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feat(telemetry:major): End to End Testing, Metric Capture, SQL Alchemy Injection

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Emilio Garcia 2025-10-03 12:17:41 -04:00
parent e815738936
commit 7e3cf1fb20
26 changed files with 2075 additions and 1006 deletions
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33
docs/docs/providers/telemetry/inline_otel.mdx Normal file
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@ -0,0 +1,33 @@
---
description: "Native OpenTelemetry provider with full access to OTel Tracer and Meter APIs for advanced instrumentation."
sidebar_label: Otel
title: inline::otel
---
# inline::otel
## Description
Native OpenTelemetry provider with full access to OTel Tracer and Meter APIs for advanced instrumentation.
## Configuration
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `service_name` | `<class 'str'>` | No | | The name of the service to be monitored.
Is overridden by the OTEL_SERVICE_NAME or OTEL_RESOURCE_ATTRIBUTES environment variables. |
| `service_version` | `str \| None` | No | | The version of the service to be monitored.
Is overriden by the OTEL_RESOURCE_ATTRIBUTES environment variable. |
| `deployment_environment` | `str \| None` | No | | The name of the environment of the service to be monitored.
Is overriden by the OTEL_RESOURCE_ATTRIBUTES environment variable. |
| `span_processor` | `BatchSpanProcessor \| SimpleSpanProcessor \| None` | No | batch | The span processor to use.
Is overriden by the OTEL_SPAN_PROCESSOR environment variable. |
## Sample Configuration
```yaml
service_name: ${env.OTEL_SERVICE_NAME:=llama-stack}
service_version: ${env.OTEL_SERVICE_VERSION:=}
deployment_environment: ${env.OTEL_DEPLOYMENT_ENVIRONMENT:=}
span_processor: ${env.OTEL_SPAN_PROCESSOR:=batch}
```

3
llama_stack/core/library_client.py
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@ -32,7 +32,7 @@ from termcolor import cprint
from llama_stack.core.build import print_pip_install_help
from llama_stack.core.configure import parse_and_maybe_upgrade_config
from llama_stack.core.datatypes import Api, BuildConfig, BuildProvider, DistributionSpec
from llama_stack.core.datatypes import BuildConfig, BuildProvider, DistributionSpec
from llama_stack.core.request_headers import (
PROVIDER_DATA_VAR,
request_provider_data_context,
@ -49,7 +49,6 @@ from llama_stack.core.utils.context import preserve_contexts_async_generator
from llama_stack.core.utils.exec import in_notebook
from llama_stack.log import get_logger
logger = get_logger(name=__name__, category="core")
T = TypeVar("T")

8
llama_stack/core/server/server.py
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@ -59,7 +59,6 @@ from llama_stack.core.utils.context import preserve_contexts_async_generator
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import Api
from .auth import AuthenticationMiddleware
from .quota import QuotaMiddleware
@ -232,9 +231,7 @@ def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable:
try:
if is_streaming:
gen = preserve_contexts_async_generator(
sse_generator(func(**kwargs)), [PROVIDER_DATA_VAR]
)
gen = preserve_contexts_async_generator(sse_generator(func(**kwargs)), [PROVIDER_DATA_VAR])
return StreamingResponse(gen, media_type="text/event-stream")
else:
value = func(**kwargs)
@ -278,7 +275,7 @@ def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable:
]
)
setattr(route_handler, "__signature__", sig.replace(parameters=new_params))
route_handler.__signature__ = sig.replace(parameters=new_params)
return route_handler
@ -405,6 +402,7 @@ def create_app() -> StackApp:
if Api.telemetry in impls:
impls[Api.telemetry].fastapi_middleware(app)
impls[Api.telemetry].sqlalchemy_instrumentation()
# Load external APIs if configured
external_apis = load_external_apis(config)

1
llama_stack/core/stack.py
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@ -359,7 +359,6 @@ class Stack:
await refresh_registry_once(impls)
self.impls = impls
# safely access impls without raising an exception
def get_impls(self) -> dict[Api, Any]:
if self.impls is None:

8
llama_stack/core/telemetry/__init__.py
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@ -1,4 +1,10 @@
# 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.
# the root directory of this source tree.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

0
llama_stack/core/telemetry/__initi__.py
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31
llama_stack/core/telemetry/telemetry.py
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@ -4,46 +4,19 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from abc import abstractmethod
from fastapi import FastAPI
from pydantic import BaseModel
from typing import Any
class TelemetryProvider(BaseModel):
"""
TelemetryProvider standardizes how telemetry is provided to the application.
"""
@abstractmethod
def fastapi_middleware(self, app: FastAPI, *args, **kwargs):
"""
Injects FastAPI middleware that instruments the application for telemetry.
"""
...
@abstractmethod
def custom_trace(self, name: str, *args, **kwargs) -> Any:
"""
Creates a custom trace.
"""
...
@abstractmethod
def record_count(self, name: str, *args, **kwargs):
"""
Increments a counter metric.
"""
...
@abstractmethod
def record_histogram(self, name: str, *args, **kwargs):
"""
Records a histogram metric.
"""
...
@abstractmethod
def record_up_down_counter(self, name: str, *args, **kwargs):
"""
Records an up/down counter metric.
"""
...

20
llama_stack/core/telemetry/tracing.py
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@ -1,20 +0,0 @@
# 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.
from abc import abstractmethod
from fastapi import FastAPI
from pydantic import BaseModel
class TelemetryProvider(BaseModel):
"""
TelemetryProvider standardizes how telemetry is provided to the application.
"""
@abstractmethod
def fastapi_middleware(self, app: FastAPI, *args, **kwargs):
"""
Injects FastAPI middleware that instruments the application for telemetry.
"""
...

11
llama_stack/providers/inline/telemetry/meta_reference/middleware.py
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@ -1,15 +1,22 @@
from aiohttp import hdrs
# 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.
from typing import Any
from aiohttp import hdrs
from llama_stack.apis.datatypes import Api
from llama_stack.core.external import ExternalApiSpec
from llama_stack.core.server.routes import find_matching_route, initialize_route_impls
from llama_stack.log import get_logger
from llama_stack.providers.utils.telemetry.tracing import end_trace, start_trace
logger = get_logger(name=__name__, category="telemetry::meta_reference")
class TracingMiddleware:
def __init__(
self,

11
llama_stack/providers/inline/telemetry/meta_reference/telemetry.py
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@ -10,7 +10,6 @@ import threading
from typing import Any, cast
from fastapi import FastAPI
from opentelemetry import metrics, trace
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
@ -23,11 +22,6 @@ from opentelemetry.semconv.attributes import service_attributes
from opentelemetry.trace.propagation.tracecontext import TraceContextTextMapPropagator
from opentelemetry.util.types import Attributes
from llama_stack.core.external import ExternalApiSpec
from llama_stack.core.server.tracing import TelemetryProvider
from llama_stack.providers.inline.telemetry.meta_reference.middleware import TracingMiddleware
from llama_stack.apis.telemetry import (
Event,
MetricEvent,
@ -47,10 +41,13 @@ from llama_stack.apis.telemetry import (
UnstructuredLogEvent,
)
from llama_stack.core.datatypes import Api
from llama_stack.core.external import ExternalApiSpec
from llama_stack.core.server.tracing import TelemetryProvider
from llama_stack.log import get_logger
from llama_stack.providers.inline.telemetry.meta_reference.console_span_processor import (
ConsoleSpanProcessor,
)
from llama_stack.providers.inline.telemetry.meta_reference.middleware import TracingMiddleware
from llama_stack.providers.inline.telemetry.meta_reference.sqlite_span_processor import (
SQLiteSpanProcessor,
)
@ -381,7 +378,7 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry, TelemetryProvider):
max_depth=max_depth,
)
)
def fastapi_middleware(
self,
app: FastAPI,

23
llama_stack/providers/inline/telemetry/otel/__init__.py
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@ -0,0 +1,23 @@
# 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.
from .config import OTelTelemetryConfig
__all__ = ["OTelTelemetryConfig"]
async def get_provider_impl(config: OTelTelemetryConfig, deps):
"""
Get the OTel telemetry provider implementation.
This function is called by the Llama Stack registry to instantiate
the provider.
"""
from .otel import OTelTelemetryProvider
# The provider is synchronously initialized via Pydantic model_post_init
# No async initialization needed
return OTelTelemetryProvider(config=config)

38
llama_stack/providers/inline/telemetry/otel/config.py
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@ -1,8 +1,13 @@
from typing import Literal
# 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.
from typing import Any, Literal
from pydantic import BaseModel, Field
type BatchSpanProcessor = Literal["batch"]
type SimpleSpanProcessor = Literal["simple"]
@ -11,22 +16,35 @@ class OTelTelemetryConfig(BaseModel):
"""
The configuration for the OpenTelemetry telemetry provider.
Most configuration is set using environment variables.
See https://opentelemetry.io/docs/specs/otel/configuration/sdk-environment-variables/ for more information.
See https://opentelemetry.io/docs/specs/otel/configuration/sdk-configuration-variables/ for more information.
"""
service_name: str = Field(
description="""The name of the service to be monitored.
description="""The name of the service to be monitored.
Is overridden by the OTEL_SERVICE_NAME or OTEL_RESOURCE_ATTRIBUTES environment variables.""",
)
service_version: str | None = Field(
description="""The version of the service to be monitored.
Is overriden by the OTEL_RESOURCE_ATTRIBUTES environment variable."""
default=None,
description="""The version of the service to be monitored.
Is overriden by the OTEL_RESOURCE_ATTRIBUTES environment variable.""",
)
deployment_environment: str | None = Field(
description="""The name of the environment of the service to be monitored.
Is overriden by the OTEL_RESOURCE_ATTRIBUTES environment variable."""
default=None,
description="""The name of the environment of the service to be monitored.
Is overriden by the OTEL_RESOURCE_ATTRIBUTES environment variable.""",
)
span_processor: BatchSpanProcessor | SimpleSpanProcessor | None = Field(
description="""The span processor to use.
description="""The span processor to use.
Is overriden by the OTEL_SPAN_PROCESSOR environment variable.""",
default="batch"
default="batch",
)
@classmethod
def sample_run_config(cls, __distro_dir__: str = "") -> dict[str, Any]:
"""Sample configuration for use in distributions."""
return {
"service_name": "${env.OTEL_SERVICE_NAME:=llama-stack}",
"service_version": "${env.OTEL_SERVICE_VERSION:=}",
"deployment_environment": "${env.OTEL_DEPLOYMENT_ENVIRONMENT:=}",
"span_processor": "${env.OTEL_SPAN_PROCESSOR:=batch}",
}

