datasette/tests/test_telemetry_registry.py

Ignoring revisions in .git-blame-ignore-revs. Click here to bypass and see the normal blame view.

502 lines
18 KiB
Python
Raw Normal View History

"""
Tests that the spans, attributes and metrics Datasette emits match
datasette/telemetry_registry.py, in both directions.
"""
import copy
import io
import itertools
import pickle
import pytest
import pytest_asyncio
pytest.importorskip("opentelemetry.sdk")
from opentelemetry.trace import SpanKind
from datasette import hookimpl
from datasette import telemetry_registry as reg
from datasette.app import Datasette
from datasette.database import QueryInterrupted
from datasette.telemetry_testing import assert_metrics_conform, assert_metrics_covered
from datasette.utils.sqlite import sqlite3
# Written out as literals rather than read from the registry, so renaming a
# signal fails these tests.
EXPECTED_ATTRIBUTES = {
"db.query": {
"db.system",
"db.namespace",
"db.query.text",
"datasette.callback",
"db.operation.name",
"datasette.param_count",
"datasette.param_sets",
"datasette.time_limit_ms",
"datasette.rows_returned",
"datasette.truncated",
"datasette.interrupted",
"datasette.sql_error_suppressed",
"datasette.executescript",
"datasette.executemany",
},
"db.query.execute": set(),
"db.write.queue_wait": set(),
"db.write.execute": {
"datasette.isolated_connection",
"datasette.transaction",
},
"datasette.startup": set(),
}
EXPECTED_SPANS = set(EXPECTED_ATTRIBUTES)
# The HTTP request span name is composed at runtime as "{method} {route}", so
# it is checked by shape rather than as a literal. The workload only issues GETs.
EXPECTED_HTTP_SPAN_NAME = "{http.request.method} {http.route}"
EXPECTED_HTTP_METHOD_NAMES = {"GET"}
EXPECTED_HTTP_ATTRIBUTES = {
"http.request.method",
"http.route",
"url.path",
"url.scheme",
"server.address",
"user_agent.original",
"http.response.status_code",
"error.type",
"datasette.internal_client",
}
# The registry uses the name template for the request span.
EXPECTED_REGISTRY_ATTRIBUTES = dict(
EXPECTED_ATTRIBUTES, **{EXPECTED_HTTP_SPAN_NAME: EXPECTED_HTTP_ATTRIBUTES}
)
EXPECTED_REGISTRY_NAMES = set(EXPECTED_REGISTRY_ATTRIBUTES)
# Named in-memory databases are shared between instances, so each workload
# uses a unique name.
_names = itertools.count()
def _unique(prefix):
return f"{prefix}{next(_names)}"
class _BoomPlugin:
"A route that raises, producing a 500 and error.type on the request span."
__name__ = "TelemetryRegistryBoomPlugin"
@hookimpl
def register_routes(self):
return [(r"^/-/telemetry-registry-boom$", lambda: 1 / 0)]
async def exercise():
"""
Drive enough of Datasette to emit every registered span and attribute,
including datasette.startup. Returns the instance so the caller can close it.
"""
name = _unique("registry")
ds = Datasette(memory=True)
ds.add_memory_database(name)
# datasette.startup
await ds.invoke_startup()
db = ds.get_database(name)
# Writes: db.write.queue_wait, db.write.execute, db.query
await db.execute_write("create table t (id integer primary key, v text)")
# datasette.executemany, datasette.param_sets
await db.execute_write_many(
"insert into t (id, v) values (?, ?)", [[i, f"v{i}"] for i in range(30)]
)
# datasette.executescript
await db.execute_write_script("create table t2 (id integer); drop table t2;")
# datasette.transaction=False - VACUUM cannot run inside a transaction
await db.execute_write("vacuum", transaction=False)
# datasette.isolated_connection=True
await db.execute_isolated_fn(lambda conn: conn.execute("select 1").fetchone())
# datasette.callback, using named functions rather than lambdas
def registry_read_callback(conn):
return conn.execute("select count(*) from t").fetchone()
def registry_write_callback(conn):
conn.execute("insert into t (id, v) values (100, 'callback')")
await db.execute_fn(registry_read_callback)
await db.execute_write_fn(registry_write_callback)
# Reads: db.query.execute, datasette.rows_returned, datasette.truncated,
# datasette.param_count, datasette.time_limit_ms
await db.execute("select * from t where id > :n", {"n": 5})
await db.execute("select * from t", truncate=True)
# datasette.sql_error_suppressed
with pytest.raises(sqlite3.OperationalError):
await db.execute("select nope from t", log_sql_errors=False)
# datasette.interrupted: an unbounded recursive CTE always exceeds 1ms
with pytest.raises(QueryInterrupted):
await db.execute(
"with recursive c(x) as (select 0 union all select x+1 from c) "
"select * from c",
custom_time_limit=1,
)
# HTTP request spans and their attributes
assert (await ds.client.get(f"/{name}/t?_facet=v")).status_code == 200
assert (await ds.client.get(f"/{name}/t/1.json")).status_code == 200
# error.type on the request span, set by a 5xx response
ds.pm.register(_BoomPlugin(), name="telemetry-registry-boom")
try:
response = await ds.client.get("/-/telemetry-registry-boom")
assert response.status_code == 500
finally:
ds.pm.unregister(name="telemetry-registry-boom")
return ds
@pytest_asyncio.fixture
async def emitted(otel_spans):
"""
Every (span name, span kind, attributes) triple emitted by exercise().
