Mocking a Metrics Gauge's set_value Method in Python

Demonstrates using unittest.mock.Mock with wraps to intercept a gauge's set_value call while verifying arguments and preserving real behavior.

Easy Python 3.9+ Aug 9, 2026 Observability & SRE 13 views 0 copies

unittest mocking metrics observability

Python code

20 lines

Python 3.9+

from unittest.mock import Mock

class MetricsGauge:
    def __init__(self, name):
        self.name = name
        self.value = 0.0

    def set_value(self, new_value):
        self.value = float(new_value)
        return self.value

# Usage demonstration with a mock
gauge = MetricsGauge("cpu_usage")
gauge_mock = Mock(wraps=gauge)
mock_result = gauge_mock.set_value(75.5)
print(f"Real gauge value: {gauge.value}")
print(f"Mock return value: {mock_result}")
print(f"Mock called with: {gauge_mock.set_value.call_args}")
gauge_mock.set_value.assert_called_once_with(75.5)
print("Assertion passed: set_value was called with 75.5")

Output

stdout

Real gauge value: 75.5
Mock return value: 75.5
Mock called with: call(75.5)
Assertion passed: set_value was called with 75.5

How it works

Mock(wraps=gauge) creates a mock that delegates to the real gauge methods while recording calls. Calling set_value(75.5) invokes the real implementation, so gauge.value becomes 75.5, and the mock returns that value. The mock stores call arguments, accessible via set_value.call_args. assert_called_once_with verifies the exact call, making it useful for testing metrics setters in observability code.

Common mistakes

Using `Mock()` without `wraps` so the real `set_value` never executes, leaving `gauge.value` unchanged.
Forgetting to check `call_args` before asserting, which can hide the actual call signature.
Asserting the mock was called with a float when the real code passed an int (though conversion handles it here).

Variations

Use `create_autospec` to auto-generate a mock matching the class's signature.
Use `patch` with `return_value` to stub `set_value` entirely for isolation.

Real-world use cases

Testing that application code calls a metrics gauge setter with the correct value during load testing.
Verifying a custom exporter pushes the right gauge values without hitting a real monitoring backend.
Auditing that periodic health-check tasks update gauges at expected intervals and magnitudes.

Mocking a Metrics Gauge's set_value Method in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Real-world use cases

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