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This chapter is from the book

This chapter is from the book


This chapter concludes the examination of document databases by considering several key issues you should consider when modeling for document databases.

Normalization and denormalization are both useful practices. Normalization helps to reduce the chance of data anomalies while denormalization is introduced to improve performance. Denormalization is a common practice in document database modeling. One of the advantages of denormalization is that it reduces or eliminates the need for joins. Joins can be complex and/or resource-intensive operations. It helps to avoid them when you can, but there will likely be times you will have to implement joins in your applications. Document databases, as a rule, do not support joins.

In addition to considering the logical aspects of modeling, you should consider the physical implementation of your design. Mutable documents, in particular, can adversely affect performance. Mutable documents that grow in size beyond the storage allocated for them may have to be moved in persistent storage, such as on disks. This need for additional writing of data can slow down your applications’ update operations.

Indexes are another important implementation topic. The goal is to have the right number of indexes for your application. All instances should help improve query performance. Indexes that would help with query performance may be avoided if they would adversely impact write performance in a noticeable way. You will have to balance benefits of faster query response with the cost of slower inserts and updates when indexes are in place.

Finally, it helps to use design patterns when modeling common relations such as one-to-many, many-to-many, and hierarchies. Sometimes embedded documents are called for, whereas in other cases, references to other document identifiers are a better option when modeling these relations.

Part IV, “Column Family Databases,” introduces wide column databases. These are another important type of NoSQL database and are especially important for managing large data sets with potentially billions of rows and millions of columns.

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