This document discusses relational databases and some of their key properties and tradeoffs. It notes that consistency, availability, and partition tolerance in shared data systems can only achieve two of the three properties at once, according to Brewer's CAP theorem. The document then examines different requirements like querying, transactions, consistency and scaling in the context of choosing a database for a startup's application. It provides an example use case of using PostgreSQL, Redis, and MongoDB together to address these requirements for a listings application called Kuai List.

1. Relational Databases
An experience by a young startup aficionado

2. "The primary online data store for an
application is the worst place to take a risk with
new technology."
Adam D' Angelo - Quora CEO

3. CAP Theorem
"Of three properties of shared-data systems-data
Consistency, system Availability and tolerance to
network Partitions - only two can be achieved at any
given moment in time."
- 2000 Prof. Eric Brewer, PoDC Conference Keynote
- 2002 Seth Gilbert and Nancy Lynch, ACM SIGACT News 33(2)

4. How Do I choose the right Database?
How do elephants make decisions?
They make the smartest decisions,
hence no sacrifices are needed.

5. It’s all about the Trade-offs
Source: http://blog.nahurst.com/visual-guide-to-nosql-systems

6. But is it really just about trade-offs?

7. Trade-offs and theories are naturally
confusing!!!

8. Hands-on experience is the best
The most improvised white-board ever!

9. Requirement #1:
What do we know already?

10. Requirement #2:
What is trending?
Source: http://db-engines.com/en/ranking_trend

11. That was easy!!!

12. Requirement #3:
What about complex queries?
AHA!!! Relationships do matter right?

13. Requirement #4:
What about Transactions?
● Transactions ensure that you atomically make changes to your database.

14. Requirement #5:
What about Consistency?
● Achieved through ACID transactions.
● Consistency is about ensuring that all clients have the same view of the data.

15. Requirement #6:
How about Scaling?
● Option 1: I assume we can scale as we go...
● Option2: How about implementing Layers (Memecached) on top of Relational DBs.
(Facebook, Twitter and Airbnb)
● Option 3: If scaling is urgent, how about partitioning at the application level. (FriendFeed)

16. Use Case:
Kuai List
● We use PostgreSQL for writing and updating listings only.
● For retrieving at desirable speeds we use Amazon Elasticache (Redis - in-memory cache)
on top of our core relational database.
● NoSQL (MongoDB) for storing listings that never change.

17. Scaling Challenge #1:
Host in the cloud
● We migrated our core database to Amazon RDS (with zero downtime).
● Amazon RDS makes it easy to use replication to enhance availability and reliability for
production workloads.

18. Consistency:
Synchronous Replication
Datacenter A
Datacenter B
Datacenter C
Datacenter D