The kind is needed to resolve the dynamically named request span.
"""
ds = await exercise()
spans = otel_spans.get_finished_spans()
assert spans, "no spans captured - the fixture is not exercising anything"
# str() so failure messages show plain strings, not registry instances
collected = tuple(
(
str(span.name),
span.kind,
{str(key): value for key, value in (span.attributes or {}).items()},
)
for span in spans
)
ds.close()
return collected
def _partition(emitted):
"The statically named spans, and the dynamically named request spans."
static = [record for record in emitted if record[1] is not SpanKind.SERVER]
server = [record for record in emitted if record[1] is SpanKind.SERVER]
return static, server
def _keys_by_span(records):
by_span = {}
for name, _kind, attributes in records:
by_span.setdefault(name, set()).update(attributes)
return by_span
@pytest.mark.asyncio
async def test_workload_emits_exactly_the_expected_names(emitted):
"Emitted span and attribute names match the expected literals."
static, server = _partition(emitted)
by_span = _keys_by_span(static)
assert set(by_span) == EXPECTED_SPANS
assert by_span == EXPECTED_ATTRIBUTES
assert server, "the workload made HTTP requests but no SERVER span was emitted"
union = set()
methods = set()
for name, _kind, attributes in server:
union |= set(attributes)
route = attributes.get("http.route")
# Every request in the workload matches a route
assert route, f"the request span {name!r} carries no http.route"
method, _, name_route = name.partition(" ")
assert name_route == route, (
f"the request span is named {name!r}, which is not the "
f"`{{method}} {{route}}` of {method!r} and {route!r}"
)
methods.add(method)
assert methods == EXPECTED_HTTP_METHOD_NAMES
assert union == EXPECTED_HTTP_ATTRIBUTES
def test_registry_matches_the_expected_names():
"Registry names match the expected literals."
assert {str(span) for span in reg.SPANS} == EXPECTED_REGISTRY_NAMES
for span in reg.SPANS:
assert {
str(attribute) for attribute in span.attributes
} == EXPECTED_REGISTRY_ATTRIBUTES[str(span)], f"{span} attributes have drifted"
@pytest.mark.asyncio
async def test_every_emitted_span_is_registered(emitted):
"A span added without a registry entry would be missing from the docs."
unregistered = sorted(
{name for name, kind, _ in emitted if reg.span_for(name, kind) is None}
)
assert (
not unregistered
), f"these spans are emitted but not in telemetry_registry.SPANS: {unregistered}"
@pytest.mark.asyncio
async def test_every_emitted_attribute_is_registered(emitted):
"An attribute added without a registry entry would be missing from the docs."
unregistered = sorted(
{
f"{name} -> {key}"
for name, kind, keys in emitted
for key in keys
if not reg.attribute_allowed(reg.span_for(name, kind), key)
}
)
assert (
not unregistered
), "these span attributes are emitted but not registered: " + ", ".join(
unregistered
)
@pytest.mark.asyncio
async def test_every_registered_span_is_emitted(emitted):
"The docs should not describe a span that is no longer emitted."
# Compare by identity: the request span's registry name never appears on
# the wire.
resolved = {id(reg.span_for(name, kind)) for name, kind, _ in emitted}
missing = sorted(str(span) for span in reg.SPANS if id(span) not in resolved)
assert not missing, (
f"these spans are documented but never emitted by the workload: {missing}. "
"Either the instrumentation was removed, or exercise() no longer reaches it."
)
@pytest.mark.asyncio
async def test_every_registered_attribute_is_emitted(emitted):
"""
Every registered attribute, including optional ones, is emitted at least
once. If a new attribute only appears in rare cases, extend exercise().
