# 5. Replication

![](../img/ch5.png)

> *The major difference between a thing that might go wrong and a thing that cannot possibly go wrong is that when a thing that cannot possibly go wrong goes wrong it usually turns out to be impossible to get at or repair.*
>
> ​    — Douglas Adams, *Mostly Harmless* (1992)

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

There are various reasons why you might want to distribute a database across multi‐ ple 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 multi‐ ple machines to give you redundancy. When one fails, another one can take over.

***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 geo‐ graphically close to them. That avoids the users having to wait for network pack‐ ets to travel halfway around the world.



## ……



## Summary

In this chapter we looked at the issue of replication. Replication can serve several purposes:

***High availability***

Keeping the system running, even when one machine (or several machines, or an entire datacenter) goes down

***Disconnected operation***

Allowing an application to continue working when there is a network interrup‐ tion

***Latency***

Placing data geographically close to users, so that users can interact with it faster

***Scalability***

Being able to handle a higher volume of reads than a single machine could han‐ dle, by performing reads on replicas



Despite being a simple goal—keeping a copy of the same data on several machines— replication turns out to be a remarkably tricky problem. It requires carefully thinking about concurrency and about all the things that can go wrong, and dealing with the consequences of those faults. At a minimum, we need to deal with unavailable nodes and network interruptions (and that’s not even considering the more insidious kinds of fault, such as silent data corruption due to software bugs).

We discussed three main approaches to replication:

***Single-leader replication***

Clients send all writes to a single node (the leader), which sends a stream of data change events to the other replicas (followers). Reads can be performed on any replica, but reads from followers might be stale.

***Multi-leader replication***

Clients send each write to one of several leader nodes, any of which can accept writes. The leaders send streams of data change events to each other and to any follower nodes.

***Leaderless replication***

Clients send each write to several nodes, and read from several nodes in parallel in order to detect and correct nodes with stale data.

Each approach has advantages and disadvantages. Single-leader replication is popular because it is fairly easy to understand and there is no conflict resolution to worry about. Multi-leader and leaderless replication can be more robust in the presence of faulty nodes, network interruptions, and latency spikes—at the cost of being harder to reason about and providing only very weak consistency guarantees.

Replication can be synchronous or asynchronous, which has a profound effect on the system behavior when there is a fault. Although asynchronous replication can be fast when the system is running smoothly, it’s important to figure out what happens when replication lag increases and servers fail. If a leader fails and you promote an asynchronously updated follower to be the new leader, recently committed data may be lost.

We looked at some strange effects that can be caused by replication lag, and we dis‐ cussed a few consistency models which are helpful for deciding how an application should behave under replication lag:

***Read-after-write consistency***

Users should always see data that they submitted themselves.

***Monotonic reads***

After users have seen the data at one point in time, they shouldn’t later see the data from some earlier point in time.

***Consistent prefix reads***

Users should see the data in a state that makes causal sense: for example, seeing a question and its reply in the correct order.

Finally, we discussed the concurrency issues that are inherent in multi-leader and leaderless replication approaches: because they allow multiple writes to happen con‐ currently, conflicts may occur. We examined an algorithm that a database might use to determine whether one operation happened before another, or whether they hap‐ pened concurrently. We also touched on methods for resolving conflicts by merging together concurrent updates.

In the next chapter we will continue looking at data that is distributed across multiple machines, through the counterpart of replication: splitting a large dataset into *partitions*.



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