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Consistency and Consensus

Is it better to be alive and wrong or right and dead?

—Jay Kreps, A Few Notes on Kafka and Jepsen (2013)

Lots of things can go wrong in distributed systems, as discussed in Chapter 8. The simplest way of handling such faults is to simply let the entire service fail, and show the user an error message. If that solution is unacceptable, we need to find ways of tolerating faults—that is, of keeping the service functioning correctly, even if some internal component is faulty.

In this chapter, we will talk about some examples of algorithms and protocols for building fault-tolerant distributed systems. We will assume that all the problems from Chapter 8 can occur: packets can be lost, reordered, duplicated, or arbitrarily delayed in the network; clocks are approximate at best; and nodes can pause (e.g., due to garbage collection) or crash at any time.

The best way of building fault-tolerant systems is to find some general-purpose abstractions with useful guarantees, implement them once, and then let applications rely on those guarantees. This is the same approach as we used with transactions in Chapter 7: by using a transaction, the application can pretend that there are no crashes (atomicity), that nobody else is concurrently accessing the database (isolation), and that storage devices are perfectly reliable (durability). Even though crashes, race conditions, and disk failures do occur, the transaction abstraction hides those problems so that the application doesn’t need to worry about them.

We will now continue along the same lines, and seek abstractions that can allow an application to ignore some of the problems with distributed systems. For example, one of the most important abstractions for distributed systems is consensus: that is, getting all of the nodes to agree on something. As we shall see in this chapter, reliably reaching consensus in spite of network faults and process failures is a surprisingly tricky problem.

Once you have an implementation of consensus, applications can use it for various purposes. For example, say you have a database with single-leader replication. If the leader dies and you need to fail over to another node, the remaining database nodes can use consensus to elect a new leader. As discussed in “Handling Node Outages” on page 156, it’s important that there is only one leader, and that all nodes agree who the leader is. If two nodes both believe that they are the leader, that situation is called split brain, and it often leads to data loss. Correct implementations of consensus help avoid such problems.

Later in this chapter, in “Distributed Transactions and Consensus” on page 352, we will look into algorithms to solve consensus and related problems. But first we first need to explore the range of guarantees and abstractions that can be provided in a distributed system.

We need to understand the scope of what can and cannot be done: in some situations, it’s possible for the system to tolerate faults and continue working; in other situations, that is not possible. The limits of what is and isn’t possible have been explored in depth, both in theoretical proofs and in practical implementations. We will get an overview of those fundamental limits in this chapter.

Researchers in the field of distributed systems have been studying these topics for decades, so there is a lot of material—we’ll only be able to scratch the surface. In this book we don’t have space to go into details of the formal models and proofs, so we will stick with informal intuitions. The literature references offer plenty of additional depth if you’re interested.

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