Analyzing APIs with PyInterceptor

PyInterceptor: A Python Function Call Interceptor for Non-Invasive Analysis

PyInterceptor is a Python library currently under development (available on GitHub) designed for non-invasive interception and analysis of function calls. Imagine needing detailed information—function names, parameters, return values, execution times—from a Python client interacting with a Python API, without modifying the existing code. PyInterceptor addresses this need.

This dev.to article introduces PyInterceptor's core concepts, use cases, and application.

Python Call Interception and Processing

Core Principles

Figure 1 — Call Interception Overview: Blocking vs. Non-Blocking Interception

Function call interception falls into two categories: blocking and non-blocking (Figure 1). Blocking interception captures call information and returns immediately, without executing the target function. This is beneficial for creating mocks or stubs during unit testing. Non-blocking interception gathers information, then executes the target function, capturing its return value before proceeding. PyInterceptor supports both modes.

Use Cases

Interception of Python API calls offers numerous applications:

• Automated Mock/Stub Creation: Utilize blocking mode to semi-automatically generate mocks and stubs for unit tests. PyInterceptor allows custom interceptor callables for returning simulated data.
• Structured Logging: Forward intercepted arguments and metadata to a logging system.
• Enhanced Debugging: Pinpoint bugs more efficiently by tracking function calls and their parameters.
• Call Statistics Generation: Collect performance metrics.
• And more…

Detailed Implementation

Figure 2 — Details: Including the handler() function and interceptor() callable

PyInterceptor operates by inserting a handler function that intercepts calls intended for the API. This handler captures metadata (arguments, timestamps, etc.), stores it in a CallInfo object, and manages call forwarding.

In blocking mode, the handler passes CallInfo to a user-defined interceptor callable. This callable processes the information (logging, statistics, etc.). The handler then returns the interceptor's result.

In non-blocking mode, the handler executes the target function, adds its return value to CallInfo, and then calls the interceptor. The actual target function's return value is returned to the caller, unlike in blocking mode.

Illustrative Code Example

This example demonstrates PyInterceptor's use with an arithmetic API and a Processor class, logging all method calls to a JSON file.

<code class="language-python">import json
from pathlib import Path
from typing import List

from interceptor import intercept, get_methods, CallInfo


class API:
    # ... (API methods remain unchanged) ...


class Processor:
    # ... (Processor methods remain unchanged) ...


class JSONLogger:
    # ... (JSONLogger class remains unchanged) ...


if __name__ == '__main__':
    # ... (Main method remains unchanged) ...</code>

The main method creates a JSONLogger instance (acting as the interceptor), intercepts the API and Processor methods using intercept(), executes Processor methods, and saves the logs to "logs.json". The JSON output contains a detailed record of each function call.

Future Enhancements

Planned improvements for PyInterceptor include:

• Comprehensive API documentation and CI/CD pipeline.
• Python decorators for simplified class/method interception.
• Configuration options for managing memory usage (copying vs. referencing CallInfo data).
• Pre-built interceptor implementations for common use cases.

Your feedback is welcome! Please leave a comment if you found this article helpful or have suggestions for future development.

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