"""
by_entry = {}
for name, kind, keys in emitted:
entry = reg.span_for(name, kind)
if entry is not None:
by_entry.setdefault(id(entry), set()).update(keys)
missing = []
for span in reg.SPANS:
emitted_keys = by_entry.get(id(span), set())
for attribute in span.attributes:
if attribute not in emitted_keys:
missing.append(f"{span} -> {attribute}")
assert not missing, (
"these attributes are documented but never emitted by the workload: "
+ ", ".join(sorted(missing))
)
def test_registry_has_no_duplicate_names():
assert len(set(reg.SPANS)) == len(reg.SPANS)
for span in reg.SPANS:
assert len(set(span.attributes)) == len(
span.attributes
), f"{span} lists an attribute twice"
def test_registry_entries_are_documented():
"Every entry has a description, used to generate the docs."
for span in reg.SPANS:
assert span.description.strip(), f"{span} has no description"
for attribute in span.attributes:
assert attribute.description.strip(), f"{span} -> {attribute} has none"
def test_registry_entries_are_usable_as_plain_strings():
assert isinstance(reg.DB_QUERY, str)
assert isinstance(reg.DB_NAMESPACE, str)
assert reg.DB_QUERY == "db.query"
assert reg.DB_NAMESPACE == "db.namespace"
assert f"{reg.DB_QUERY}.execute" == "db.query.execute"
def test_registry_entries_survive_deepcopy_and_pickle():
"""
A copied or unpickled entry is a plain str. ConsoleMetricExporter
deepcopies metric attributes, which use registry entries as keys.
"""
for entry in (reg.DB_NAMESPACE, reg.DB_QUERY, reg.M_OPERATION_DURATION):
assert copy.deepcopy({entry: 1}) == {str(entry): 1}
assert type(copy.deepcopy(entry)) is str
assert pickle.loads(pickle.dumps(entry)) == str(entry)
# The original entry keeps its metadata
assert entry.description.strip()
@pytest.mark.asyncio
async def test_console_metric_exporter_renders_core_metric_points(otel_metrics):
from opentelemetry.sdk.metrics.export import (
ConsoleMetricExporter,
MetricExportResult,
)
name = _unique("registry_console_export")
ds = Datasette(memory=True)
ds.add_memory_database(name)
await ds.invoke_startup()
# Produces a db.client.operation.duration point keyed by DB_NAMESPACE
await ds.get_database(name).execute("select 1")
data = otel_metrics.reader.get_metrics_data()
assert data is not None, "no metrics captured - nothing to export"
exporter = ConsoleMetricExporter(out=io.StringIO())
assert exporter.export(data) is MetricExportResult.SUCCESS
ds.close()
def test_every_histogram_declares_bucket_boundaries():
"""
Every histogram declares bucket boundaries, and only histograms do.
OpenTelemetry's defaults are meant for milliseconds, not seconds.
"""
for metric in reg.METRICS:
if metric.kind == reg.HISTOGRAM:
assert metric.buckets, f"{metric} is a histogram with no boundaries"
assert list(metric.buckets) == sorted(
set(metric.buckets)
), f"{metric} boundaries must be ascending and unique"
assert metric.buckets[0] > 0, f"{metric} has a non-positive boundary"
else:
assert (
metric.buckets is None
), f"{metric} is a {metric.kind} and cannot have bucket boundaries"
def test_dynamic_span_lookup():
"""
dynamic=True entries such as the request span match on kind. They never
match without a kind, and never override a registered name.
"""
assert reg.span_for("GET", SpanKind.SERVER) is reg.HTTP_REQUEST
assert reg.span_for("POST /^/(?P<database>[^/]+)$", SpanKind.SERVER) is (
reg.HTTP_REQUEST
)
assert reg.span_for("GET") is None
assert reg.span_for("anything at all", SpanKind.INTERNAL) is None
assert reg.span_for("db.query", SpanKind.SERVER) is reg.DB_QUERY
def test_span_and_attribute_lookup():
assert reg.span_for("db.query") is reg.DB_QUERY
assert reg.span_for("datasette.startup") is reg.STARTUP
assert reg.span_for("not.a.datasette.span") is None
assert reg.attribute_allowed(reg.DB_QUERY, "db.namespace")
assert not reg.attribute_allowed(reg.DB_QUERY, "db.namespace.extra")
assert not reg.attribute_allowed(reg.DB_QUERY, "datasette.isolated_connection")
assert not reg.attribute_allowed(None, "db.namespace")
# --- Metric conformance ----------------------------------------------------
@pytest_asyncio.fixture
async def emitted_metrics(otel_metrics):
"""
Metric names and (metric name, attribute key) pairs from a broad workload.
Checks use attribute keys rather than values, since other Datasette
instances in the session can also report points.
