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2a05263ab8
The five surveyed plugin plans all kept a hand-rolled metrics-vs-registry
diff because the kit's conformance helpers covered spans only. This adds
the metric side:
- metric_for() in the registry (the span_for analogue - no prefix/dynamic
machinery, metric names are static), and the attribute helpers are
documented as accepting MetricName entries.
- MetricsCollector.collect() now retains the instrumentation scope per
collected metric, so a plugin is judged against its own meter only.
- assert_metrics_conform(): every collected metric in scope is registered,
was created as the instrument kind and unit its registry entry declares
(drift between the registry entry and the meter.create_*() call was
previously caught by nothing, in core or any plugin), sets only
registered attributes, and respects values= enums - the check that makes
a metric dimension provably bounded.
- assert_metrics_covered(): every registered metric collected at least
once with every non-optional attribute seen. Both *_covered helpers now
exempt optional=True attributes, so a workload is not forced to
manufacture every error path; pin those with targeted tests instead.
- datasette.operation declares values={"read", "write"} - core dogfoods
the enum enforcement on the dimension where it matters most.
- Core's generic metric conformance tests are now calls to the kit
helpers with scope_name="datasette"; the stricter literal-pinning and
optional-attribute-coverage tests stay hand-written on purpose.
- The metric reference docs render attributes through the same helper as
spans, so *(optional)* markers and enum values now appear there too.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012U7coQfVu8nK2R4q2mCULA
53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
"""
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Render the span reference in ``internals.rst`` from
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``datasette/telemetry_registry.py``.
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Driven by cog, and ``cog --check docs/*.rst`` runs in CI - so adding a span
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without documenting it, or documenting one that no longer exists, is a build
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failure rather than something a reader discovers later.
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"""
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def _attribute_lines(cog, attributes):
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if not attributes:
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cog.out(" No attributes.\n\n")
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return
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cog.out(" Attributes:\n\n")
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for attribute in attributes:
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suffix = " *(optional)*" if attribute.optional else ""
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line = f" - ``{attribute}``{suffix} - {attribute.description}"
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if attribute.values is not None:
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rendered = ", ".join(f"``{value}``" for value in sorted(attribute.values))
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line += f" One of: {rendered}."
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cog.out(line + "\n")
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cog.out("\n")
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def spans(cog):
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from opentelemetry.trace import SpanKind
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from datasette.telemetry_registry import SPANS
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cog.out("\n")
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for span in SPANS:
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cog.out(f"``{span}``\n")
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cog.out(f" {span.description}\n\n")
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# INTERNAL is the default and the overwhelming majority of spans -
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# printing it on every one would be noise. Only the exceptional case,
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# a real database call, is worth calling out.
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if span.kind != SpanKind.INTERNAL:
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cog.out(f" Kind: ``{span.kind.name}``.\n\n")
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_attribute_lines(cog, span.attributes)
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def metrics(cog):
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from datasette.telemetry_registry import METRICS
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cog.out("\n")
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for metric in METRICS:
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cog.out(f"``{metric}``\n")
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cog.out(f" {metric.kind}, unit ``{metric.unit}``. {metric.description}\n\n")
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if metric.buckets:
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boundaries = ", ".join(f"``{boundary}``" for boundary in metric.buckets)
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cog.out(f" Bucket boundaries: {boundaries}.\n\n")
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_attribute_lines(cog, metric.attributes)
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