Introduction

The firebird-base package is a set of Python 3 modules commonly used by Firebird Project in various development projects (for example the firebird-driver). However, these modules have general applicability outside the scope of development for Firebird RDBMS.

Common data types

The types module provides collection of classes and other types that are often used by other library modules or applications.

  • Exception Error that is intended to be used as a base class of all application-related errors. The important difference from Exception class is that Error accepts keyword arguments, that are stored into instance attributes with the same name.

  • Singleton base class for singletons.

  • Sentinel base class for named sentinel objects that provide meaningful str and repr, along with collection of predefined sentinels.

  • Distinct abstract base class for classes (incl. dataclasses) with distinct instances.

  • Collection of Enums and custom string types.

Various collection types

The collections module provides data structures that behave much like builtin list and dict types, but with direct support of operations that can use structured data stored in container, and which would normally require utilization of operator, functools or other means.

All containers provide next operations:

  • filter and filterfalse that return generator that yields items for which expr is evaluated as True (or False).

  • find that returns first item for which expr is evaluated as True, or default.

  • contains that returns True if there is any item for which expr is evaluated as True.

  • occurrence that returns number of items for which expr is evaluated as True.

  • all and any that return True if expr is evaluated as True for all or any list element(s).

  • report that returns generator that yields data produced by expression(s) evaluated on list items.

Individual collection types provide additional operations like splitting and extracting based on expression etc.

Expressions used by these methods could be strings that contain Python expression referencing the collection item(s), or lambda functions.

Data conversion from/to string

While Python types typically support conversion to string via builtin str() function (and custom __str__ methods), there is no symetric operation that converts string created by str() back to typed value. Module strconv provides support for such symetric conversion from/to string for any data type.

Symetric string conversion is used by config module, notably by ListOption and DataclassOption. You can extend the range of data types supported by these options by registering convertors for required data types.

Configuration definitions

Complex applications (and some library modules like logging) could be often parametrized via configuration. Module config provides a framework for unified structured configuration that supports:

  • configuration options of various data type, including lists and other complex types

  • validation

  • direct manipulation of configuration values

  • reading from (and writing into) configuration in configparser format

  • exchanging configuration (for example between processes) using Google protobuf messages

Memory buffer manager

Module buffer provides a raw memory buffer manager with convenient methods to read/write data of various data type.

Hook manager

Module hooks provides a general framework for callbacks and “hookable” events, that supports multiple usage strategies.

Context-based logging

Module logging provides context-based logging system built on top of standard logging module.

The context-based logging:

  • Adds context information (defined as combination of topic, agent and context string values) into LogRecord, that could be used in logging message.

  • Adds support for f-string message format.

  • Allows assignment of loggers to specific contexts. The LoggingManager class maintains a set of bindings between Logger objects and combination of agent, context and topic specifications. It’s possible to bind loggers to exact combination of values, or whole sets of values using ANY sentinel. It means that is possible to assign specific Logger to log messages for particular agent in any context, or any agent operating in specific context etc.

Trace/audit for class instances

Module trace provides trace/audit logging for functions or object methods through context-based logging provided by logging module.

The trace logging is performed by traced decorator. You can use this decorator directly, or use TracedMixin class to automatically decorate methods of class instances on creation. Each decorated callable could log messages before execution, after successful execution or on failed execution (when unhandled execption is raised by callable). The trace decorator can automatically add agent and context information, and include parameters passed to callable, execution time, return value, information about raised exception etc. to log messages.

The trace logging is managed by TraceManager, that allows dynamic configuration of traced callables at runtime.

Registry for Google Protocol Buffer messages and enums

Module protobuf provides central registry for Google Protocol Buffer messages and enums. The generated *_pb2.py protobuf files could be registered using register_decriptor or load_registered function. The registry could be then used to obtain information about protobuf messages or enum types, or to create message instances or enum values.