"""
# Reaches every synchronous metric except datasette.sql.queries.interrupted
ds = await exercise()
# datasette.sql.queries.interrupted ignores custom_time_limit timeouts, so
# this needs an instance with a low sql_time_limit_ms.
slow_name = _unique("registry_metrics_slow")
slow = Datasette(memory=True, settings={"sql_time_limit_ms": 5})
slow.add_memory_database(slow_name)
await slow.invoke_startup()
slow_db = slow.get_database(slow_name)
with pytest.raises(QueryInterrupted):
await slow_db.execute(
"with recursive c(x) as (select 0 union all select x+1 from c) "
"select * from c"
)
# Collect before closing the instances so the observable gauges report them
otel_metrics.collect()
snapshot = otel_metrics.snapshot
assert snapshot, "no metrics captured - the fixture is not exercising anything"
pairs = set()
for metric_name, points in snapshot.items():
for point in points:
for key in point.attributes or {}:
pairs.add((metric_name, key))
ds.close()
slow.close()
Extend the kit with metric-side conformance: kind, unit and enum checks 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
2026-09-02 12:55:47 -07:00
return {"names": set(snapshot), "pairs": pairs, "collector": otel_metrics}
@pytest.mark.asyncio
Extend the kit with metric-side conformance: kind, unit and enum checks 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
2026-09-02 12:55:47 -07:00
async def test_metrics_conform_to_the_registry(emitted_metrics):
"""
Emitted metric names, kinds, units, attribute keys and enum values match
the registry, using the plugin testing helper.
Extend the kit with metric-side conformance: kind, unit and enum checks 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
2026-09-02 12:55:47 -07:00
"""
assert_metrics_conform(
reg.METRICS, emitted_metrics["collector"], scope_name="datasette"
)
@pytest.mark.asyncio
Extend the kit with metric-side conformance: kind, unit and enum checks 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
2026-09-02 12:55:47 -07:00
async def test_every_registered_metric_is_emitted(emitted_metrics):
assert_metrics_covered(
reg.METRICS, emitted_metrics["collector"], scope_name="datasette"
)
@pytest.mark.asyncio
async def test_every_registered_metric_attribute_is_emitted(emitted_metrics):
"Every registered metric attribute, including optional ones, is emitted."
emitted_keys_by_metric = {}
for metric_name, key in emitted_metrics["pairs"]:
emitted_keys_by_metric.setdefault(metric_name, set()).add(key)
missing = []
for metric in reg.METRICS:
if str(metric) not in emitted_metrics["names"]:
# Reported by test_every_registered_metric_is_emitted
continue
emitted_keys = emitted_keys_by_metric.get(str(metric), set())
for attribute in metric.attributes:
if attribute not in emitted_keys:
missing.append(f"{metric} -> {attribute}")
assert not missing, (
"these metric attributes are documented but never emitted by the "
"test workload: " + ", ".join(sorted(missing))
)
Add a plugin telemetry kit: public registry API, linked_root_span_kwargs, test helpers, docs 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
2026-09-02 12:24:42 -07:00
def test_prefix_span_lookup():
"prefix=True matching, which core does not use but plugin registries can."
Add a plugin telemetry kit: public registry API, linked_root_span_kwargs, test helpers, docs 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
2026-09-02 12:24:42 -07:00
hook = reg.SpanName("myplugin.hook.", "A hypothetical span family", prefix=True)
spans = reg.SPANS + (hook,)
assert reg.span_for("myplugin.hook.render_cell", spans=spans) is hook
assert reg.span_for("myplugin.hook.anything", spans=spans) is hook
assert reg.span_for("myplugin.hookish", spans=spans) is None
assert reg.span_for("db.query", spans=spans) is reg.DB_QUERY
def test_exact_match_wins_over_prefix():
family = reg.SpanName("db.", "Greedy prefix", prefix=True)
spans = (family,) + reg.SPANS
assert reg.span_for("db.query", spans=spans) is reg.DB_QUERY
assert reg.span_for("db.anything-else", spans=spans) is family
def test_attribute_values_enum_enforced():
outcome = reg.Attribute("myplugin.outcome", "Enum.", values={"ok", "error"})
open_attr = reg.Attribute("myplugin.note", "Open value set.")
span = reg.SpanName("myplugin.job", "Test span", (outcome, open_attr))
assert reg.attribute_value_allowed(span, "myplugin.outcome", "ok")
assert not reg.attribute_value_allowed(span, "myplugin.outcome", "surprise")
assert reg.attribute_value_allowed(span, "myplugin.note", "anything at all")
assert not reg.attribute_value_allowed(span, "not.registered", "x")
assert not reg.attribute_value_allowed(None, "myplugin.outcome", "ok")