llama-stack-mirror/tests/integration/telemetry/collectors/base.py

186 lines
6 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
"""Shared helpers for telemetry test collectors."""
from collections.abc import Iterable, Mapping
from dataclasses import dataclass
from typing import Any
@dataclass
class SpanStub:
"""Unified span interface for both in-memory and OTLP collectors."""
name: str
attributes: Mapping[str, Any] | None = None
resource_attributes: dict[str, Any] | None = None
events: list[dict[str, Any]] | None = None
trace_id: str | None = None
span_id: str | None = None
@property
def context(self):
"""Provide context-like interface for trace_id compatibility."""
if self.trace_id is None:
return None
return type("Context", (), {"trace_id": int(self.trace_id, 16)})()
def get_attributes(self) -> dict[str, Any]:
"""Get span attributes as a dictionary.
Handles different attribute types (mapping, dict, etc.) and returns
a consistent dictionary format.
"""
attrs = self.attributes
if attrs is None:
return {}
# Handle mapping-like objects (e.g., mappingproxy)
try:
return dict(attrs.items()) # type: ignore[attr-defined]
except AttributeError:
try:
return dict(attrs)
except TypeError:
return dict(attrs) if attrs else {}
def get_attribute(self, key: str) -> Any:
"""Get a specific attribute value by key."""
attrs = self.get_attributes()
return attrs.get(key)
def get_trace_id(self) -> str | None:
"""Get trace ID in hex format.
Tries context.trace_id first, then falls back to direct trace_id.
"""
context = getattr(self, "context", None)
if context and getattr(context, "trace_id", None) is not None:
return f"{context.trace_id:032x}"
return getattr(self, "trace_id", None)
def has_message(self, text: str) -> bool:
"""Check if span contains a specific message in its args."""
args = self.get_attribute("__args__")
if not args or not isinstance(args, str):
return False
return text in args
def is_root_span(self) -> bool:
"""Check if this is a root span."""
return self.get_attribute("__root__") is True
def is_autotraced(self) -> bool:
"""Check if this span was automatically traced."""
return self.get_attribute("__autotraced__") is True
def get_span_type(self) -> str | None:
"""Get the span type (async, sync, async_generator)."""
return self.get_attribute("__type__")
def get_class_method(self) -> tuple[str | None, str | None]:
"""Get the class and method names for autotraced spans."""
return (self.get_attribute("__class__"), self.get_attribute("__method__"))
def get_location(self) -> str | None:
"""Get the location (library_client, server) for root spans."""
return self.get_attribute("__location__")
def _value_to_python(value: Any) -> Any:
kind = value.WhichOneof("value")
if kind == "string_value":
return value.string_value
if kind == "int_value":
return value.int_value
if kind == "double_value":
return value.double_value
if kind == "bool_value":
return value.bool_value
if kind == "bytes_value":
return value.bytes_value
if kind == "array_value":
return [_value_to_python(item) for item in value.array_value.values]
if kind == "kvlist_value":
return {kv.key: _value_to_python(kv.value) for kv in value.kvlist_value.values}
return None
def attributes_to_dict(key_values: Iterable[Any]) -> dict[str, Any]:
return {key_value.key: _value_to_python(key_value.value) for key_value in key_values}
def events_to_list(events: Iterable[Any]) -> list[dict[str, Any]]:
return [
{
"name": event.name,
"timestamp": event.time_unix_nano,
"attributes": attributes_to_dict(event.attributes),
}
for event in events
]
class BaseTelemetryCollector:
"""Base class for telemetry collectors that ensures consistent return types.
All collectors must return SpanStub objects to ensure test compatibility
across both library-client and server modes.
"""
def get_spans(
self,
expected_count: int | None = None,
timeout: float = 5.0,
poll_interval: float = 0.05,
) -> tuple[SpanStub, ...]:
import time
deadline = time.time() + timeout
min_count = expected_count if expected_count is not None else 1
last_len: int | None = None
stable_iterations = 0
while True:
spans = tuple(self._snapshot_spans())
if len(spans) >= min_count:
if expected_count is not None and len(spans) >= expected_count:
return spans
if last_len == len(spans):
stable_iterations += 1
if stable_iterations >= 2:
return spans
else:
stable_iterations = 1
else:
stable_iterations = 0
if time.time() >= deadline:
return spans
last_len = len(spans)
time.sleep(poll_interval)
def get_metrics(self) -> Any | None:
return self._snapshot_metrics()
def clear(self) -> None:
self._clear_impl()
def _snapshot_spans(self) -> tuple[SpanStub, ...]: # pragma: no cover - interface hook
raise NotImplementedError
def _snapshot_metrics(self) -> Any | None: # pragma: no cover - interface hook
raise NotImplementedError
def _clear_impl(self) -> None: # pragma: no cover - interface hook
raise NotImplementedError
def shutdown(self) -> None:
"""Optional hook for subclasses with background workers."""