This chapter discusses the purpose of event streams, how to process them, and some key differences between them and batch processing. Message brokers process event streams, and there are two […]

This chapter starts by discussing Unix tools and pipes and how they are composed to solve larger problems. Later, the same principles are derived to explain how MapReduce and other […]

This chapter, as the title says, touches on both consistency and consensus from different angles. The first concept it dishes out is about linearizability, a consistency model that tries to […]

In distributed systems, things will be wrong and the systems engineers should be creating mechanisms to avoid that a failure in components affects the whole availability of the system. We […]

Many things can go wrong with systems, such as: The software or hardware might fail at the middle of an operation Network failures can cause unexpected cut offs in the […]

Partition is the technique of splitting the data, or shard, across many database instances. It is mainly intended to reduce the load on single nodes, so the developer must ensure […]

As mentioned in the introduction of part II, the reasons to replicate data are: Reducing latency by leaving data close to users Allow users to continue their work even if […]

This part of the book talks about data that lives in multiple nodes. An application that needs to be distributed usually has to do so due to some factors, such […]

One thing is sure, the application will change over time and the data schema underlying it certainly will too. Changing data schema, sometimes an ALTER table, or adding a new […]

Databases, at the most fundamental level, should do two things well with your data: store and be able to retrieve it later. The simplest database possible, considering a key-value one, […]