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
Outcome of a whole-stack review with the kit visible as one system:
- otel_reset: an autouse fixture draining the span exporter and metric
reader after every test. Without it a large suite accumulates hundreds
of thousands of recorded spans in the session-scoped exporter - the
likeliest amplifier of the slow-runner CI flakes - and plugins would
inherit the same leak.
- assert_registry_covered renamed to assert_spans_covered: the old name
read as covering the whole registry, which is exactly wrong next to
assert_metrics_covered. Public API is forever; renamed before anything
ships, no alias.
- The installers now verify their provider actually took: with a
provider installed first (opentelemetry-instrument, an embedding app),
set_*_provider() is silently ignored, and fixtures would assert
against an exporter wired to nothing. They skip clearly instead.
- UPDOWN_COUNTER registry kind, mapped to Sum with monotonicity checked
both ways - a Counter must collect monotonic, an UpDownCounter must
not. Previously an UpDownCounter's kind check was silently skipped.
- The docs page now prescribes naming: scope = import package name
(underscores), signal prefix = a name you own, never bare datasette.*;
its own examples no longer teach the hyphenated outlier. Plus an
observable-gauges pattern section and a prefix-overlap note.
- assert_no_forbidden_values(): the enforcement half of the privacy
rules - plant sentinel secrets in a workload and assert they never
appear in any span name, attribute, event, status description or
metric attribute, across all scopes by default.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012U7coQfVu8nK2R4q2mCULA
test_kit_module_itself_never_imports_the_sdk shells out, and like every
subprocess-spawning test in this suite it crashes the interpreter with
SIGBUS on macOS/CPython 3.13 when it runs late enough that the process
holds many threads - the exact failure conftest already front-loads
test_datasette_package_never_imports_the_sdk for. Move it to the front
too, and note the hazard in assert_package_never_imports_sdk's docstring
since plugin suites will call it.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012U7coQfVu8nK2R4q2mCULA
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
The callback entry points gained db.query spans in the database-spans PR;
this adds their other half - the duration histogram measurement, so a
plugin's execute_fn/execute_write_fn work and the JSON write API's inserts
and deletes stop being invisible to the one series that survives trace
sampling. execute_isolated_fn records "write" when the database is mutable
(the call blocks the write queue) and "read" when immutable (it runs on
the read pool). error.type comes from the raised exception class, same as
the SQL-string paths.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012U7coQfVu8nK2R4q2mCULA
- The exemplars docs described "the pinned opentelemetry-exporter-prometheus"
and "Datasette's own Prometheus exporter" - context from demo/plugin work
that is no longer part of this stack. Reworded to stand alone.
- Saturate a num_sql_threads=1 pool and assert the queue-depth gauge reads
above zero - the headline alerting metric previously only had an absence
test, and this also pins the private ThreadPoolExecutor._work_queue
attribute it depends on.
- Pin error.type on the write path of db.client.operation.duration - the
write wrappers time a different code path than the read one already tested.
- Isolate the non-threaded-mode gauge test from other live instances instead
of comparing global observation counts, which a GC pass could shift.
- Halve the metrics banner, point conftest's meter note at it, compact the
interrupted-counter call-site comment to a registry pointer, note why
instrument and registry descriptions are separate strings, and stop
calling the metric dimension a "later phase" now that metrics shipped.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012U7coQfVu8nK2R4q2mCULA
Histograms recorded inside a sampled span carry trace IDs automatically, so a
latency spike links to a trace that caused it. Nothing said so.
Two things are documented because they were measured rather than assumed: an
exemplar is kept per histogram bucket, so the bucket boundaries fixed earlier
in this stack took the same workload from one reachable trace to four; and the
pinned opentelemetry-exporter-prometheus drops exemplars entirely, so the path
that works is an OTLP collector rather than Datasette's Prometheus exporter.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
(cherry picked from 9d066255; section numbering and cross-references
adjusted to this branch's demo README, and the exemplar reference placed
as a subsection of the new Metric reference in internals.rst.)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01F2h9ANGZ7paWSpqs5DUAcG
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
Spans describe requests that have finished. They structurally cannot answer
"am I saturating my 3 SQL threads right now", because that is a level rather
than an event - and with num_sql_threads defaulting to 3, it is usually the
first thing worth knowing about a busy Datasette. This adds the metrics that
answer it.
Five observable gauges, computed only when something is collecting, so an
instance with no MeterProvider installed does no work for them at all:
datasette.sql.threads.limit num_sql_threads
datasette.sql.threads.queue_depth queries waiting for a free thread
datasette.sql.queries.pending in-flight reads, by db.namespace
datasette.write.queue_depth writes behind the single write thread
datasette.connections.open tracked file connections
Three instruments recorded inline, which matters because metrics survive
trace sampling and spans do not - an operator sampling 1% of traces still
gets 100% of the latency distribution:
db.client.operation.duration semconv histogram, with error.type
datasette.write.queue_wait the metric twin of the existing span
datasette.sql.queries.interrupted sql_time_limit_ms kills
The interrupted counter closes a gap the plan called out as unanswerable:
"how often are we killing queries at the limit" is a rate, and a rate cannot
be recovered from sampled spans.
Core still creates no provider of any kind, so the architecture is unchanged;
`grep -rn 'opentelemetry.sdk' datasette/` stays empty. One real difference
from tracing is worth recording: _ProxyMeter and its instruments forward to a
provider installed after they were created, whereas ProxyTracer permanently
caches the first concrete tracer it resolves. Module-level instruments are
therefore safe and the test fixture has no ordering constraint.
Live instances are tracked in a lock-guarded WeakSet so instrumenting an
instance never keeps it alive. The pool gauges carry no attribute saying
which Datasette produced them: production runs one instance per process, and
adding an id to disambiguate the test suite's hundreds of instances would buy
unbounded attribute cardinality to fix a case that does not occur. The
collision is documented instead, and the gauge callbacks are plain generator
functions so tests can assert exact values by calling them directly rather
than through the SDK's last-value aggregation.
demos/otel/metrics_demo.py fires 12 concurrent 40ms queries at a 3-thread
pool and samples the gauges mid-flight: queue_depth peaks at exactly 9, and
the duration histogram reads max=0.1695s for a query whose work is 40ms. That
gap is the queue, and it is the thing traces alone will not show you.
Also corrects the demo README's privacy section, which still claimed
parameter values are never recorded - that stopped being unconditionally true
when trace_sql_parameters landed.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
(cherry picked from 6ef0dd8c and adapted to the rebuilt phase-1 stack:
attribute names now come from telemetry_registry where entries exist, the
meter carries the instrumentation-scope version and schema URL, and the
interrupted-queries counter skips expected timeouts - callers that opted
into a deliberately short budget, like facet suggestion - matching how
those are excluded from span error status. The internals.rst reference
lands with the registry commit that follows.)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01F2h9ANGZ7paWSpqs5DUAcG
