litellm/tests/logging_callback_tests/test_datadog.py
devin-ai-integration[bot] fa2c8984ba
test: move the unit half of 126 mixed legacy files into tests/unit (#45090)
* test: move the unit half of 126 mixed legacy files into tests/unit

* test: restore litellm globals that moved tests set

* test: finalize migration test cleanup

* test: restore original bodies of moved legacy tests

The move into tests/unit had rewritten 612 test bodies, and some of the rewrites dropped assertions. Each moved test now carries its original body from the legacy file, with only the imports, helpers, fake provider credentials and monkeypatched env it needs to run under tests/unit

test_timeout_streaming goes back to tests/local_testing because it needs the fake OpenAI endpoint server. The image payload fixture moves with its only user, and two tests that leaked global state (a registered model cost entry and queued logging tasks) are now isolated

* test: drop module imports shadowed by restored local imports

* test: assert on LiteLLM output in no-assertion moved tests and isolate leaks

Twenty no-assertion candidates get one assertion on the value LiteLLM returns, with the original lines unchanged. Four tests go back to their legacy files because they only check types or imports, write into the working directory, or cannot assert without a body change

Two moved tests leaked globals into later tests in the same worker, so monkeypatch fixtures now restore the retry-after header parser and the end user cost tracking flags

* test: drain queued logging tasks before the Phoenix span test

The moved Phoenix test counted spans from logging tasks that earlier tests had queued, so the drain fixture moves to tests/unit/conftest.py and both it and the Datadog batch test use it. test_factory_function goes back to its legacy file because its returned wrapper calls the real Assistants API and cannot be asserted on without a body change