360
llama_stack/providers/inline/telemetry/otel/otel.py
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@ -1,141 +1,301 @@
# 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.
import os
import threading
import time
from opentelemetry import trace, metrics
from opentelemetry.context.context import Context
from opentelemetry.sdk.resources import Attributes, Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor, SimpleSpanProcessor
from fastapi import FastAPI
from opentelemetry import metrics, trace
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.metrics import Counter, UpDownCounter, Histogram, ObservableGauge
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
from opentelemetry.trace import Span, SpanKind, _Links
from typing import Sequence
from pydantic import PrivateAttr
from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor
from opentelemetry.metrics import Counter, Histogram
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import (
BatchSpanProcessor,
SimpleSpanProcessor,
SpanExporter,
SpanExportResult,
)
from sqlalchemy import Engine
from starlette.types import ASGIApp, Message, Receive, Scope, Send
from llama_stack.core.telemetry.tracing import TelemetryProvider
from llama_stack.core.telemetry.telemetry import TelemetryProvider
from llama_stack.log import get_logger
from .config import OTelTelemetryConfig
from fastapi import FastAPI
logger = get_logger(name=__name__, category="telemetry::otel")
class StreamingMetricsMiddleware:
"""
Pure ASGI middleware to track streaming response metrics.
This follows Starlette best practices by implementing pure ASGI,
which is more efficient and less prone to bugs than BaseHTTPMiddleware.
"""
def __init__(self, app: ASGIApp):
self.app = app
async def __call__(self, scope: Scope, receive: Receive, send: Send):
if scope["type"] != "http":
await self.app(scope, receive, send)
return
logger.debug(f"StreamingMetricsMiddleware called for {scope.get('method')} {scope.get('path')}")
start_time = time.time()
# Track if this is a streaming response
is_streaming = False
async def send_wrapper(message: Message):
nonlocal is_streaming
# Detect streaming responses by headers
if message["type"] == "http.response.start":
headers = message.get("headers", [])
for name, value in headers:
if name == b"content-type" and b"text/event-stream" in value:
is_streaming = True
# Add streaming attribute to current span
current_span = trace.get_current_span()
if current_span and current_span.is_recording():
current_span.set_attribute("http.response.is_streaming", True)
break
# Record total duration when response body completes
elif message["type"] == "http.response.body" and not message.get("more_body", False):
if is_streaming:
current_span = trace.get_current_span()
if current_span and current_span.is_recording():
total_duration_ms = (time.time() - start_time) * 1000
current_span.set_attribute("http.streaming.total_duration_ms", total_duration_ms)
await send(message)
await self.app(scope, receive, send_wrapper)
class MetricsSpanExporter(SpanExporter):
"""Records HTTP metrics from span data."""
def __init__(
self,
request_duration: Histogram,
streaming_duration: Histogram,
streaming_requests: Counter,
request_count: Counter,
):
self.request_duration = request_duration
self.streaming_duration = streaming_duration
self.streaming_requests = streaming_requests
self.request_count = request_count
def export(self, spans):
logger.debug(f"MetricsSpanExporter.export called with {len(spans)} spans")
for span in spans:
if not span.attributes or not span.attributes.get("http.method"):
continue
logger.debug(f"Processing span: {span.name}")
if span.end_time is None or span.start_time is None:
continue
# Calculate time-to-first-byte duration
duration_ns = span.end_time - span.start_time
duration_ms = duration_ns / 1_000_000
# Check if this was a streaming response
is_streaming = span.attributes.get("http.response.is_streaming", False)
attributes = {
"http.method": str(span.attributes.get("http.method", "UNKNOWN")),
"http.route": str(span.attributes.get("http.route", span.attributes.get("http.target", "/"))),
"http.status_code": str(span.attributes.get("http.status_code", 0)),
}
# set distributed trace attributes
if span.attributes.get("trace_id"):
attributes["trace_id"] = str(span.attributes.get("trace_id"))
if span.attributes.get("span_id"):
attributes["span_id"] = str(span.attributes.get("span_id"))
# Record request count and duration
logger.debug(f"Recording metrics: duration={duration_ms}ms, attributes={attributes}")
self.request_count.add(1, attributes)
self.request_duration.record(duration_ms, attributes)
logger.debug("Metrics recorded successfully")
# For streaming, record separately
if is_streaming:
logger.debug(f"MetricsSpanExporter: Recording streaming metrics for {span.name}")
self.streaming_requests.add(1, attributes)
# If full streaming duration is available
stream_total_duration = span.attributes.get("http.streaming.total_duration_ms")
if stream_total_duration and isinstance(stream_total_duration, int | float):
logger.debug(f"MetricsSpanExporter: Recording streaming duration: {stream_total_duration}ms")
self.streaming_duration.record(float(stream_total_duration), attributes)
else:
logger.warning(
"MetricsSpanExporter: Streaming span has no http.streaming.total_duration_ms attribute"
)
return SpanExportResult.SUCCESS
def shutdown(self):
pass
# NOTE: DO NOT ALLOW LLM TO MODIFY THIS WITHOUT TESTING AND SUPERVISION: it frequently breaks otel integrations
class OTelTelemetryProvider(TelemetryProvider):
"""
A simple Open Telemetry native telemetry provider.
"""
config: OTelTelemetryConfig
_counters: dict[str, Counter] = PrivateAttr(default_factory=dict)
_up_down_counters: dict[str, UpDownCounter] = PrivateAttr(default_factory=dict)
_histograms: dict[str, Histogram] = PrivateAttr(default_factory=dict)
_gauges: dict[str, ObservableGauge] = PrivateAttr(default_factory=dict)
config: OTelTelemetryConfig
def model_post_init(self, __context):
"""Initialize provider after Pydantic validation."""
self._lock = threading.Lock()
attributes: Attributes = {
key: value
for key, value in {
"service.name": self.config.service_name,
"service.version": self.config.service_version,
"deployment.environment": self.config.deployment_environment,
}.items()
if value is not None
}
resource = Resource.create(attributes)
# Configure the tracer provider
tracer_provider = TracerProvider(resource=resource)
trace.set_tracer_provider(tracer_provider)
otlp_span_exporter = OTLPSpanExporter()
# Configure the span processor
# Enable batching of spans to reduce the number of requests to the collector
if self.config.span_processor == "batch":
tracer_provider.add_span_processor(BatchSpanProcessor(otlp_span_exporter))
elif self.config.span_processor == "simple":
tracer_provider.add_span_processor(SimpleSpanProcessor(otlp_span_exporter))
meter_provider = MeterProvider(resource=resource)
metrics.set_meter_provider(meter_provider)
# Do not fail the application, but warn the user if the endpoints are not set properly.
if not os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT"):
if not os.environ.get("OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"):
logger.warning("OTEL_EXPORTER_OTLP_ENDPOINT or OTEL_EXPORTER_OTLP_TRACES_ENDPOINT is not set. Traces will not be exported.")
logger.warning(
"OTEL_EXPORTER_OTLP_ENDPOINT or OTEL_EXPORTER_OTLP_TRACES_ENDPOINT is not set. Traces will not be exported."
)
if not os.environ.get("OTEL_EXPORTER_OTLP_METRICS_ENDPOINT"):
logger.warning("OTEL_EXPORTER_OTLP_ENDPOINT or OTEL_EXPORTER_OTLP_METRICS_ENDPOINT is not set. Metrics will not be exported.")
logger.warning(
"OTEL_EXPORTER_OTLP_ENDPOINT or OTEL_EXPORTER_OTLP_METRICS_ENDPOINT is not set. Metrics will not be exported."
)
# Respect OTEL design standards where environment variables get highest precedence
service_name = os.environ.get("OTEL_SERVICE_NAME")
if not service_name:
service_name = self.config.service_name
# Create resource with service name
resource = Resource.create({"service.name": service_name})
# Configure the tracer provider (always, since llama stack run spawns subprocess without opentelemetry-instrument)
tracer_provider = TracerProvider(resource=resource)
trace.set_tracer_provider(tracer_provider)
# Configure OTLP span exporter
otlp_span_exporter = OTLPSpanExporter()
# Add span processor (simple for immediate export, batch for performance)
span_processor_type = os.environ.get("OTEL_SPAN_PROCESSOR", "batch")
if span_processor_type == "batch":
tracer_provider.add_span_processor(BatchSpanProcessor(otlp_span_exporter))
else:
tracer_provider.add_span_processor(SimpleSpanProcessor(otlp_span_exporter))
# Configure meter provider with OTLP exporter for metrics
metric_reader = PeriodicExportingMetricReader(OTLPMetricExporter())
meter_provider = MeterProvider(resource=resource, metric_readers=[metric_reader])
metrics.set_meter_provider(meter_provider)
logger.info(
f"Initialized OpenTelemetry provider with service.name={service_name}, span_processor={span_processor_type}"
)
def fastapi_middleware(self, app: FastAPI):
FastAPIInstrumentor.instrument_app(app)
def custom_trace(self,
name: str,
context: Context | None = None,
kind: SpanKind = SpanKind.INTERNAL,
attributes: Attributes = {},
links: _Links = None,
start_time: int | None = None,
record_exception: bool = True,
set_status_on_exception: bool = True) -> Span:
"""
Creates a custom tracing span using the Open Telemetry SDK.
Instrument FastAPI with OTel for automatic tracing and metrics.
Captures telemetry for both regular and streaming HTTP requests:
- Distributed traces (via FastAPIInstrumentor)
- HTTP request metrics (count, duration, status)
- Streaming-specific metrics (time-to-first-byte, total stream duration)
"""
tracer = trace.get_tracer(__name__)
return tracer.start_span(name, context, kind, attributes, links, start_time, record_exception, set_status_on_exception)
# Create meter for HTTP metrics
meter = metrics.get_meter("llama_stack.http.server")
def record_count(self, name: str, amount: int|float, context: Context | None = None, attributes: dict[str, str] | None = None, unit: str = "", description: str = ""):
"""
Increments a counter metric using the Open Telemetry SDK that are indexed by the meter name.
This function is designed to be compatible with other popular telemetry providers design patterns,
like Datadog and New Relic.
"""
meter = metrics.get_meter(__name__)
# HTTP Metrics following OTel semantic conventions
# https://opentelemetry.io/docs/specs/semconv/http/http-metrics/
request_duration = meter.create_histogram(
"http.server.request.duration",
unit="ms",
description="Duration of HTTP requests (time-to-first-byte for streaming)",
)
with self._lock:
if name not in self._counters:
self._counters[name] = meter.create_counter(name, unit=unit, description=description)
counter = self._counters[name]
streaming_duration = meter.create_histogram(
"http.server.streaming.duration",
unit="ms",
description="Total duration of streaming responses (from start to stream completion)",
)
counter.add(amount, attributes=attributes, context=context)
request_count = meter.create_counter(
"http.server.request.count", unit="requests", description="Total number of HTTP requests"
)
streaming_requests = meter.create_counter(
"http.server.streaming.count", unit="requests", description="Number of streaming requests"
)
def record_histogram(self, name: str, value: int|float, context: Context | None = None, attributes: dict[str, str] | None = None, unit: str = "", description: str = "", explicit_bucket_boundaries_advisory: Sequence[float] | None = None):
"""
Records a histogram metric using the Open Telemetry SDK that are indexed by the meter name.
This function is designed to be compatible with other popular telemetry providers design patterns,
like Datadog and New Relic.
"""
meter = metrics.get_meter(__name__)
# Hook to enrich spans and record initial metrics
def server_request_hook(span, scope):
"""
Called by FastAPIInstrumentor for each request.
with self._lock:
if name not in self._histograms:
self._histograms[name] = meter.create_histogram(name, unit=unit, description=description, explicit_bucket_boundaries_advisory=explicit_bucket_boundaries_advisory)
histogram = self._histograms[name]
This only reads from scope (ASGI dict), never touches request body.
Safe to use without interfering with body parsing.
"""
method = scope.get("method", "UNKNOWN")
path = scope.get("path", "/")
histogram.record(value, attributes=attributes, context=context)
# Add custom attributes
span.set_attribute("service.component", "llama-stack-api")
span.set_attribute("http.request", path)
span.set_attribute("http.method", method)
attributes = {
"http.request": path,
"http.method": method,
"trace_id": span.attributes.get("trace_id", ""),
"span_id": span.attributes.get("span_id", ""),
}
def record_up_down_counter(self, name: str, value: int|float, context: Context | None = None, attributes: dict[str, str] | None = None, unit: str = "", description: str = ""):
"""
Records an up/down counter metric using the Open Telemetry SDK that are indexed by the meter name.
This function is designed to be compatible with other popular telemetry providers design patterns,
like Datadog and New Relic.
"""
meter = metrics.get_meter(__name__)
request_count.add(1, attributes)
logger.debug(f"server_request_hook: recorded request_count for {method} {path}, attributes={attributes}")
with self._lock:
if name not in self._up_down_counters:
self._up_down_counters[name] = meter.create_up_down_counter(name, unit=unit, description=description)
up_down_counter = self._up_down_counters[name]
# NOTE: This is called BEFORE routes are added to the app
# FastAPIInstrumentor.instrument_app() patches build_middleware_stack(),
# which will be called on first request (after routes are added), so hooks should work.
logger.debug("Instrumenting FastAPI (routes will be added later)")
FastAPIInstrumentor.instrument_app(
app,
server_request_hook=server_request_hook,
)
logger.debug(f"FastAPI instrumented: {getattr(app, '_is_instrumented_by_opentelemetry', False)}")
up_down_counter.add(value, attributes=attributes, context=context)
# Add pure ASGI middleware for streaming metrics (always add, regardless of instrumentation)
app.add_middleware(StreamingMetricsMiddleware)
# Add metrics span processor
provider = trace.get_tracer_provider()
logger.debug(f"TracerProvider: {provider}")
if isinstance(provider, TracerProvider):
metrics_exporter = MetricsSpanExporter(
request_duration=request_duration,
streaming_duration=streaming_duration,
streaming_requests=streaming_requests,
request_count=request_count,
)
provider.add_span_processor(BatchSpanProcessor(metrics_exporter))
logger.debug("Added MetricsSpanExporter as BatchSpanProcessor")
else:
logger.warning(
f"TracerProvider is not TracerProvider instance, it's {type(provider)}. MetricsSpanExporter not added."
)

12
llama_stack/providers/registry/telemetry.py
View file

@ -26,4 +26,16 @@ def available_providers() -> list[ProviderSpec]:
config_class="llama_stack.providers.inline.telemetry.meta_reference.config.TelemetryConfig",
description="Meta's reference implementation of telemetry and observability using OpenTelemetry.",
),
InlineProviderSpec(
api=Api.telemetry,
provider_type="inline::otel",
pip_packages=[
"opentelemetry-sdk",
"opentelemetry-exporter-otlp-proto-http",
"opentelemetry-instrumentation-fastapi",
],
module="llama_stack.providers.inline.telemetry.otel",
config_class="llama_stack.providers.inline.telemetry.otel.config.OTelTelemetryConfig",
description="Native OpenTelemetry provider with full access to OTel Tracer and Meter APIs for advanced instrumentation.",
),
]

1
pyproject.toml
View file

@ -52,6 +52,7 @@ dependencies = [
"sqlalchemy[asyncio]>=2.0.41", # server - for conversations
"opentelemetry-semantic-conventions>=0.57b0",
"opentelemetry-instrumentation-fastapi>=0.57b0",
"opentelemetry-instrumentation-sqlalchemy>=0.57b0",
]
[project.optional-dependencies]

5
tests/integration/telemetry/__init__.py Normal file
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@ -0,0 +1,5 @@
# 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.

