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
- Close in-memory database connections, including reads opened on threads
- Close completed and partial file upload file handles
- Close SQLite connections and file handles owned by tests
When streaming a view as CSV with ?_stream=on, _next was being
re-read from request.args, overwriting the kwarg passed by the
streaming loop with None. This caused the pagination offset to stay
stuck on page 1 forever.
