Chapter 11. Persistence and Databases

Python supports a variety of ways of making data persistent. One such way, known as serialization, involves viewing the data as a collection of Python objects. These objects can be saved, or serialized, to a byte stream, and later loaded and recreated, or deserialized, back from the byte stream. Object persistence layers on top of serialization and adds such features as object naming. This chapter covers the built-in Python modules that support serialization and object persistence.

Another way to make data persistent is to store it in a database. One simple type of database is actually just a file format that uses keyed access to enable selective reading and updating of relevant parts of the data. Python supplies modules that support several variations of this file format, known as DBM, and these modules are covered in this chapter.

A relational database management system (RDBMS), such as MySQL or Oracle, provides a more powerful approach to storing, searching, and retrieving persistent data. Relational databases rely on dialects of Structured Query Language (SQL) to create and alter a database's schema, insert and update data in the database, and query the database according to search criteria. This chapter does not provide any reference material on SQL. For that purpose, I recommend SQL in a Nutshell, by Kevin Kline (O'Reilly). Unfortunately, despite the existence of SQL standards, no two RDBMSes implement exactly the same SQL dialect.

The Python standard library does not come with an RDBMS interface. However, many free third-party modules let your Python programs access a specific RDBMS. Such modules mostly follow the Python Database API 2.0 standard, also known as the DBAPI. This chapter covers the DBAPI standard and mentions some of the third-party modules that implement it.