Attribute, SpanName and MetricName are str subclasses whose __new__
requires the metadata arguments, so copy.deepcopy could not reconstruct
one - it falls back to cls.__new__(cls) and raises TypeError.
That broke a real path rather than a theoretical one. The SDK's
ConsoleMetricExporter renders data points through dataclasses.asdict(),
which deepcopies mappings, and both core and kit-based plugins pass
registry entries as metric attribute keys - so every console metrics
dump crashed, core's own points included. Found by datasette-paper's
dev harness running opentelemetry-instrument with console exporters.
__reduce__ collapses copies to a plain str, which is what an entry is
everywhere except the registry module itself: the description, values
and buckets describe the single registered instance, and nothing reads
them off a copy. Pickle is fixed by the same change.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01JwU7BcTnAxUGSJYhBrQaY7
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
A survey of five plugin OTel plans (datasette-paper, -agent, -litestream,
-accounts, -cron) found every one hand-copying the same core machinery:
the registry classes, the conformance-test harness, the pytest fixtures,
the bucket boundaries and the detached-root-with-Link recipe. This makes
that machinery importable instead:
- The registry classes are documented public API. Attribute gains
values= (a closed enum the conformance helpers enforce - what makes an
attribute safe as a metric dimension); SpanName gains prefix=True for
span families like "chat {model}" whose names share a fixed prefix,
matched by span_for() after exact names. span_for()/attribute helpers
accept a spans= tuple so plugin registries can use them.
- datasette.telemetry.linked_root_span_kwargs(): the root-span-with-Link
shape for work a request caused without containing - background jobs,
scheduled ticks, block=False writes. Core's own write thread now uses
it instead of building the kwargs inline.
- datasette.telemetry_testing: the session provider fixtures, otel_spans
/ otel_metrics, a two-way registry conformance checker (including enum
and prefix handling, filtered by instrumentation scope) and an
assert_package_never_imports_sdk() guard. Core's conftest now imports
these instead of defining them, so the suite consumes the kit exactly
as a plugin's would.
- New "Telemetry for plugin authors" docs page: scope discipline,
registry usage, privacy/cardinality rules, named-callable guidance,
request_span(), the background root-with-link convention (one root per
tick, always emitted), provider-ordering facts and known caveats.
request_span() is now documented public API.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012U7coQfVu8nK2R4q2mCULA
Span attributes were checked in both directions; metric attributes were not
checked at all, so the generated reference could publish an incomplete list
with nothing to catch it.
The metric workload lives in an `emitted_metrics` fixture, mirroring the
span side, and error.type is checked like every other attribute rather than
exempted for being optional - the workload reaches it two separate ways.
(Adapted from b30c5341: the old workload's facet-timeout probe belongs to
phase 5 and is dropped, and the interrupted counter now needs a query that
exceeds the *configured* time limit - custom short budgets are excluded from
the count on this lineage - so the fixture runs one against a second
instance configured with sql_time_limit_ms=5.)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01F2h9ANGZ7paWSpqs5DUAcG
Both histograms declared unit="s" but inherited OpenTelemetry's default
boundaries, which are tuned for milliseconds - so every SQLite query
landed in the single (0, 5] second bucket and every quantile query
returned noise.
The boundaries are the semantic conventions' recommended set for
db.client.operation.duration plus 0.0001 and 0.0005 at the bottom, since
SQLite is in-process and many real queries take tens of microseconds.
(Adapted from 024f2029: that commit assumed the metrics were already in
telemetry_registry.py, which on this lineage held spans only - so this
commit also brings the MetricName registry machinery, the registry
entries for all eight phase-3 metrics, the cog-generated Metric
reference in internals.rst, and the datasette.operation attribute. The
template and facet histograms it also touched belong to phase 5 and are
not included.)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01F2h9ANGZ7paWSpqs5DUAcG
