PART II Distributed Data

In Part I of this book, we discussed aspects of data systems that apply when data is stored on a single machine. Now, in Part II, we move up a level and ask: what happens if multiple machines are involved in storage and retrieval of data?

NOTE: 参见文章One-to-multiple.

There are various reasons why you might want to distribute a database across multiple machines:

Scalability

If your data volume, read load, or write load grows bigger than a single machine can handle, you can potentially spread the load across multiple machines.

Fault tolerance/high availability

If your application needs to continue working even if one machine (or several machines, or the network, or an entire datacenter) goes down, you can use multiple machines to give you redundancy. When one fails, another one can take over.

NOTE: 参见：

维基百科High availability

维基百科Single point of failure

维基百科Redundancy (engineering)

Latency

If you have users around the world, you might want to have servers at various locations worldwide so that each user can be served from a datacenter that is geographically close to them. That avoids the users having to wait for network packets to travel halfway around the world.

Scaling to Higher Load

NOTE: 如何进行scale？

shared-memory architecture（vertical scaling or scaling up）

shared-disk architecture

shared-nothing architectures（horizontal scaling or scaling out）

Shared-Nothing Architectures

NOTE: 这是本书重点关注的。这总architecture是distributed system。

Replication Versus Partitioning

There are two common ways data is distributed across multiple nodes:

Replication

Keeping a copy of the same data on several different nodes, potentially in different locations. Replication provides redundancy: if some nodes are unavailable, the data can still be served from the remaining nodes. Replication can also help improve performance. We discuss replication in Chapter 5.

Partitioning

Splitting a big database into smaller subsets called partitions so that different partitions can be assigned to different nodes (also known as sharding). We discuss partitioning in Chapter 6.

These are separate mechanisms, but they often go hand in hand, as illustrated in Figure II-1.