Systems Design

Question 1

CAP Theorem

Answer 1

No distributed system is safe from network failures, thus network partitioning generally has to be tolerated.

In the presence of a partition, one is then left with two options: consistency or availability.

When choosing consistency over availability, the system will return an error or a time out if particular information cannot be guaranteed to be up to date due to network partitioning.

Question 2

Consistency

Answer 2

reads are always up to date, which means any client making a request to the database will get the same view of data

Question 3

Availability

Answer 3

database requests always receive a response (when valid)

Question 4

Meaning of CAP Theorem

Answer 4

Consistency – that reads are always up to date, which means any client making a request to the database will get the same view of data.
Availability – database requests always receive a response (when valid).
Partition tolerance – that a network fault doesn’t prevent messaging between nodes.

In the context of distributed (NoSQL) databases, this means there is always going to be a trade-off between consistency and availability. This is because distributed systems are always necessarily partition tolerant (ie. it simply wouldn’t be a distributed database if it wasn’t partition tolerant.)

Question 5

Long Polling vs Short Polling

Answer 5

In almost all cases, Amazon SQS long polling is preferable to short polling. Long-polling requests let your queue consumers receive messages as soon as they arrive in your queue while reducing the number of empty ReceiveMessageResponse instances returned.