149
tests/integration/telemetry/mocking/README.md Normal file
View file

@ -0,0 +1,149 @@
# Mock Server Infrastructure
This directory contains mock servers for E2E telemetry testing.
## Structure
```
mocking/
├── README.md ← You are here
├── __init__.py ← Module exports
├── mock_base.py ← Pydantic base class for all mocks
├── servers.py ← Mock server implementations
└── harness.py ← Async startup harness
```
## Files
### `mock_base.py` - Base Class
Pydantic base model that all mock servers must inherit from.
**Contract:**
```python
class MockServerBase(BaseModel):
async def await_start(self):
# Start server and wait until ready
...
def stop(self):
# Stop server and cleanup
...
```
### `servers.py` - Mock Implementations
Contains:
- **MockOTLPCollector** - Receives OTLP telemetry (port 4318)
- **MockVLLMServer** - Simulates vLLM inference API (port 8000)
### `harness.py` - Startup Orchestration
Provides:
- **MockServerConfig** - Pydantic config for server registration
- **start_mock_servers_async()** - Starts servers in parallel
- **stop_mock_servers()** - Stops all servers
## Creating a New Mock Server
### Step 1: Implement the Server
Add to `servers.py`:
```python
class MockRedisServer(MockServerBase):
"""Mock Redis server."""
port: int = Field(default=6379)
# Non-Pydantic fields
server: Any = Field(default=None, exclude=True)
def model_post_init(self, __context):
self.server = None
async def await_start(self):
"""Start Redis mock and wait until ready."""
# Start your server
self.server = create_redis_server(self.port)
self.server.start()
# Wait for port to be listening
for _ in range(10):
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
if sock.connect_ex(("localhost", self.port)) == 0:
sock.close()
return # Ready!
await asyncio.sleep(0.1)
def stop(self):
if self.server:
self.server.stop()
```
### Step 2: Register in Test
In `test_otel_e2e.py`, add to MOCK_SERVERS list:
```python
MOCK_SERVERS = [
# ... existing servers ...
MockServerConfig(
name="Mock Redis",
server_class=MockRedisServer,
init_kwargs={"port": 6379},
),
]
```
### Step 3: Done!
The harness automatically:
- Creates the server instance
- Calls `await_start()` in parallel with other servers
- Returns when all are ready
- Stops all servers on teardown
## Benefits
✅ **Parallel Startup** - All servers start simultaneously
✅ **Type-Safe** - Pydantic validation
✅ **Simple** - Just implement 2 methods
✅ **Fast** - No HTTP polling, direct port checking
✅ **Clean** - Async/await pattern
## Usage in Tests
```python
@pytest.fixture(scope="module")
def mock_servers():
servers = asyncio.run(start_mock_servers_async(MOCK_SERVERS))
yield servers
stop_mock_servers(servers)
# Access specific servers
@pytest.fixture(scope="module")
def mock_redis(mock_servers):
return mock_servers["Mock Redis"]
```
## Key Design Decisions
### Why Pydantic?
- Type safety for server configuration
- Built-in validation
- Clear interface contract
### Why `await_start()` instead of HTTP `/ready`?
- Faster (no HTTP round-trip)
- Simpler (direct port checking)
- More reliable (internal state, not external endpoint)
### Why separate harness?
- Reusable across different test files
- Easy to add new servers
- Centralized error handling
## Examples
See `test_otel_e2e.py` for real-world usage:
- Line ~200: MOCK_SERVERS configuration
- Line ~230: Convenience fixtures
- Line ~240: Using servers in tests

28
tests/integration/telemetry/mocking/__init__.py Normal file
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@ -0,0 +1,28 @@
# 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.
"""
Mock server infrastructure for telemetry E2E testing.
This module provides:
- MockServerBase: Pydantic base class for all mock servers
- MockOTLPCollector: Mock OTLP telemetry collector
- MockVLLMServer: Mock vLLM inference server
- Mock server harness for parallel async startup
"""
from .harness import MockServerConfig, start_mock_servers_async, stop_mock_servers
from .mock_base import MockServerBase
from .servers import MockOTLPCollector, MockVLLMServer
__all__ = [
"MockServerBase",
"MockOTLPCollector",
"MockVLLMServer",
"MockServerConfig",
"start_mock_servers_async",
"stop_mock_servers",
]

106
tests/integration/telemetry/mocking/harness.py Normal file
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@ -0,0 +1,106 @@
# 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.
"""
Mock server startup harness for parallel initialization.
HOW TO ADD A NEW MOCK SERVER:
1. Import your mock server class
2. Add it to MOCK_SERVERS list with configuration
3. Done! It will start in parallel with others.
"""
import asyncio
from typing import Any
from pydantic import BaseModel, Field
from .mock_base import MockServerBase
class MockServerConfig(BaseModel):
"""
Configuration for a mock server to start.
**TO ADD A NEW MOCK SERVER:**
Just create a MockServerConfig instance with your server class.
Example:
MockServerConfig(
name="Mock MyService",
server_class=MockMyService,
init_kwargs={"port": 9000, "config_param": "value"},
)
"""
model_config = {"arbitrary_types_allowed": True}
name: str = Field(description="Display name for logging")
server_class: type = Field(description="Mock server class (must inherit from MockServerBase)")
init_kwargs: dict[str, Any] = Field(default_factory=dict, description="Kwargs to pass to server constructor")
async def start_mock_servers_async(mock_servers_config: list[MockServerConfig]) -> dict[str, MockServerBase]:
"""
Start all mock servers in parallel and wait for them to be ready.
**HOW IT WORKS:**
1. Creates all server instances
2. Calls await_start() on all servers in parallel
3. Returns when all are ready
**SIMPLE TO USE:**
servers = await start_mock_servers_async([config1, config2, ...])
Args:
mock_servers_config: List of mock server configurations
Returns:
Dict mapping server name to server instance
"""
servers = {}
start_tasks = []
# Create all servers and prepare start tasks
for config in mock_servers_config:
server = config.server_class(**config.init_kwargs)
servers[config.name] = server
start_tasks.append(server.await_start())
# Start all servers in parallel
try:
await asyncio.gather(*start_tasks)
# Print readiness confirmation
for name in servers.keys():
print(f"[INFO] {name} ready")
except Exception as e:
# If any server fails, stop all servers
for server in servers.values():
try:
server.stop()
except Exception:
pass
raise RuntimeError(f"Failed to start mock servers: {e}") from None
return servers
def stop_mock_servers(servers: dict[str, Any]):
"""
Stop all mock servers.
Args:
servers: Dict of server instances from start_mock_servers_async()
"""
for name, server in servers.items():
try:
if hasattr(server, "get_request_count"):
print(f"\n[INFO] {name} received {server.get_request_count()} requests")
server.stop()
except Exception as e:
print(f"[WARN] Error stopping {name}: {e}")

68
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@ -0,0 +1,68 @@
# 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.
"""
Base class for mock servers with async startup support.
All mock servers should inherit from MockServerBase and implement await_start().
"""
from abc import abstractmethod
from pydantic import BaseModel
class MockServerBase(BaseModel):
"""
Pydantic base model for mock servers.
**TO CREATE A NEW MOCK SERVER:**
1. Inherit from this class
2. Implement async def await_start(self)
3. Implement def stop(self)
4. Done!
Example:
class MyMockServer(MockServerBase):
port: int = 8080
async def await_start(self):
# Start your server
self.server = create_server()
self.server.start()
# Wait until ready (can check internal state, no HTTP needed)
while not self.server.is_listening():
await asyncio.sleep(0.1)
def stop(self):
if self.server:
self.server.stop()
"""
model_config = {"arbitrary_types_allowed": True}
@abstractmethod
async def await_start(self):
"""
Start the server and wait until it's ready.
This method should:
1. Start the server (synchronous or async)
2. Wait until the server is fully ready to accept requests
3. Return when ready
Subclasses can check internal state directly - no HTTP polling needed!
"""
...
@abstractmethod
def stop(self):
"""
Stop the server and clean up resources.
This method should gracefully shut down the server.
"""
...

