bad news: simultaneity isn’t actually a thing
● Time is actually weird and lumpy ( ask a physicist )

13. Advantages and Disadvantages
● Linearizable Strongly consistent, Single POV, May entail patience.
● Serializable Almost a single POV, allows modest concurrency, patience still required.
● Sequential Concurrent writers go nuts, ordering is arbitrary though, patience required for reads.
● Eventual Concurrent writers and no patience required! It’ll get applied, no promises when.
● Causal No patience required! No waiting for readers or writers, but no single POV either.

14. What consistency models do we really use?
“Linearizability / Serializability, obviously. End of presentation.”

15. But actually...

16. But Wait!
AWS MAGIC CASTLE TECHNOLOGY TO THE RESCUE!
Not so fast – AWS is pretty good, but we must still
reason about their consistency models:
● S3 Read after Write
● DynamoDB Eventual
● SQS Sequential or Linearizable
● Aurora / RDS Serializable

17. ● FIFO makes my life easier
● It works around packet loss
● We’re accustomed to it’s foibles
Why do we like TCP?

18. ● Single POV makes my life easier
● A central gatekeeper helps us ignore our other consistency models
● It works well in the small scale
Why do we like Serializable RDBMS?

19. It’s nice to avoid coordination, but:
● Incompatible with user’s worldview
● Requires ad-hoc consistency models as an overlay
Why isn’t eventual consistency your final answer?

20. ● LieFi TCP linearizability gone wrong.
● Errant Promotion Asymmetries are problematic.
● Race conditions When two systems race head to head, you lose.
A few scenarios:

21. Concurrency is either something you’re dealing with,
or something you’re putting off. No exceptions.
Your system is distributed
Our limited comprehension of this complexity eventually leads to:
● Mysterious System Behaviors
● Inefficient Business Processes
● Lapses in Service
● Sadness

22. Causality is great when you can use it.
Wallclock baad!
Eventual Consistency is seductive, but problematic.
Consistency models are everywhere.
Be mindful of Linearizability / Serializability limitations.
Parting words:

23. Thank you!
Daniel Norman
CTO, güdTECH
unba.se contributor
Twitter: @DreamingInCode