12. 数据持久化

The modules described in this chapter support storing Python data in a persistent form on disk. The pickle and marshal modules can turn many Python data types into a stream of bytes and then recreate the objects from the bytes. The various DBM-related modules support a family of hash-based file formats that store a mapping of strings to other strings.

The list of modules described in this chapter is:

• 12.1. pickle — Python对象序列化
  • 12.1.1. Relationship to other Python modules
  • 12.1.2. Data stream format
  • 12.1.3. Module Interface
  • 12.1.4. What can be pickled and unpickled?
  • 12.1.5. Pickling Class Instances
    • 12.1.5.1. Persistence of External Objects
    • 12.1.5.2. Dispatch Tables
    • 12.1.5.3. Handling Stateful Objects
  • 12.1.6. Restricting Globals
  • 12.1.7. Examples
• 12.2. copyreg — 注册 pickle 支持函数
  • 12.2.1. Example
• 12.3. shelve — Python对象持久性
  • 12.3.1. Restrictions
  • 12.3.2. Example
• 12.4. marshal — 内部的Python对象序列化
• 12.5. dbm — 接口Unix的 “数据库”
  • 12.5.1. dbm.gnu — GNU’s reinterpretation of dbm
  • 12.5.2. dbm.ndbm — Interface based on ndbm
  • 12.5.3. dbm.dumb — Portable DBM implementation
• 12.6. sqlite3 — SQLite数据库的DB-API 2.0接口
  • 12.6.1. Module functions and constants
  • 12.6.2. Connection Objects
  • 12.6.3. Cursor Objects
  • 12.6.4. Row Objects
  • 12.6.5. SQLite and Python types
    • 12.6.5.1. Introduction
    • 12.6.5.2. Using adapters to store additional Python types in SQLite databases
      • 12.6.5.2.1. Letting your object adapt itself
      • 12.6.5.2.2. Registering an adapter callable
    • 12.6.5.3. Converting SQLite values to custom Python types
    • 12.6.5.4. Default adapters and converters
  • 12.6.6. Controlling Transactions
  • 12.6.7. Using sqlite3 efficiently
    • 12.6.7.1. Using shortcut methods
    • 12.6.7.2. Accessing columns by name instead of by index
    • 12.6.7.3. Using the connection as a context manager
  • 12.6.8. Common issues
    • 12.6.8.1. Multithreading