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Designing Data-Intensive Applications by Martin Kleppmann

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Chapter 8. The Trouble with Distributed Systems

Hey I just met you The network’s laggy But here’s my data So store it maybe

Kyle Kingsbury, Carly Rae Jepsen and the Perils of Network Partitions (2013)

A recurring theme in the last few chapters has been how systems handle things going wrong. For example, we discussed replica failover (“Handling Node Outages”), replication lag (“Problems with Replication Lag”), and concurrency control for transactions (“Weak Isolation Levels”). As we come to understand various edge cases that can occur in real systems, we get better at handling them.

However, even though we have talked a lot about faults, the last few chapters have still been too optimistic. The reality is even darker. We will now turn our pessimism to the maximum and assume that anything that can go wrong will go wrong.i (Experienced systems operators will tell you that is a reasonable assumption. If you ask nicely, they might tell you some frightening stories while nursing their scars of past battles.)

Working with distributed systems is fundamentally different from writing software on a single computer—and the main difference is that there are lots of new and exciting ways for things to go wrong [1, 2]. In this chapter, we will get a taste of the problems that arise in practice, and an understanding of the things we can and cannot rely on.

In the end, our task as engineers ...

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