Preface

Distributed Data

Moving up a level to data systems that run on multiple machines, we still care about similar things.

• Scalability: splitting load between multiple machines
• Fault tolerance: tolerating failure at one or more machines
• Latency: minimizing the time between a request and response using geographic proximity of CDNs, caching, etc.

Scaling Up

Shared memory architecture is a single computer with multiple CPUs, each with their own cache and memory. The CPUs communicate via a shared memory bus. Prices to double power of machine quickly become prohibitive, and the shared memory bus becomes a bottleneck anyways.

Shared Disk Architecture is a multi machine setup with each machine having its own CPU and memory, but all machines share a disk over the network. This is a bit better, but the disk becomes a bottleneck.

Scaling Out

Shared Nothing Architecture is a multi machine setup with each machine having its own CPU, memory, and disk. This is the most scalable, but it requires a lot of coordination between machines. Each machine (or VM) is a node, and each node is completely independent. Inter-node communication is done over the network through software.

For the remainder of this part (Part 2), we will be focusing on shared nothing architectures.