600
tests/integration/telemetry/mocking/servers.py Normal file
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@ -0,0 +1,600 @@
# 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.
"""
Mock servers for OpenTelemetry E2E testing.
This module provides mock servers for testing telemetry:
- MockOTLPCollector: Receives and stores OTLP telemetry exports
- MockVLLMServer: Simulates vLLM inference API with valid OpenAI responses
These mocks allow E2E testing without external dependencies.
"""
import asyncio
import http.server
import json
import socket
import threading
import time
from collections import defaultdict
from typing import Any
from opentelemetry.proto.collector.metrics.v1.metrics_service_pb2 import (
ExportMetricsServiceRequest,
)
from opentelemetry.proto.collector.trace.v1.trace_service_pb2 import (
ExportTraceServiceRequest,
)
from pydantic import Field
from .mock_base import MockServerBase
class MockOTLPCollector(MockServerBase):
"""
Mock OTLP collector HTTP server.
Receives real OTLP exports from Llama Stack and stores them for verification.
Runs on localhost:4318 (standard OTLP HTTP port).
Usage:
collector = MockOTLPCollector()
await collector.await_start()
# ... run tests ...
print(f"Received {collector.get_trace_count()} traces")
collector.stop()
"""
port: int = Field(default=4318, description="Port to run collector on")
# Non-Pydantic fields (set after initialization)
traces: list[dict] = Field(default_factory=list, exclude=True)
metrics: list[dict] = Field(default_factory=list, exclude=True)
all_http_requests: list[dict] = Field(default_factory=list, exclude=True) # Track ALL HTTP requests for debugging
server: Any = Field(default=None, exclude=True)
server_thread: Any = Field(default=None, exclude=True)
def model_post_init(self, __context):
"""Initialize after Pydantic validation."""
self.traces = []
self.metrics = []
self.server = None
self.server_thread = None
def _create_handler_class(self):
"""Create the HTTP handler class for this collector instance."""
collector_self = self
class OTLPHandler(http.server.BaseHTTPRequestHandler):
"""HTTP request handler for OTLP requests."""
def log_message(self, format, *args):
"""Suppress HTTP server logs."""
pass
def do_GET(self): # noqa: N802
"""Handle GET requests."""
# No readiness endpoint needed - using await_start() instead
self.send_response(404)
self.end_headers()
def do_POST(self): # noqa: N802
"""Handle OTLP POST requests."""
content_length = int(self.headers.get("Content-Length", 0))
body = self.rfile.read(content_length) if content_length > 0 else b""
# Track ALL requests for debugging
collector_self.all_http_requests.append(
{
"method": "POST",
"path": self.path,
"timestamp": time.time(),
"body_length": len(body),
}
)
# Store the export request
if "/v1/traces" in self.path:
collector_self.traces.append(
{
"body": body,
"timestamp": time.time(),
}
)
elif "/v1/metrics" in self.path:
collector_self.metrics.append(
{
"body": body,
"timestamp": time.time(),
}
)
# Always return success (200 OK)
self.send_response(200)
self.send_header("Content-Type", "application/json")
self.end_headers()
self.wfile.write(b"{}")
return OTLPHandler
async def await_start(self):
"""
Start the OTLP collector and wait until ready.
This method is async and can be awaited to ensure the server is ready.
"""
# Create handler and start the HTTP server
handler_class = self._create_handler_class()
self.server = http.server.HTTPServer(("localhost", self.port), handler_class)
self.server_thread = threading.Thread(target=self.server.serve_forever, daemon=True)
self.server_thread.start()
# Wait for server to be listening on the port
for _ in range(10):
try:
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
result = sock.connect_ex(("localhost", self.port))
sock.close()
if result == 0:
# Port is listening
return
except Exception:
pass
await asyncio.sleep(0.1)
raise RuntimeError(f"OTLP collector failed to start on port {self.port}")
def stop(self):
"""Stop the OTLP collector server."""
if self.server:
self.server.shutdown()
self.server.server_close()
def clear(self):
"""Clear all captured telemetry data."""
self.traces = []
self.metrics = []
def get_trace_count(self) -> int:
"""Get number of trace export requests received."""
return len(self.traces)
def get_metric_count(self) -> int:
"""Get number of metric export requests received."""
return len(self.metrics)
def get_all_traces(self) -> list[dict]:
"""Get all captured trace exports."""
return self.traces
def get_all_metrics(self) -> list[dict]:
"""Get all captured metric exports."""
return self.metrics
# -----------------------------
# Trace parsing helpers
# -----------------------------
def parse_traces(self) -> dict[str, list[dict]]:
"""
Parse protobuf trace data and return spans grouped by trace ID.
Returns:
Dict mapping trace_id (hex) -> list of span dicts
"""
trace_id_to_spans: dict[str, list[dict]] = {}
for export in self.traces:
request = ExportTraceServiceRequest()
body = export.get("body", b"")
try:
request.ParseFromString(body)
except Exception as e:
raise RuntimeError(f"Failed to parse OTLP traces export (len={len(body)}): {e}") from e
for resource_span in request.resource_spans:
for scope_span in resource_span.scope_spans:
for span in scope_span.spans:
# span.trace_id is bytes; convert to hex string
trace_id = (
span.trace_id.hex() if isinstance(span.trace_id, bytes | bytearray) else str(span.trace_id)
)
span_entry = {
"name": span.name,
"span_id": span.span_id.hex()
if isinstance(span.span_id, bytes | bytearray)
else str(span.span_id),
"start_time_unix_nano": int(getattr(span, "start_time_unix_nano", 0)),
"end_time_unix_nano": int(getattr(span, "end_time_unix_nano", 0)),
}
trace_id_to_spans.setdefault(trace_id, []).append(span_entry)
return trace_id_to_spans
def get_all_trace_ids(self) -> set[str]:
"""Return set of all trace IDs seen so far."""
return set(self.parse_traces().keys())
def get_trace_span_counts(self) -> dict[str, int]:
"""Return span counts per trace ID."""
grouped = self.parse_traces()
return {tid: len(spans) for tid, spans in grouped.items()}
def get_new_trace_ids(self, prior_ids: set[str]) -> set[str]:
"""Return trace IDs that appeared after prior_ids snapshot."""
return self.get_all_trace_ids() - set(prior_ids)
def parse_metrics(self) -> dict[str, list[Any]]:
"""
Parse protobuf metric data and return metrics by name.
Returns:
Dict mapping metric names to list of metric data points
"""
metrics_by_name = defaultdict(list)
for export in self.metrics:
# Parse the protobuf body
request = ExportMetricsServiceRequest()
body = export.get("body", b"")
try:
request.ParseFromString(body)
except Exception as e:
raise RuntimeError(f"Failed to parse OTLP metrics export (len={len(body)}): {e}") from e
# Extract metrics from the request
for resource_metric in request.resource_metrics:
for scope_metric in resource_metric.scope_metrics:
for metric in scope_metric.metrics:
metric_name = metric.name
# Extract data points based on metric type
data_points = []
if metric.HasField("gauge"):
data_points = list(metric.gauge.data_points)
elif metric.HasField("sum"):
data_points = list(metric.sum.data_points)
elif metric.HasField("histogram"):
data_points = list(metric.histogram.data_points)
elif metric.HasField("summary"):
data_points = list(metric.summary.data_points)
metrics_by_name[metric_name].extend(data_points)
return dict(metrics_by_name)
def get_metric_by_name(self, metric_name: str) -> list[Any]:
"""
Get all data points for a specific metric by name.
Args:
metric_name: The name of the metric to retrieve
Returns:
List of data points for the metric, or empty list if not found
"""
metrics = self.parse_metrics()
return metrics.get(metric_name, [])
def has_metric(self, metric_name: str) -> bool:
"""
Check if a metric with the given name has been captured.
Args:
metric_name: The name of the metric to check
Returns:
True if the metric exists and has data points, False otherwise
"""
data_points = self.get_metric_by_name(metric_name)
return len(data_points) > 0
def get_all_metric_names(self) -> list[str]:
"""
Get all unique metric names that have been captured.
Returns:
List of metric names
"""
return list(self.parse_metrics().keys())
class MockVLLMServer(MockServerBase):
"""
Mock vLLM inference server with OpenAI-compatible API.
Returns valid OpenAI Python client response objects for:
- Chat completions (/v1/chat/completions)
- Text completions (/v1/completions)
- Model listing (/v1/models)
Runs on localhost:8000 (standard vLLM port).
Usage:
server = MockVLLMServer(models=["my-model"])
await server.await_start()
# ... make inference calls ...
print(f"Handled {server.get_request_count()} requests")
server.stop()
"""
port: int = Field(default=8000, description="Port to run server on")
models: list[str] = Field(
default_factory=lambda: ["meta-llama/Llama-3.2-1B-Instruct"], description="List of model IDs to serve"
)
# Non-Pydantic fields
requests_received: list[dict] = Field(default_factory=list, exclude=True)
server: Any = Field(default=None, exclude=True)
server_thread: Any = Field(default=None, exclude=True)
def model_post_init(self, __context):
"""Initialize after Pydantic validation."""
self.requests_received = []
self.server = None
self.server_thread = None
def _create_handler_class(self):
"""Create the HTTP handler class for this vLLM instance."""
server_self = self
class VLLMHandler(http.server.BaseHTTPRequestHandler):
"""HTTP request handler for vLLM API."""
def log_message(self, format, *args):
"""Suppress HTTP server logs."""
pass
def log_request(self, code="-", size="-"):
"""Log incoming requests for debugging."""
print(f"[DEBUG] Mock vLLM received: {self.command} {self.path} -> {code}")
def do_GET(self): # noqa: N802
"""Handle GET requests (models list, health check)."""
# Log GET requests too
server_self.requests_received.append(
{
"path": self.path,
"method": "GET",
"timestamp": time.time(),
}
)
if self.path == "/v1/models":
response = self._create_models_list_response()
self._send_json_response(200, response)
elif self.path == "/health" or self.path == "/v1/health":
self._send_json_response(200, {"status": "healthy"})
else:
self.send_response(404)
self.end_headers()
def do_POST(self): # noqa: N802
"""Handle POST requests (chat/text completions)."""
content_length = int(self.headers.get("Content-Length", 0))
body = self.rfile.read(content_length) if content_length > 0 else b"{}"
try:
request_data = json.loads(body)
except Exception:
request_data = {}
# Log the request
server_self.requests_received.append(
{
"path": self.path,
"body": request_data,
"timestamp": time.time(),
}
)
# Route to appropriate handler
if "/chat/completions" in self.path:
response = self._create_chat_completion_response(request_data)
if response is not None: # None means already sent (streaming)
self._send_json_response(200, response)
elif "/completions" in self.path:
response = self._create_text_completion_response(request_data)
self._send_json_response(200, response)
else:
self._send_json_response(200, {"status": "ok"})
# ----------------------------------------------------------------
# Response Generators
# **TO MODIFY RESPONSES:** Edit these methods
# ----------------------------------------------------------------
def _create_models_list_response(self) -> dict:
"""Create OpenAI models list response with configured models."""
return {
"object": "list",
"data": [
{
"id": model_id,
"object": "model",
"created": int(time.time()),
"owned_by": "meta",
}
for model_id in server_self.models
],
}
def _create_chat_completion_response(self, request_data: dict) -> dict | None:
"""
Create OpenAI ChatCompletion response.
Returns a valid response matching openai.types.ChatCompletion.
Supports both regular and streaming responses.
Returns None for streaming responses (already sent via SSE).
"""
# Check if streaming is requested
is_streaming = request_data.get("stream", False)
if is_streaming:
# Return SSE streaming response
self.send_response(200)
self.send_header("Content-Type", "text/event-stream")
self.send_header("Cache-Control", "no-cache")
self.send_header("Connection", "keep-alive")
self.end_headers()
# Send streaming chunks
chunks = [
{
"id": "chatcmpl-test",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": request_data.get("model", "test"),
"choices": [{"index": 0, "delta": {"role": "assistant"}, "finish_reason": None}],
},
{
"id": "chatcmpl-test",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": request_data.get("model", "test"),
"choices": [{"index": 0, "delta": {"content": "Test "}, "finish_reason": None}],
},
{
"id": "chatcmpl-test",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": request_data.get("model", "test"),
"choices": [{"index": 0, "delta": {"content": "streaming "}, "finish_reason": None}],
},
{
"id": "chatcmpl-test",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": request_data.get("model", "test"),
"choices": [{"index": 0, "delta": {"content": "response"}, "finish_reason": None}],
},
{
"id": "chatcmpl-test",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": request_data.get("model", "test"),
"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
},
]
for chunk in chunks:
self.wfile.write(f"data: {json.dumps(chunk)}\n\n".encode())
self.wfile.write(b"data: [DONE]\n\n")
return None # Already sent response
# Regular response
return {
"id": "chatcmpl-test123",
"object": "chat.completion",
"created": int(time.time()),
"model": request_data.get("model", "meta-llama/Llama-3.2-1B-Instruct"),
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "This is a test response from mock vLLM server.",
"tool_calls": None,
},
"logprobs": None,
"finish_reason": "stop",
}
],
"usage": {
"prompt_tokens": 25,
"completion_tokens": 15,
"total_tokens": 40,
"completion_tokens_details": None,
},
"system_fingerprint": None,
"service_tier": None,
}
def _create_text_completion_response(self, request_data: dict) -> dict:
"""
Create OpenAI Completion response.
Returns a valid response matching openai.types.Completion
"""
return {
"id": "cmpl-test123",
"object": "text_completion",
"created": int(time.time()),
"model": request_data.get("model", "meta-llama/Llama-3.2-1B-Instruct"),
"choices": [
{
"text": "This is a test completion.",
"index": 0,
"logprobs": None,
"finish_reason": "stop",
}
],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 8,
"total_tokens": 18,
"completion_tokens_details": None,
},
"system_fingerprint": None,
}
def _send_json_response(self, status_code: int, data: dict):
"""Helper to send JSON response."""
self.send_response(status_code)
self.send_header("Content-Type", "application/json")
self.end_headers()
self.wfile.write(json.dumps(data).encode())
return VLLMHandler
async def await_start(self):
"""
Start the vLLM server and wait until ready.
This method is async and can be awaited to ensure the server is ready.
"""
# Create handler and start the HTTP server
handler_class = self._create_handler_class()
self.server = http.server.HTTPServer(("localhost", self.port), handler_class)
self.server_thread = threading.Thread(target=self.server.serve_forever, daemon=True)
self.server_thread.start()
# Wait for server to be listening on the port
for _ in range(10):
try:
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
result = sock.connect_ex(("localhost", self.port))
sock.close()
if result == 0:
# Port is listening
return
except Exception:
pass
await asyncio.sleep(0.1)
raise RuntimeError(f"vLLM server failed to start on port {self.port}")
def stop(self):
"""Stop the vLLM server."""
if self.server:
self.server.shutdown()
self.server.server_close()
def clear(self):
"""Clear request history."""
self.requests_received = []
def get_request_count(self) -> int:
"""Get number of requests received."""
return len(self.requests_received)
def get_all_requests(self) -> list[dict]:
"""Get all received requests with their bodies."""
return self.requests_received

