Metrics Exceptions¶

The metrics module handles application telemetry and performance tracking. The exception hierarchy is intentionally minimal, with a single base class for all metrics-related errors.

Exception hierarchy¶

MetricsError (base)

Common failure modes¶

MetricsError is raised when metrics collection, aggregation, or export encounters an unrecoverable state.
Typical causes: invalid metric names, backend connectivity issues, or malformed metric data.

Usage patterns¶

Guarding metrics operations¶

from kstlib.metrics.exceptions import MetricsError

try:
    metrics.record("order_latency", latency_ms)
except MetricsError as error:
    LOGGER.warning("Metrics recording failed: %s", error)
    # Continue execution - metrics should not block business logic

Defensive metrics wrapper¶

from kstlib.metrics.exceptions import MetricsError

def safe_record(name: str, value: float) -> None:
    """Record a metric without raising on failure."""
    try:
        metrics.record(name, value)
    except MetricsError:
        pass  # Metrics failure should not crash the app

Module reference¶

Exceptions for the metrics module.

exception kstlib.metrics.exceptions.MetricsError[source]¶

Bases: KstlibError

Base exception for metrics-related errors.

Examples

>>> raise MetricsError("Something went wrong")
Traceback (most recent call last):
    ...
kstlib.metrics.exceptions.MetricsError: Something went wrong