How to Build a Metrics Counter with Increment and Snapshot in Python

A simple dict-backed MetricsCounter class that increments named counters and returns a snapshot of the current values.

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

metrics counter observability dict class

Python code

18 lines

Python 3.9+

class MetricsCounter:
    def __init__(self):
        self._metrics = {}

    def increment(self, key, delta=1):
        self._metrics[key] = self._metrics.get(key, 0) + delta

    def snapshot(self):
        return dict(self._metrics)


if __name__ == "__main__":
    counter = MetricsCounter()
    counter.increment("requests")
    counter.increment("errors")
    counter.increment("requests", delta=3)
    counter.increment("errors")
    print(counter.snapshot())

Output

stdout

{'requests': 4, 'errors': 2}

How it works

The MetricsCounter stores counters in a plain dict. increment uses get with a default of 0 so a key is created on first use, then adds the delta. The snapshot method returns a shallow copy via dict(self._metrics) so callers can't mutate the internal state. Using a class keeps the counter logic encapsulated and easy to test. The pattern is intentionally minimal — no locks, no external dependencies — which makes it a good starting point for in-process metrics.

Common mistakes

Forgetting to copy the dict in snapshot, exposing internal state to callers
Using `self._metrics[key] += delta` without a default, raising KeyError on first use
Not handling delta=0 or negative values explicitly when the semantics need them
Assuming thread safety — a plain dict is not thread-safe for concurrent increments

Variations

Return an immutable snapshot with `MappingProxyType` to prevent mutation
Add a `reset(key)` or `reset()` method for test fixtures and period resets
Store timestamps alongside counters to support rate calculations

Real-world use cases

Track HTTP request counts and error rates inside a web service middleware.
Record batch job progress (processed, failed, retried items) during a pipeline run.
Provide lightweight in-process metrics for integration tests and local dev dashboards.

How to Build a Metrics Counter with Increment and Snapshot in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Common mistakes

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Real-world use cases

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