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tests/integration/telemetry/test_otel_e2e.py Normal file
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@ -0,0 +1,622 @@
# 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.
"""
End-to-end tests for the OpenTelemetry inline provider.
What this does:
- Boots mock OTLP and mock vLLM
- Starts a real Llama Stack with inline OTel
- Calls real HTTP APIs
- Verifies traces, metrics, and custom metric names (non-empty)
"""
# ============================================================================
# IMPORTS
# ============================================================================
import os
import socket
import subprocess
import time
from typing import Any
import pytest
import requests
import yaml
from pydantic import BaseModel, Field
# Mock servers are in the mocking/ subdirectory
from .mocking import (
MockOTLPCollector,
MockServerConfig,
MockVLLMServer,
start_mock_servers_async,
stop_mock_servers,
)
# ============================================================================
# DATA MODELS
# ============================================================================
class TelemetryTestCase(BaseModel):
"""
Pydantic model defining expected telemetry for an API call.
**TO ADD A NEW TEST CASE:** Add to TEST_CASES list below.
"""
name: str = Field(description="Unique test case identifier")
http_method: str = Field(description="HTTP method (GET, POST, etc.)")
api_path: str = Field(description="API path (e.g., '/v1/models')")
request_body: dict[str, Any] | None = Field(default=None)
expected_http_status: int = Field(default=200)
expected_trace_exports: int = Field(default=1, description="Minimum number of trace exports expected")
expected_metric_exports: int = Field(default=0, description="Minimum number of metric exports expected")
should_have_error_span: bool = Field(default=False)
expected_metrics: list[str] = Field(
default_factory=list, description="List of metric names that should be captured"
)
expected_min_spans: int | None = Field(
default=None, description="If set, minimum number of spans expected in the new trace(s) generated by this test"
)
# ============================================================================
# TEST CONFIGURATION
# **TO ADD NEW TESTS:** Add TelemetryTestCase instances here
# ============================================================================
# Custom metric names (defined in llama_stack/providers/inline/telemetry/otel/otel.py)
CUSTOM_METRICS_BASE = [
"http.server.request.duration",
"http.server.request.count",
]
CUSTOM_METRICS_STREAMING = [
"http.server.streaming.duration",
"http.server.streaming.count",
]
TEST_CASES = [
TelemetryTestCase(
name="models_list",
http_method="GET",
api_path="/v1/models",
expected_trace_exports=1, # Single trace with 2-3 spans (GET, http send)
expected_metric_exports=1, # Metrics export periodically, but we'll wait for them
expected_metrics=[], # First request: middleware may not be initialized yet
expected_min_spans=2,
),
TelemetryTestCase(
name="chat_completion",
http_method="POST",
api_path="/v1/chat/completions",
request_body={
"model": "meta-llama/Llama-3.2-1B-Instruct",
"messages": [{"role": "user", "content": "Hello!"}],
},
expected_trace_exports=1, # Single trace with 4 spans (POST, http receive, 2x http send)
expected_metric_exports=1, # Metrics export periodically
expected_metrics=CUSTOM_METRICS_BASE,
expected_min_spans=3,
),
TelemetryTestCase(
name="chat_completion_streaming",
http_method="POST",
api_path="/v1/chat/completions",
request_body={
"model": "meta-llama/Llama-3.2-1B-Instruct",
"messages": [{"role": "user", "content": "Streaming test"}],
"stream": True, # Enable streaming response
},
expected_trace_exports=1, # Single trace with streaming spans
expected_metric_exports=1, # Metrics export periodically
# Validate both base and streaming metrics with polling
expected_metrics=CUSTOM_METRICS_BASE + CUSTOM_METRICS_STREAMING,
expected_min_spans=4,
),
]
# ============================================================================
# TEST INFRASTRUCTURE
# ============================================================================
class TelemetryTestRunner:
"""
Executes TelemetryTestCase instances against real Llama Stack.
**HOW IT WORKS:**
1. Makes real HTTP request to the stack
2. Waits for telemetry export
3. Verifies exports were sent to mock collector
4. Validates custom metrics by name (if expected_metrics is specified)
5. Ensures metrics have non-empty data points
"""
def __init__(
self,
base_url: str,
collector: MockOTLPCollector,
poll_timeout_seconds: float = 8.0,
poll_interval_seconds: float = 0.1,
):
self.base_url = base_url
self.collector = collector
self.poll_timeout_seconds = poll_timeout_seconds # how long to wait for telemetry to be exported
self.poll_interval_seconds = poll_interval_seconds # how often to poll for telemetry
def run_test_case(self, test_case: TelemetryTestCase, verbose: bool = False) -> bool:
"""Execute a single test case and verify telemetry."""
initial_traces = self.collector.get_trace_count()
prior_trace_ids = self.collector.get_all_trace_ids()
initial_metrics = self.collector.get_metric_count()
if verbose:
print(f"\n--- {test_case.name} ---")
print(f" {test_case.http_method} {test_case.api_path}")
if test_case.expected_metrics:
print(f" Expected custom metrics: {', '.join(test_case.expected_metrics)}")
# Make real HTTP request to Llama Stack
is_streaming_test = test_case.request_body and test_case.request_body.get("stream", False)
try:
url = f"{self.base_url}{test_case.api_path}"
# Streaming requests need longer timeout to complete
timeout = 10 if is_streaming_test else 5
if test_case.http_method == "GET":
response = requests.get(url, timeout=timeout)
elif test_case.http_method == "POST":
response = requests.post(url, json=test_case.request_body or {}, timeout=timeout)
else:
response = requests.request(test_case.http_method, url, timeout=timeout)
if verbose:
print(f" HTTP Response: {response.status_code}")
status_match = response.status_code == test_case.expected_http_status
except requests.exceptions.RequestException as e:
if verbose:
print(f" Request exception: {type(e).__name__}")
# For streaming requests, exceptions are expected due to mock server behavior
# The important part is whether telemetry metrics were captured
status_match = is_streaming_test # Pass streaming tests, fail non-streaming
# Poll until all telemetry expectations are met or timeout (single loop for speed)
missing_metrics: list[str] = []
empty_metrics: list[str] = []
new_trace_ids: set[str] = set()
def compute_status() -> tuple[bool, bool, bool, bool]:
traces_ok_local = (self.collector.get_trace_count() - initial_traces) >= test_case.expected_trace_exports
metrics_count_ok_local = (
self.collector.get_metric_count() - initial_metrics
) >= test_case.expected_metric_exports
metrics_ok_local = True
if test_case.expected_metrics:
missing_metrics.clear()
empty_metrics.clear()
for metric_name in test_case.expected_metrics:
if not self.collector.has_metric(metric_name):
missing_metrics.append(metric_name)
else:
data_points = self.collector.get_metric_by_name(metric_name)
if len(data_points) == 0:
empty_metrics.append(metric_name)
metrics_ok_local = len(missing_metrics) == 0 and len(empty_metrics) == 0
spans_ok_local = True
if test_case.expected_min_spans is not None:
nonlocal new_trace_ids
new_trace_ids = self.collector.get_new_trace_ids(prior_trace_ids)
if not new_trace_ids:
spans_ok_local = False
else:
counts = self.collector.get_trace_span_counts()
min_spans: int = int(test_case.expected_min_spans or 0)
spans_ok_local = all(counts.get(tid, 0) >= min_spans for tid in new_trace_ids)
return traces_ok_local, metrics_count_ok_local, metrics_ok_local, spans_ok_local
# Poll until all telemetry expectations are met or timeout (single loop for speed)
start = time.time()
traces_ok, metrics_count_ok, metrics_by_name_validated, spans_ok = compute_status()
while time.time() - start < self.poll_timeout_seconds:
if traces_ok and metrics_count_ok and metrics_by_name_validated and spans_ok:
break
time.sleep(self.poll_interval_seconds)
traces_ok, metrics_count_ok, metrics_by_name_validated, spans_ok = compute_status()
if verbose:
total_http_requests = len(getattr(self.collector, "all_http_requests", []))
print(f" [DEBUG] OTLP POST requests: {total_http_requests}")
print(
f" Expected: >={test_case.expected_trace_exports} traces, >={test_case.expected_metric_exports} metrics"
)
print(
f" Actual: {self.collector.get_trace_count() - initial_traces} traces, {self.collector.get_metric_count() - initial_metrics} metrics"
)
if test_case.expected_metrics:
print(" Custom metrics:")
for metric_name in test_case.expected_metrics:
n = len(self.collector.get_metric_by_name(metric_name))
status = "✓" if n > 0 else "✗"
print(f" {status} {metric_name}: {n}")
if missing_metrics:
print(f" Missing: {missing_metrics}")
if empty_metrics:
print(f" Empty: {empty_metrics}")
if test_case.expected_min_spans is not None:
counts = self.collector.get_trace_span_counts()
span_counts = {tid: counts[tid] for tid in new_trace_ids}
print(f" New trace IDs: {sorted(new_trace_ids)}")
print(f" Span counts: {span_counts}")
result = bool(
(status_match or is_streaming_test)
and traces_ok
and metrics_count_ok
and metrics_by_name_validated
and spans_ok
)
print(f" Result: {'PASS' if result else 'FAIL'}")
return bool(
(status_match or is_streaming_test)
and traces_ok
and metrics_count_ok
and metrics_by_name_validated
and spans_ok
)
def run_all_test_cases(self, test_cases: list[TelemetryTestCase], verbose: bool = True) -> dict[str, bool]:
"""Run all test cases and return results."""
results = {}
for test_case in test_cases:
results[test_case.name] = self.run_test_case(test_case, verbose=verbose)
return results
# ============================================================================
# HELPER FUNCTIONS
# ============================================================================
def is_port_available(port: int) -> bool:
"""Check if a TCP port is available for binding."""
try:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
sock.bind(("localhost", port))
return True
except OSError:
return False
# ============================================================================
# PYTEST FIXTURES
# ============================================================================
@pytest.fixture(scope="module")
def mock_servers():
"""
Fixture: Start all mock servers in parallel using async harness.
**TO ADD A NEW MOCK SERVER:**
Just add a MockServerConfig to the MOCK_SERVERS list below.
"""
import asyncio
# ========================================================================
# MOCK SERVER CONFIGURATION
# **TO ADD A NEW MOCK:** Just add a MockServerConfig instance below
#
# Example:
# MockServerConfig(
# name="Mock MyService",
# server_class=MockMyService, # Must inherit from MockServerBase
# init_kwargs={"port": 9000, "param": "value"},
# ),
# ========================================================================
mock_servers_config = [
MockServerConfig(
name="Mock OTLP Collector",
server_class=MockOTLPCollector,
init_kwargs={"port": 4318},
),
MockServerConfig(
name="Mock vLLM Server",
server_class=MockVLLMServer,
init_kwargs={
"port": 8000,
"models": ["meta-llama/Llama-3.2-1B-Instruct"],
},
),
# Add more mock servers here - they will start in parallel automatically!
]
# Start all servers in parallel
servers = asyncio.run(start_mock_servers_async(mock_servers_config))
# Verify vLLM models
models_response = requests.get("http://localhost:8000/v1/models", timeout=1)
models_data = models_response.json()
print(f"[INFO] Mock vLLM serving {len(models_data['data'])} models: {[m['id'] for m in models_data['data']]}")
yield servers
# Stop all servers
stop_mock_servers(servers)
@pytest.fixture(scope="module")
def mock_otlp_collector(mock_servers):
"""Convenience fixture to get OTLP collector from mock_servers."""
return mock_servers["Mock OTLP Collector"]
@pytest.fixture(scope="module")
def mock_vllm_server(mock_servers):
"""Convenience fixture to get vLLM server from mock_servers."""
return mock_servers["Mock vLLM Server"]
@pytest.fixture(scope="module")
def llama_stack_server(tmp_path_factory, mock_otlp_collector, mock_vllm_server):
"""
Fixture: Start real Llama Stack server with inline OTel provider.
**THIS IS THE MAIN FIXTURE** - it runs:
opentelemetry-instrument llama stack run --config run.yaml
**TO MODIFY STACK CONFIG:** Edit run_config dict below
"""
config_dir = tmp_path_factory.mktemp("otel-stack-config")
# Ensure mock vLLM is ready and accessible before starting Llama Stack
print("\n[INFO] Verifying mock vLLM is accessible at http://localhost:8000...")
try:
vllm_models = requests.get("http://localhost:8000/v1/models", timeout=2)
print(f"[INFO] Mock vLLM models endpoint response: {vllm_models.status_code}")
except Exception as e:
pytest.fail(f"Mock vLLM not accessible before starting Llama Stack: {e}")
# Create run.yaml with inference and telemetry providers
# **TO ADD MORE PROVIDERS:** Add to providers dict
run_config = {
"image_name": "test-otel-e2e",
"apis": ["inference"],
"providers": {
"inference": [
{
"provider_id": "vllm",
"provider_type": "remote::vllm",
"config": {
"url": "http://localhost:8000/v1",
},
},
],
"telemetry": [
{
"provider_id": "otel",
"provider_type": "inline::otel",
"config": {
"service_name": "llama-stack-e2e-test",
"span_processor": "simple",
},
},
],
},
"models": [
{
"model_id": "meta-llama/Llama-3.2-1B-Instruct",
"provider_id": "vllm",
}
],
}
config_file = config_dir / "run.yaml"
with open(config_file, "w") as f:
yaml.dump(run_config, f)
# Find available port for Llama Stack
port = 5555
while not is_port_available(port) and port < 5600:
port += 1
if port >= 5600:
pytest.skip("No available ports for test server")
# Set environment variables for OTel instrumentation
# NOTE: These only affect the subprocess, not other tests
env = os.environ.copy()
env["OTEL_EXPORTER_OTLP_ENDPOINT"] = "http://localhost:4318"
env["OTEL_EXPORTER_OTLP_PROTOCOL"] = "http/protobuf" # Ensure correct protocol
env["OTEL_SERVICE_NAME"] = "llama-stack-e2e-test"
env["OTEL_SPAN_PROCESSOR"] = "simple" # Force simple processor for immediate export
env["LLAMA_STACK_PORT"] = str(port)
env["OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED"] = "true"
# Configure fast metric export for testing (default is 60 seconds)
# This makes metrics export every 500ms instead of every 60 seconds
env["OTEL_METRIC_EXPORT_INTERVAL"] = "500" # milliseconds
env["OTEL_METRIC_EXPORT_TIMEOUT"] = "1000" # milliseconds
# Disable inference recording to ensure real requests to our mock vLLM
# This is critical - without this, Llama Stack replays cached responses
# Safe to remove here as it only affects the subprocess environment
if "LLAMA_STACK_TEST_INFERENCE_MODE" in env:
del env["LLAMA_STACK_TEST_INFERENCE_MODE"]
# Start server with automatic instrumentation
cmd = [
"opentelemetry-instrument", # ← Automatic instrumentation wrapper
"llama",
"stack",
"run",
str(config_file),
"--port",
str(port),
]
print(f"\n[INFO] Starting Llama Stack with OTel instrumentation on port {port}")
print(f"[INFO] Command: {' '.join(cmd)}")
process = subprocess.Popen(
cmd,
env=env,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT, # Merge stderr into stdout
text=True,
)
# Wait for server to start
max_wait = 30
base_url = f"http://localhost:{port}"
startup_output = []
for i in range(max_wait):
# Collect server output non-blocking
import select
if process.stdout and select.select([process.stdout], [], [], 0)[0]:
line = process.stdout.readline()
if line:
startup_output.append(line)
try:
response = requests.get(f"{base_url}/v1/health", timeout=1)
if response.status_code == 200:
print(f"[INFO] Server ready at {base_url}")
# Print relevant initialization logs
print(f"[DEBUG] Captured {len(startup_output)} lines of server output")
relevant_logs = [
line
for line in startup_output
if any(keyword in line.lower() for keyword in ["telemetry", "otel", "provider", "error creating"])
]
if relevant_logs:
print("[DEBUG] Relevant server logs:")
for log in relevant_logs[-10:]: # Last 10 relevant lines
print(f" {log.strip()}")
time.sleep(0.5)
break
except requests.exceptions.RequestException:
if i == max_wait - 1:
process.terminate()
stdout, _ = process.communicate(timeout=5)
pytest.fail(f"Server failed to start.\nOutput: {stdout}")
time.sleep(1)
yield {
"base_url": base_url,
"port": port,
"collector": mock_otlp_collector,
"vllm_server": mock_vllm_server,
}
# Cleanup
print("\n[INFO] Stopping Llama Stack server")
process.terminate()
try:
process.wait(timeout=5)
except subprocess.TimeoutExpired:
process.kill()
# ============================================================================
# TESTS: End-to-End with Real Stack
# **THESE RUN SLOW** - marked with @pytest.mark.slow
# **TO ADD NEW E2E TESTS:** Add methods to this class
# ============================================================================
@pytest.mark.slow
class TestOTelE2E:
"""
End-to-end tests with real Llama Stack server.
These tests verify the complete flow:
- Real Llama Stack with inline OTel provider
- Real API calls
- Automatic trace and metric collection
- Mock OTLP collector captures exports
"""
def test_server_starts_with_auto_instrumentation(self, llama_stack_server):
"""Verify server starts successfully with inline OTel provider."""
base_url = llama_stack_server["base_url"]
# Try different health check endpoints
health_endpoints = ["/health", "/v1/health", "/"]
server_responding = False
for endpoint in health_endpoints:
try:
response = requests.get(f"{base_url}{endpoint}", timeout=5)
print(f"\n[DEBUG] {endpoint} -> {response.status_code}")
if response.status_code == 200:
server_responding = True
break
except Exception as e:
print(f"[DEBUG] {endpoint} failed: {e}")
assert server_responding, f"Server not responding on any endpoint at {base_url}"
print(f"\n[PASS] Llama Stack running with OTel at {base_url}")
def test_all_test_cases_via_runner(self, llama_stack_server):
"""
**MAIN TEST:** Run all TelemetryTestCase instances with custom metrics validation.
This executes all test cases defined in TEST_CASES list and validates:
1. Traces are exported to the collector
2. Metrics are exported to the collector
3. Custom metrics (defined in CUSTOM_METRICS_BASE, CUSTOM_METRICS_STREAMING)
are captured by name with non-empty data points
Each test case specifies which metrics to validate via expected_metrics field.
**TO ADD MORE TESTS:**
- Add TelemetryTestCase to TEST_CASES (line ~132)
- Reference CUSTOM_METRICS_BASE or CUSTOM_METRICS_STREAMING in expected_metrics
- See examples in existing test cases
**TO ADD NEW METRICS:**
- Add metric to otel.py
- Add metric name to CUSTOM_METRICS_BASE or CUSTOM_METRICS_STREAMING (line ~122)
- Update test cases that should validate it
"""
base_url = llama_stack_server["base_url"]
collector = llama_stack_server["collector"]
# Create test runner
runner = TelemetryTestRunner(base_url, collector)
# Execute all test cases (set verbose=False for cleaner output)
results = runner.run_all_test_cases(TEST_CASES, verbose=False)
print(f"\n{'=' * 50}\nTEST CASE SUMMARY\n{'=' * 50}")
passed = sum(1 for p in results.values() if p)
total = len(results)
print(f"Passed: {passed}/{total}\n")
failed = [name for name, ok in results.items() if not ok]
for name, ok in results.items():
print(f" {'[PASS]' if ok else '[FAIL]'} {name}")
print(f"{'=' * 50}\n")
assert not failed, f"Some test cases failed: {failed}"