---------

Co-authored-by: yuneng <yuneng@berri.ai>
2026-10-07 14:07:43 -07:00

226 lines
6.8 KiB
Python

import asyncio
import gzip
import io
import json
import logging
import os
from datetime import datetime as datetime_class
from unittest.mock import AsyncMock
import pytest
import litellm
from litellm._logging import verbose_logger
from litellm.integrations.datadog.datadog import *
from litellm.types.utils import (
StandardLoggingHiddenParams,
StandardLoggingMetadata,
StandardLoggingModelInformation,
StandardLoggingPayload,
)
verbose_logger.setLevel(logging.DEBUG)
def create_standard_logging_payload() -> StandardLoggingPayload:
return StandardLoggingPayload(
id="test_id",
call_type="completion",
response_cost=0.1,
response_cost_failure_debug_info=None,
status="success",
total_tokens=30,
prompt_tokens=20,
completion_tokens=10,
startTime=1234567890.0,
endTime=1234567891.0,
completionStartTime=1234567890.5,
model_map_information=StandardLoggingModelInformation(
model_map_key="gpt-4.1-mini", model_map_value=None
),
model="gpt-4.1-mini",
model_id="model-123",
model_group="openai-gpt",
api_base="https://api.openai.com",
metadata=StandardLoggingMetadata(
user_api_key_hash="test_hash",
user_api_key_org_id=None,
user_api_key_alias="test_alias",
user_api_key_team_id="test_team",
user_api_key_user_id="test_user",
user_api_key_team_alias="test_team_alias",
spend_logs_metadata=None,
requester_ip_address="127.0.0.1",
requester_metadata=None,
),
cache_hit=False,
cache_key=None,
saved_cache_cost=0.0,
request_tags=[],
end_user=None,
requester_ip_address="127.0.0.1",
messages=[{"role": "user", "content": "Hello, world!"}],
response={"choices": [{"message": {"content": "Hi there!"}}]},
error_str=None,
model_parameters={"stream": True},
hidden_params=StandardLoggingHiddenParams(
model_id="model-123",
cache_key=None,
api_base="https://api.openai.com",
response_cost="0.1",
additional_headers=None,
),
)
@pytest.mark.asyncio
async def test_create_datadog_logging_payload():
"""Test creating a DataDog logging payload from a standard logging object"""
dd_logger = DataDogLogger()
standard_payload = create_standard_logging_payload()
# Create mock kwargs with the standard logging object
kwargs = {"standard_logging_object": standard_payload}
# Test payload creation
dd_payload = dd_logger.create_datadog_logging_payload(
kwargs=kwargs,
response_obj=None,
start_time=datetime_class.now(),
end_time=datetime_class.now(),
)
# Verify payload structure
assert dd_payload["ddsource"] == os.getenv("DD_SOURCE", "litellm")
assert dd_payload["service"] == "litellm-server"
assert dd_payload["status"] == DataDogStatus.INFO
# verify the message field == standard_payload
dict_payload = json.loads(dd_payload["message"])
assert dict_payload == standard_payload
@pytest.mark.asyncio
async def test_datadog_failure_logging():
"""Test logging a failure event to DataDog"""
dd_logger = DataDogLogger()
standard_payload = create_standard_logging_payload()
standard_payload["status"] = "failure" # Set status to failure
standard_payload["error_str"] = "Test error"
kwargs = {"standard_logging_object": standard_payload}
dd_payload = dd_logger.create_datadog_logging_payload(
kwargs=kwargs,
response_obj=None,
start_time=datetime_class.now(),
end_time=datetime_class.now(),
)
assert (
dd_payload["status"] == DataDogStatus.ERROR
) # Verify failure maps to warning status
# verify the message field == standard_payload
dict_payload = json.loads(dd_payload["message"])
assert dict_payload == standard_payload
# verify error_str is in the message field
assert "error_str" in dict_payload
assert dict_payload["error_str"] == "Test error"
@pytest.mark.asyncio
async def test_datadog_log_redis_failures():
"""
Test that poorly configured Redis is logged as Warning on DataDog
"""
try:
from litellm.caching.caching import Cache
from litellm.integrations.datadog.datadog import DataDogLogger
litellm.cache = Cache(
type="redis", host="badhost", port="6379", password="badpassword"
)
os.environ["DD_SITE"] = "https://fake.datadoghq.com"
os.environ["DD_API_KEY"] = "anything"
dd_logger = DataDogLogger()
litellm.callbacks = [dd_logger]
litellm.service_callback = ["datadog"]
litellm.set_verbose = True
# Create a mock for the async_client's post method
mock_post = AsyncMock()
mock_post.return_value.status_code = 202
mock_post.return_value.text = "Accepted"
dd_logger.async_client.post = mock_post
# Make the completion call
for _ in range(3):
response = await litellm.acompletion(
model="gpt-4.1-mini",
messages=[{"role": "user", "content": "what llm are u"}],
max_tokens=10,
temperature=0.2,
mock_response="Accepted",
)
print(response)
# Wait for 5 seconds
await asyncio.sleep(6)
# Assert that the mock was called
assert mock_post.called, "HTTP request was not made"
# Get the arguments of the last call
args, kwargs = mock_post.call_args
print("CAll args and kwargs", args, kwargs)
# For example, checking if the URL is correct
assert kwargs["url"].endswith("/api/v2/logs"), "Incorrect DataDog endpoint"
body = kwargs["data"]
# use gzip to unzip the body
with gzip.open(io.BytesIO(body), "rb") as f:
body = f.read().decode("utf-8")
print(body)
# body is string parse it to dict
body = json.loads(body)
print(body)
failure_events = [log for log in body if log["status"] == "warning"]
assert len(failure_events) > 0, "No failure events logged"
print("ALL FAILURE/WARN EVENTS", failure_events)
for event in failure_events:
message = json.loads(event["message"])
assert (
event["status"] == "warning"
), f"Event status is not 'warning': {event['status']}"
assert (
message["service"] == "redis"
), f"Service is not 'redis': {message['service']}"
assert "error" in message, "No 'error' field in the message"
assert message["error"], "Error field is empty"
except Exception as e:
pytest.fail(f"Test failed with exception: {str(e)}")