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Storage and Retrieval

Wer Ordnung halt, ist nur zu faul zum Suchen.

  • (If you keep things tidily ordered, you’re just too lazy to go searching.)
  • —German proverb

On the most fundamental level, a database needs to do two things: when you give it some data, it should store the data, and when you ask it again later, it should give the data back to you.

In Chapter 2 we discussed data models and query languages—i.e., the format in which you (the application developer) give the database your data, and the mechanism by which you can ask for it again later. In this chapter we discuss the same from the database’s point of view: how we can store the data that we’re given, and how we can find it again when we’re asked for it.

Why should you, as an application developer, care how the database handles storage and retrieval internally? You’re probably not going to implement your own storage engine from scratch, but you do need to select a storage engine that is appropriate for your application, from the many that are available. In order to tune a storage engine to perform well on your kind of workload, you need to have a rough idea of what the storage engine is doing under the hood.

In particular, there is a big difference between storage engines that are optimized for transactional workloads and those that are optimized for analytics. We will explore that distinction later in “Transaction Processing or Analytics?” on page 90, and in “Column-Oriented Storage” on page 95 we’ll discuss a family of storage engines that is optimized for analytics.

However, first we’ll start this chapter by talking about storage engines that are used in the kinds of databases that you’re probably familiar with: traditional relational databases, and also most so-called NoSQL databases. We will examine two families of storage engines: log-structured storage engines, and page-oriented storage engines such as B-trees.

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