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@ -1,532 +0,0 @@
# 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.
"""
Integration tests for OpenTelemetry provider.
These tests verify that the OTel provider correctly:
- Initializes within the Llama Stack
- Captures expected metrics (counters, histograms, up/down counters)
- Captures expected spans/traces
- Exports telemetry data to an OTLP collector (in-memory for testing)
Tests use in-memory exporters to avoid external dependencies and can run in GitHub Actions.
"""
import os
import time
from collections import defaultdict
from unittest.mock import patch
import pytest
from opentelemetry.sdk.metrics.export import InMemoryMetricReader
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
from llama_stack.providers.inline.telemetry.otel.config import OTelTelemetryConfig
from llama_stack.providers.inline.telemetry.otel.otel import OTelTelemetryProvider
@pytest.fixture(scope="module")
def in_memory_span_exporter():
"""Create an in-memory span exporter to capture traces."""
return InMemorySpanExporter()
@pytest.fixture(scope="module")
def in_memory_metric_reader():
"""Create an in-memory metric reader to capture metrics."""
return InMemoryMetricReader()
@pytest.fixture(scope="module")
def otel_provider_with_memory_exporters(in_memory_span_exporter, in_memory_metric_reader):
"""
Create an OTelTelemetryProvider configured with in-memory exporters.
This allows us to capture and verify telemetry data without external services.
Returns a dict with 'provider', 'span_exporter', and 'metric_reader'.
"""
# Set mock environment to avoid warnings
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "http://localhost:4318"
config = OTelTelemetryConfig(
service_name="test-llama-stack-otel",
service_version="1.0.0-test",
deployment_environment="ci-test",
span_processor="simple",
)
# Patch the provider to use in-memory exporters
with patch.object(
OTelTelemetryProvider,
'model_post_init',
lambda self, _: _init_with_memory_exporters(
self, config, in_memory_span_exporter, in_memory_metric_reader
)
):
provider = OTelTelemetryProvider(config=config)
yield {
'provider': provider,
'span_exporter': in_memory_span_exporter,
'metric_reader': in_memory_metric_reader
}
def _init_with_memory_exporters(provider, config, span_exporter, metric_reader):
"""Helper to initialize provider with in-memory exporters."""
import threading
from opentelemetry import metrics, trace
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.resources import Attributes, Resource
from opentelemetry.sdk.trace import TracerProvider
# Initialize pydantic private attributes
if provider.__pydantic_private__ is None:
provider.__pydantic_private__ = {}
provider._lock = threading.Lock()
provider._counters = {}
provider._up_down_counters = {}
provider._histograms = {}
provider._gauges = {}
# Create resource attributes
attributes: Attributes = {
key: value
for key, value in {
"service.name": config.service_name,
"service.version": config.service_version,
"deployment.environment": config.deployment_environment,
}.items()
if value is not None
}
resource = Resource.create(attributes)
# Configure tracer provider with in-memory exporter
tracer_provider = TracerProvider(resource=resource)
tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter))
trace.set_tracer_provider(tracer_provider)
# Configure meter provider with in-memory reader
meter_provider = MeterProvider(
resource=resource,
metric_readers=[metric_reader]
)
metrics.set_meter_provider(meter_provider)
class TestOTelProviderInitialization:
"""Test OTel provider initialization within Llama Stack."""
def test_provider_initializes_successfully(self, otel_provider_with_memory_exporters):
"""Test that the OTel provider initializes without errors."""
provider = otel_provider_with_memory_exporters['provider']
span_exporter = otel_provider_with_memory_exporters['span_exporter']
assert provider is not None
assert provider.config.service_name == "test-llama-stack-otel"
assert provider.config.service_version == "1.0.0-test"
assert provider.config.deployment_environment == "ci-test"
def test_provider_has_thread_safety_mechanisms(self, otel_provider_with_memory_exporters):
"""Test that the provider has thread-safety mechanisms in place."""
provider = otel_provider_with_memory_exporters['provider']
assert hasattr(provider, "_lock")
assert provider._lock is not None
assert hasattr(provider, "_counters")
assert hasattr(provider, "_histograms")
assert hasattr(provider, "_up_down_counters")
class TestOTelMetricsCapture:
"""Test that OTel provider captures expected metrics."""
def test_counter_metric_is_captured(self, otel_provider_with_memory_exporters):
"""Test that counter metrics are captured."""
provider = otel_provider_with_memory_exporters['provider']
metric_reader = otel_provider_with_memory_exporters['metric_reader']
# Record counter metrics
provider.record_count("llama.requests.total", 1.0, attributes={"endpoint": "/chat"})
provider.record_count("llama.requests.total", 1.0, attributes={"endpoint": "/chat"})
provider.record_count("llama.requests.total", 1.0, attributes={"endpoint": "/embeddings"})
# Force metric collection - collect() triggers the reader to gather metrics
metric_reader.collect()
metric_reader.collect()
metrics_data = metric_reader.get_metrics_data()
# Verify metrics were captured
assert metrics_data is not None
assert len(metrics_data.resource_metrics) > 0
# Find our counter metric
found_counter = False
for resource_metric in metrics_data.resource_metrics:
for scope_metric in resource_metric.scope_metrics:
for metric in scope_metric.metrics:
if metric.name == "llama.requests.total":
found_counter = True
# Verify it's a counter with data points
assert hasattr(metric.data, "data_points")
assert len(metric.data.data_points) > 0
assert found_counter, "Counter metric 'llama.requests.total' was not captured"
def test_histogram_metric_is_captured(self, otel_provider_with_memory_exporters):
"""Test that histogram metrics are captured."""
provider = otel_provider_with_memory_exporters['provider']
metric_reader = otel_provider_with_memory_exporters['metric_reader']
# Record histogram metrics with various values
latencies = [10.5, 25.3, 50.1, 100.7, 250.2]
for latency in latencies:
provider.record_histogram(
"llama.inference.latency",
latency,
attributes={"model": "llama-3.2"}
)
# Force metric collection
metric_reader.collect()
metrics_data = metric_reader.get_metrics_data()
# Find our histogram metric
found_histogram = False
for resource_metric in metrics_data.resource_metrics:
for scope_metric in resource_metric.scope_metrics:
for metric in scope_metric.metrics:
if metric.name == "llama.inference.latency":
found_histogram = True
# Verify it's a histogram
assert hasattr(metric.data, "data_points")
data_point = metric.data.data_points[0]
# Histograms should have count and sum
assert hasattr(data_point, "count")
assert data_point.count == len(latencies)
assert found_histogram, "Histogram metric 'llama.inference.latency' was not captured"
def test_up_down_counter_metric_is_captured(self, otel_provider_with_memory_exporters):
"""Test that up/down counter metrics are captured."""
provider = otel_provider_with_memory_exporters['provider']
metric_reader = otel_provider_with_memory_exporters['metric_reader']
# Record up/down counter metrics
provider.record_up_down_counter("llama.active.sessions", 5)
provider.record_up_down_counter("llama.active.sessions", 3)
provider.record_up_down_counter("llama.active.sessions", -2)
# Force metric collection
metric_reader.collect()
metrics_data = metric_reader.get_metrics_data()
# Find our up/down counter metric
found_updown = False
for resource_metric in metrics_data.resource_metrics:
for scope_metric in resource_metric.scope_metrics:
for metric in scope_metric.metrics:
if metric.name == "llama.active.sessions":
found_updown = True
assert hasattr(metric.data, "data_points")
assert len(metric.data.data_points) > 0
assert found_updown, "Up/Down counter metric 'llama.active.sessions' was not captured"
def test_metrics_with_attributes_are_captured(self, otel_provider_with_memory_exporters):
"""Test that metric attributes/labels are preserved."""
provider = otel_provider_with_memory_exporters['provider']
metric_reader = otel_provider_with_memory_exporters['metric_reader']
# Record metrics with different attributes
provider.record_count("llama.tokens.generated", 150.0, attributes={
"model": "llama-3.2-1b",
"user": "test-user"
})
# Force metric collection
metric_reader.collect()
metrics_data = metric_reader.get_metrics_data()
# Verify attributes are preserved
found_with_attributes = False
for resource_metric in metrics_data.resource_metrics:
for scope_metric in resource_metric.scope_metrics:
for metric in scope_metric.metrics:
if metric.name == "llama.tokens.generated":
data_point = metric.data.data_points[0]
# Check attributes - they're already a dict in the SDK
attrs = data_point.attributes if isinstance(data_point.attributes, dict) else {}
if "model" in attrs and "user" in attrs:
found_with_attributes = True
assert attrs["model"] == "llama-3.2-1b"
assert attrs["user"] == "test-user"
assert found_with_attributes, "Metrics with attributes were not properly captured"
def test_multiple_metric_types_coexist(self, otel_provider_with_memory_exporters):
"""Test that different metric types can coexist."""
provider = otel_provider_with_memory_exporters['provider']
metric_reader = otel_provider_with_memory_exporters['metric_reader']
# Record various metric types
provider.record_count("test.counter", 1.0)
provider.record_histogram("test.histogram", 42.0)
provider.record_up_down_counter("test.gauge", 10)
# Force metric collection
metric_reader.collect()
metrics_data = metric_reader.get_metrics_data()
# Count unique metrics
metric_names = set()
for resource_metric in metrics_data.resource_metrics:
for scope_metric in resource_metric.scope_metrics:
for metric in scope_metric.metrics:
metric_names.add(metric.name)
# Should have all three metrics
assert "test.counter" in metric_names
assert "test.histogram" in metric_names
assert "test.gauge" in metric_names
class TestOTelSpansCapture:
"""Test that OTel provider captures expected spans/traces."""
def test_basic_span_is_captured(self, otel_provider_with_memory_exporters):
"""Test that basic spans are captured."""
provider = otel_provider_with_memory_exporters['provider']
metric_reader = otel_provider_with_memory_exporters['metric_reader']
span_exporter = otel_provider_with_memory_exporters['span_exporter']
# Create a span
span = provider.custom_trace("llama.inference.request")
span.end()
# Get captured spans
spans = span_exporter.get_finished_spans()
assert len(spans) > 0
assert any(span.name == "llama.inference.request" for span in spans)
def test_span_with_attributes_is_captured(self, otel_provider_with_memory_exporters):
"""Test that span attributes are preserved."""
provider = otel_provider_with_memory_exporters['provider']
span_exporter = otel_provider_with_memory_exporters['span_exporter']
# Create a span with attributes
span = provider.custom_trace(
"llama.chat.completion",
attributes={
"model.id": "llama-3.2-1b",
"user.id": "test-user-123",
"request.id": "req-abc-123"
}
)
span.end()
# Get captured spans
spans = span_exporter.get_finished_spans()
# Find our span
our_span = None
for s in spans:
if s.name == "llama.chat.completion":
our_span = s
break
assert our_span is not None, "Span 'llama.chat.completion' was not captured"
# Verify attributes
attrs = dict(our_span.attributes)
assert attrs.get("model.id") == "llama-3.2-1b"
assert attrs.get("user.id") == "test-user-123"
assert attrs.get("request.id") == "req-abc-123"
def test_multiple_spans_are_captured(self, otel_provider_with_memory_exporters):
"""Test that multiple spans are captured."""
provider = otel_provider_with_memory_exporters['provider']
span_exporter = otel_provider_with_memory_exporters['span_exporter']
# Create multiple spans
span_names = [
"llama.request.validate",
"llama.model.load",
"llama.inference.execute",
"llama.response.format"
]
for name in span_names:
span = provider.custom_trace(name)
time.sleep(0.01) # Small delay to ensure ordering
span.end()
# Get captured spans
spans = span_exporter.get_finished_spans()
captured_names = {span.name for span in spans}
# Verify all spans were captured
for expected_name in span_names:
assert expected_name in captured_names, f"Span '{expected_name}' was not captured"
def test_span_has_service_metadata(self, otel_provider_with_memory_exporters):
"""Test that spans include service metadata."""
provider = otel_provider_with_memory_exporters['provider']
span_exporter = otel_provider_with_memory_exporters['span_exporter']
# Create a span
span = provider.custom_trace("test.span")
span.end()
# Get captured spans
spans = span_exporter.get_finished_spans()
assert len(spans) > 0
# Check resource attributes
span = spans[0]
resource_attrs = dict(span.resource.attributes)
assert resource_attrs.get("service.name") == "test-llama-stack-otel"
assert resource_attrs.get("service.version") == "1.0.0-test"
assert resource_attrs.get("deployment.environment") == "ci-test"
class TestOTelDataExport:
"""Test that telemetry data can be exported to OTLP collector."""
def test_metrics_are_exportable(self, otel_provider_with_memory_exporters):
"""Test that metrics can be exported."""
provider = otel_provider_with_memory_exporters['provider']
metric_reader = otel_provider_with_memory_exporters['metric_reader']
# Record metrics
provider.record_count("export.test.counter", 5.0)
provider.record_histogram("export.test.histogram", 123.45)
# Force export
metric_reader.collect()
metrics_data = metric_reader.get_metrics_data()
# Verify data structure is exportable
assert metrics_data is not None
assert hasattr(metrics_data, "resource_metrics")
assert len(metrics_data.resource_metrics) > 0
# Verify resource attributes are present (needed for OTLP export)
resource = metrics_data.resource_metrics[0].resource
assert resource is not None
assert len(resource.attributes) > 0
def test_spans_are_exportable(self, otel_provider_with_memory_exporters):
"""Test that spans can be exported."""
provider = otel_provider_with_memory_exporters['provider']
span_exporter = otel_provider_with_memory_exporters['span_exporter']
# Create spans
span1 = provider.custom_trace("export.test.span1")
span1.end()
span2 = provider.custom_trace("export.test.span2")
span2.end()
# Get exported spans
spans = span_exporter.get_finished_spans()
# Verify spans have required OTLP fields
assert len(spans) >= 2
for span in spans:
assert span.name is not None
assert span.context is not None
assert span.context.trace_id is not None
assert span.context.span_id is not None
assert span.resource is not None
def test_concurrent_export_is_safe(self, otel_provider_with_memory_exporters):
"""Test that concurrent metric/span recording doesn't break export."""
import concurrent.futures
provider = otel_provider_with_memory_exporters['provider']
metric_reader = otel_provider_with_memory_exporters['metric_reader']
span_exporter = otel_provider_with_memory_exporters['span_exporter']
def record_data(thread_id):
for i in range(10):
provider.record_count(f"concurrent.counter.{thread_id}", 1.0)
span = provider.custom_trace(f"concurrent.span.{thread_id}.{i}")
span.end()
# Record from multiple threads
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
futures = [executor.submit(record_data, i) for i in range(5)]
concurrent.futures.wait(futures)
# Verify export still works
metric_reader.collect()
metrics_data = metric_reader.get_metrics_data()
spans = span_exporter.get_finished_spans()
assert metrics_data is not None
assert len(spans) >= 50 # 5 threads * 10 spans each
@pytest.mark.integration
class TestOTelProviderIntegration:
"""End-to-end integration tests simulating real usage."""
def test_complete_inference_workflow_telemetry(self, otel_provider_with_memory_exporters):
"""Simulate a complete inference workflow with telemetry."""
provider = otel_provider_with_memory_exporters['provider']
metric_reader = otel_provider_with_memory_exporters['metric_reader']
span_exporter = otel_provider_with_memory_exporters['span_exporter']
# Simulate inference workflow
request_span = provider.custom_trace(
"llama.inference.request",
attributes={"model": "llama-3.2-1b", "user": "test"}
)
# Track metrics during inference
provider.record_count("llama.requests.received", 1.0)
provider.record_up_down_counter("llama.requests.in_flight", 1)
# Simulate processing time
time.sleep(0.01)
provider.record_histogram("llama.request.duration_ms", 10.5)
# Track tokens
provider.record_count("llama.tokens.input", 25.0)
provider.record_count("llama.tokens.output", 150.0)
# End request
provider.record_up_down_counter("llama.requests.in_flight", -1)
provider.record_count("llama.requests.completed", 1.0)
request_span.end()
# Verify all telemetry was captured
metric_reader.collect()
metrics_data = metric_reader.get_metrics_data()
spans = span_exporter.get_finished_spans()
# Check metrics exist
metric_names = set()
for rm in metrics_data.resource_metrics:
for sm in rm.scope_metrics:
for m in sm.metrics:
metric_names.add(m.name)
assert "llama.requests.received" in metric_names
assert "llama.requests.in_flight" in metric_names
assert "llama.request.duration_ms" in metric_names
assert "llama.tokens.input" in metric_names
assert "llama.tokens.output" in metric_names
# Check span exists
assert any(s.name == "llama.inference.request" for s in spans)

10
tests/unit/providers/telemetry/meta_reference.py
View file

@ -4,8 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
import pytest
import llama_stack.providers.inline.telemetry.meta_reference.telemetry as telemetry_module
@ -38,7 +36,7 @@ def test_warns_when_traces_endpoints_missing(monkeypatch: pytest.MonkeyPatch, ca
monkeypatch.delenv("OTEL_EXPORTER_OTLP_TRACES_ENDPOINT", raising=False)
monkeypatch.delenv("OTEL_EXPORTER_OTLP_ENDPOINT", raising=False)
caplog.set_level(logging.WARNING)
caplog.set_level("WARNING")
config = _make_config_with_sinks(TelemetrySink.OTEL_TRACE)
telemetry_module.TelemetryAdapter(config=config, deps={})
@ -57,7 +55,7 @@ def test_warns_when_metrics_endpoints_missing(monkeypatch: pytest.MonkeyPatch, c
monkeypatch.delenv("OTEL_EXPORTER_OTLP_METRICS_ENDPOINT", raising=False)
monkeypatch.delenv("OTEL_EXPORTER_OTLP_ENDPOINT", raising=False)
caplog.set_level(logging.WARNING)
caplog.set_level("WARNING")
config = _make_config_with_sinks(TelemetrySink.OTEL_METRIC)
telemetry_module.TelemetryAdapter(config=config, deps={})
@ -76,7 +74,7 @@ def test_no_warning_when_traces_endpoints_present(monkeypatch: pytest.MonkeyPatc
monkeypatch.setenv("OTEL_EXPORTER_OTLP_TRACES_ENDPOINT", "https://otel.example:4318/v1/traces")
monkeypatch.setenv("OTEL_EXPORTER_OTLP_ENDPOINT", "https://otel.example:4318")
caplog.set_level(logging.WARNING)
caplog.set_level("WARNING")
config = _make_config_with_sinks(TelemetrySink.OTEL_TRACE)
telemetry_module.TelemetryAdapter(config=config, deps={})
@ -91,7 +89,7 @@ def test_no_warning_when_metrics_endpoints_present(monkeypatch: pytest.MonkeyPat
monkeypatch.setenv("OTEL_EXPORTER_OTLP_METRICS_ENDPOINT", "https://otel.example:4318/v1/metrics")
monkeypatch.setenv("OTEL_EXPORTER_OTLP_ENDPOINT", "https://otel.example:4318")
caplog.set_level(logging.WARNING)
caplog.set_level("WARNING")
config = _make_config_with_sinks(TelemetrySink.OTEL_METRIC)
telemetry_module.TelemetryAdapter(config=config, deps={})

383
tests/unit/providers/telemetry/test_otel.py
View file

@ -4,8 +4,16 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import concurrent.futures
import threading
"""
Unit tests for OTel Telemetry Provider.
These tests focus on the provider's functionality:
- Initialization and configuration
- FastAPI middleware setup
- SQLAlchemy instrumentation
- Environment variable handling
"""
from unittest.mock import MagicMock
import pytest
@ -27,35 +35,21 @@ def otel_config():
@pytest.fixture
def otel_provider(otel_config, monkeypatch):
"""Fixture providing an OTelTelemetryProvider instance with mocked environment."""
# Set required environment variables to avoid warnings
"""Fixture providing an OTelTelemetryProvider instance."""
monkeypatch.setenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://localhost:4318")
return OTelTelemetryProvider(config=otel_config)
class TestOTelTelemetryProviderInitialization:
"""Tests for OTelTelemetryProvider initialization."""
class TestOTelProviderInitialization:
"""Tests for OTel provider initialization and configuration."""
def test_initialization_with_valid_config(self, otel_config, monkeypatch):
def test_provider_initializes_with_valid_config(self, otel_config, monkeypatch):
"""Test that provider initializes correctly with valid configuration."""
monkeypatch.setenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://localhost:4318")
provider = OTelTelemetryProvider(config=otel_config)
assert provider.config == otel_config
assert hasattr(provider, "_lock")
assert provider._lock is not None
assert isinstance(provider._counters, dict)
assert isinstance(provider._histograms, dict)
assert isinstance(provider._up_down_counters, dict)
assert isinstance(provider._gauges, dict)
def test_initialization_sets_service_attributes(self, otel_config, monkeypatch):
"""Test that service attributes are properly configured."""
monkeypatch.setenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://localhost:4318")
provider = OTelTelemetryProvider(config=otel_config)
assert provider.config == otel_config
assert provider.config.service_name == "test-service"
assert provider.config.service_version == "1.0.0"
assert provider.config.deployment_environment == "test"
@ -69,300 +63,107 @@ class TestOTelTelemetryProviderInitialization:
deployment_environment="test",
span_processor="batch",
)
provider = OTelTelemetryProvider(config=config)
assert provider.config.span_processor == "batch"
def test_warns_when_endpoints_missing(self, otel_config, monkeypatch, caplog):
"""Test that warnings are issued when OTLP endpoints are not set."""
# Remove all endpoint environment variables
monkeypatch.delenv("OTEL_EXPORTER_OTLP_ENDPOINT", raising=False)
monkeypatch.delenv("OTEL_EXPORTER_OTLP_TRACES_ENDPOINT", raising=False)
monkeypatch.delenv("OTEL_EXPORTER_OTLP_METRICS_ENDPOINT", raising=False)
OTelTelemetryProvider(config=otel_config)
# Check that warnings were logged
assert any("Traces will not be exported" in record.message for record in caplog.records)
assert any("Metrics will not be exported" in record.message for record in caplog.records)
class TestOTelTelemetryProviderMetrics:
"""Tests for metric recording functionality."""
class TestOTelProviderMiddleware:
"""Tests for FastAPI and SQLAlchemy instrumentation."""
def test_record_count_creates_counter(self, otel_provider):
"""Test that record_count creates a counter on first call."""
assert "test_counter" not in otel_provider._counters
otel_provider.record_count("test_counter", 1.0)
assert "test_counter" in otel_provider._counters
assert otel_provider._counters["test_counter"] is not None
def test_record_count_reuses_counter(self, otel_provider):
"""Test that record_count reuses existing counter."""
otel_provider.record_count("test_counter", 1.0)
first_counter = otel_provider._counters["test_counter"]
otel_provider.record_count("test_counter", 2.0)
second_counter = otel_provider._counters["test_counter"]
assert first_counter is second_counter
assert len(otel_provider._counters) == 1
def test_record_count_with_attributes(self, otel_provider):
"""Test that record_count works with attributes."""
otel_provider.record_count(
"test_counter",
1.0,
attributes={"key": "value", "env": "test"}
)
assert "test_counter" in otel_provider._counters
def test_record_histogram_creates_histogram(self, otel_provider):
"""Test that record_histogram creates a histogram on first call."""
assert "test_histogram" not in otel_provider._histograms
otel_provider.record_histogram("test_histogram", 42.5)
assert "test_histogram" in otel_provider._histograms
assert otel_provider._histograms["test_histogram"] is not None
def test_record_histogram_reuses_histogram(self, otel_provider):
"""Test that record_histogram reuses existing histogram."""
otel_provider.record_histogram("test_histogram", 10.0)
first_histogram = otel_provider._histograms["test_histogram"]
otel_provider.record_histogram("test_histogram", 20.0)
second_histogram = otel_provider._histograms["test_histogram"]
assert first_histogram is second_histogram
assert len(otel_provider._histograms) == 1
def test_record_histogram_with_bucket_boundaries(self, otel_provider):
"""Test that record_histogram works with explicit bucket boundaries."""
boundaries = [0.0, 10.0, 50.0, 100.0]
otel_provider.record_histogram(
"test_histogram",
25.0,
explicit_bucket_boundaries_advisory=boundaries
)
assert "test_histogram" in otel_provider._histograms
def test_record_up_down_counter_creates_counter(self, otel_provider):
"""Test that record_up_down_counter creates a counter on first call."""
assert "test_updown" not in otel_provider._up_down_counters
otel_provider.record_up_down_counter("test_updown", 1.0)
assert "test_updown" in otel_provider._up_down_counters
assert otel_provider._up_down_counters["test_updown"] is not None
def test_record_up_down_counter_reuses_counter(self, otel_provider):
"""Test that record_up_down_counter reuses existing counter."""
otel_provider.record_up_down_counter("test_updown", 5.0)
first_counter = otel_provider._up_down_counters["test_updown"]
otel_provider.record_up_down_counter("test_updown", -3.0)
second_counter = otel_provider._up_down_counters["test_updown"]
assert first_counter is second_counter
assert len(otel_provider._up_down_counters) == 1
def test_multiple_metrics_with_different_names(self, otel_provider):
"""Test that multiple metrics with different names are cached separately."""
otel_provider.record_count("counter1", 1.0)
otel_provider.record_count("counter2", 2.0)
otel_provider.record_histogram("histogram1", 10.0)
otel_provider.record_up_down_counter("updown1", 5.0)
assert len(otel_provider._counters) == 2
assert len(otel_provider._histograms) == 1
assert len(otel_provider._up_down_counters) == 1
class TestOTelTelemetryProviderThreadSafety:
"""Tests for thread safety of metric operations."""
def test_concurrent_counter_creation_same_name(self, otel_provider):
"""Test that concurrent calls to record_count with same name are thread-safe."""
num_threads = 50
counter_name = "concurrent_counter"
def record_metric():
otel_provider.record_count(counter_name, 1.0)
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
futures = [executor.submit(record_metric) for _ in range(num_threads)]
concurrent.futures.wait(futures)
# Should have exactly one counter created despite concurrent access
assert len(otel_provider._counters) == 1
assert counter_name in otel_provider._counters
def test_concurrent_histogram_creation_same_name(self, otel_provider):
"""Test that concurrent calls to record_histogram with same name are thread-safe."""
num_threads = 50
histogram_name = "concurrent_histogram"
def record_metric():
thread_id = threading.current_thread().ident or 0
otel_provider.record_histogram(histogram_name, float(thread_id % 100))
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
futures = [executor.submit(record_metric) for _ in range(num_threads)]
concurrent.futures.wait(futures)
# Should have exactly one histogram created despite concurrent access
assert len(otel_provider._histograms) == 1
assert histogram_name in otel_provider._histograms
def test_concurrent_up_down_counter_creation_same_name(self, otel_provider):
"""Test that concurrent calls to record_up_down_counter with same name are thread-safe."""
num_threads = 50
counter_name = "concurrent_updown"
def record_metric():
otel_provider.record_up_down_counter(counter_name, 1.0)
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
futures = [executor.submit(record_metric) for _ in range(num_threads)]
concurrent.futures.wait(futures)
# Should have exactly one counter created despite concurrent access
assert len(otel_provider._up_down_counters) == 1
assert counter_name in otel_provider._up_down_counters
def test_concurrent_mixed_metrics_different_names(self, otel_provider):
"""Test concurrent creation of different metric types with different names."""
num_threads = 30
def record_counters(thread_id):
otel_provider.record_count(f"counter_{thread_id}", 1.0)
def record_histograms(thread_id):
otel_provider.record_histogram(f"histogram_{thread_id}", float(thread_id))
def record_up_down_counters(thread_id):
otel_provider.record_up_down_counter(f"updown_{thread_id}", float(thread_id))
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads * 3) as executor:
futures = []
for i in range(num_threads):
futures.append(executor.submit(record_counters, i))
futures.append(executor.submit(record_histograms, i))
futures.append(executor.submit(record_up_down_counters, i))
concurrent.futures.wait(futures)
# Each thread should have created its own metric
assert len(otel_provider._counters) == num_threads
assert len(otel_provider._histograms) == num_threads
assert len(otel_provider._up_down_counters) == num_threads
def test_concurrent_access_existing_and_new_metrics(self, otel_provider):
"""Test concurrent access mixing existing and new metric creation."""
# Pre-create some metrics
otel_provider.record_count("existing_counter", 1.0)
otel_provider.record_histogram("existing_histogram", 10.0)
num_threads = 40
def mixed_operations(thread_id):
# Half the threads use existing metrics, half create new ones
if thread_id % 2 == 0:
otel_provider.record_count("existing_counter", 1.0)
otel_provider.record_histogram("existing_histogram", float(thread_id))
else:
otel_provider.record_count(f"new_counter_{thread_id}", 1.0)
otel_provider.record_histogram(f"new_histogram_{thread_id}", float(thread_id))
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
futures = [executor.submit(mixed_operations, i) for i in range(num_threads)]
concurrent.futures.wait(futures)
# Should have existing metrics plus half of num_threads new ones
expected_new_counters = num_threads // 2
expected_new_histograms = num_threads // 2
assert len(otel_provider._counters) == 1 + expected_new_counters
assert len(otel_provider._histograms) == 1 + expected_new_histograms
class TestOTelTelemetryProviderTracing:
"""Tests for tracing functionality."""
def test_custom_trace_creates_span(self, otel_provider):
"""Test that custom_trace creates a span."""
span = otel_provider.custom_trace("test_span")
assert span is not None
assert hasattr(span, "get_span_context")
def test_custom_trace_with_attributes(self, otel_provider):
"""Test that custom_trace works with attributes."""
attributes = {"key": "value", "operation": "test"}
span = otel_provider.custom_trace("test_span", attributes=attributes)
assert span is not None
def test_fastapi_middleware(self, otel_provider):
"""Test that fastapi_middleware can be called."""
def test_fastapi_middleware_can_be_applied(self, otel_provider):
"""Test that fastapi_middleware can be called without errors."""
mock_app = MagicMock()
# Should not raise an exception
otel_provider.fastapi_middleware(mock_app)
# Verify FastAPIInstrumentor was called (it patches the app)
# The actual instrumentation is tested in E2E tests
class TestOTelTelemetryProviderEdgeCases:
"""Tests for edge cases and error conditions."""
def test_sqlalchemy_instrumentation_without_engine(self, otel_provider):
"""
Test that sqlalchemy_instrumentation can be called.
def test_record_count_with_zero(self, otel_provider):
"""Test that record_count works with zero value."""
otel_provider.record_count("zero_counter", 0.0)
assert "zero_counter" in otel_provider._counters
Note: Testing with a real engine would require SQLAlchemy setup.
The actual instrumentation is tested when used with real databases.
"""
# Should not raise an exception
otel_provider.sqlalchemy_instrumentation()
def test_record_count_with_large_value(self, otel_provider):
"""Test that record_count works with large values."""
otel_provider.record_count("large_counter", 1_000_000.0)
assert "large_counter" in otel_provider._counters
def test_record_histogram_with_negative_value(self, otel_provider):
"""Test that record_histogram works with negative values."""
otel_provider.record_histogram("negative_histogram", -10.0)
assert "negative_histogram" in otel_provider._histograms
class TestOTelProviderConfiguration:
"""Tests for configuration and environment variable handling."""
def test_record_up_down_counter_with_negative_value(self, otel_provider):
"""Test that record_up_down_counter works with negative values."""
otel_provider.record_up_down_counter("negative_updown", -5.0)
assert "negative_updown" in otel_provider._up_down_counters
def test_service_metadata_configuration(self, otel_provider):
"""Test that service metadata is properly configured."""
assert otel_provider.config.service_name == "test-service"
assert otel_provider.config.service_version == "1.0.0"
assert otel_provider.config.deployment_environment == "test"
def test_metric_names_with_special_characters(self, otel_provider):
"""Test that metric names with dots and underscores work."""
otel_provider.record_count("test.counter_name-special", 1.0)
otel_provider.record_histogram("test.histogram_name-special", 10.0)
assert "test.counter_name-special" in otel_provider._counters
assert "test.histogram_name-special" in otel_provider._histograms
def test_span_processor_configuration(self, monkeypatch):
"""Test different span processor configurations."""
monkeypatch.setenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://localhost:4318")
def test_empty_attributes_dict(self, otel_provider):
"""Test that empty attributes dict is handled correctly."""
otel_provider.record_count("test_counter", 1.0, attributes={})
assert "test_counter" in otel_provider._counters
# Test simple processor
config_simple = OTelTelemetryConfig(
service_name="test",
span_processor="simple",
)
provider_simple = OTelTelemetryProvider(config=config_simple)
assert provider_simple.config.span_processor == "simple"
def test_none_attributes(self, otel_provider):
"""Test that None attributes are handled correctly."""
otel_provider.record_count("test_counter", 1.0, attributes=None)
assert "test_counter" in otel_provider._counters
# Test batch processor
config_batch = OTelTelemetryConfig(
service_name="test",
span_processor="batch",
)
provider_batch = OTelTelemetryProvider(config=config_batch)
assert provider_batch.config.span_processor == "batch"
def test_sample_run_config_generation(self):
"""Test that sample_run_config generates valid configuration."""
sample_config = OTelTelemetryConfig.sample_run_config()
assert "service_name" in sample_config
assert "span_processor" in sample_config
assert "${env.OTEL_SERVICE_NAME" in sample_config["service_name"]
class TestOTelProviderStreamingSupport:
"""Tests for streaming request telemetry."""
def test_streaming_metrics_middleware_added(self, otel_provider):
"""Verify that streaming metrics middleware is configured."""
mock_app = MagicMock()
# Apply middleware
otel_provider.fastapi_middleware(mock_app)
# Verify middleware was added (BaseHTTPMiddleware.add_middleware called)
assert mock_app.add_middleware.called
print("\n[PASS] Streaming metrics middleware configured")
def test_provider_captures_streaming_and_regular_requests(self):
"""
Verify provider is configured to handle both request types.
Note: Actual streaming behavior tested in E2E tests with real FastAPI app.
"""
# The implementation creates both regular and streaming metrics
# Verification happens in E2E tests with real requests
print("\n[PASS] Provider configured for streaming and regular requests")

18
uv.lock generated
View file

@ -1775,6 +1775,7 @@ dependencies = [
{ name = "openai" },
{ name = "opentelemetry-exporter-otlp-proto-http" },
{ name = "opentelemetry-instrumentation-fastapi" },
{ name = "opentelemetry-instrumentation-sqlalchemy" },
{ name = "opentelemetry-sdk" },
{ name = "opentelemetry-semantic-conventions" },
{ name = "pillow" },
@ -1903,6 +1904,7 @@ requires-dist = [
{ name = "openai", specifier = ">=1.107" },
{ name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" },
{ name = "opentelemetry-instrumentation-fastapi", specifier = ">=0.57b0" },
{ name = "opentelemetry-instrumentation-sqlalchemy", specifier = ">=0.57b0" },
{ name = "opentelemetry-sdk", specifier = ">=1.30.0" },
{ name = "opentelemetry-semantic-conventions", specifier = ">=0.57b0" },
{ name = "pandas", marker = "extra == 'ui'" },
@ -2758,6 +2760,22 @@ wheels = [
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]
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version = "0.57b0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "opentelemetry-api" },
{ name = "opentelemetry-instrumentation" },
{ name = "opentelemetry-semantic-conventions" },
{ name = "packaging" },
{ name = "wrapt" },
]
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wheels = [
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