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[Feat - Perf Improvement] DataDog Logger 91% lower latency (#5687)

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* fix refactor dd to be an instance of custom logger

* migrate dd logger to be async

* clean up dd logging

* add datadog sync and async code

* use batching for datadog logger

* add doc string for dd logging

* add clear doc string

* fix doc string

* allow debugging intake url

* clean up requirements.txt

* allow setting custom batch size on logger

* fix dd logging to use compression

* fix linting

* add dd load test

* fix dd load test

* fix dd url

* add test_datadog_logging_http_request

* fix test_datadog_logging_http_request
This commit is contained in:
Ishaan Jaff 2024-09-13 17:39:17 -07:00 • committed by GitHub
parent cd8d7ca915
commit 741c8e8a45
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GPG key ID: B5690EEEBB952194
11 changed files with 622 additions and 199 deletions
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41
litellm/litellm_core_utils/litellm_logging.py
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@ -69,7 +69,7 @@ from ..integrations.berrispend import BerriSpendLogger
from ..integrations.braintrust_logging import BraintrustLogger
from ..integrations.clickhouse import ClickhouseLogger
from ..integrations.custom_logger import CustomLogger
from ..integrations.datadog import DataDogLogger
from ..integrations.datadog.datadog import DataDogLogger
from ..integrations.dynamodb import DyanmoDBLogger
from ..integrations.galileo import GalileoObserve
from ..integrations.gcs_bucket import GCSBucketLogger
@ -962,33 +962,6 @@ class Logging:
service_name="langfuse",
trace_id=_trace_id,
)
if callback == "datadog":
global dataDogLogger
verbose_logger.debug("reaches datadog for success logging!")
kwargs = {}
for k, v in self.model_call_details.items():
if (
k != "original_response"
): # copy.deepcopy raises errors as this could be a coroutine
kwargs[k] = v
# this only logs streaming once, complete_streaming_response exists i.e when stream ends
if self.stream:
verbose_logger.debug(
f"datadog: is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}"
)
if complete_streaming_response is None:
continue
else:
print_verbose("reaches datadog for streaming logging!")
result = kwargs["complete_streaming_response"]
dataDogLogger.log_event(
kwargs=kwargs,
response_obj=result,
start_time=start_time,
end_time=end_time,
user_id=kwargs.get("user", None),
print_verbose=print_verbose,
)
if callback == "generic":
global genericAPILogger
verbose_logger.debug("reaches langfuse for success logging!")
@ -2125,6 +2098,14 @@ def _init_custom_logger_compatible_class(
_prometheus_logger = PrometheusLogger()
_in_memory_loggers.append(_prometheus_logger)
return _prometheus_logger # type: ignore
elif logging_integration == "datadog":
for callback in _in_memory_loggers:
if isinstance(callback, DataDogLogger):
return callback # type: ignore
_datadog_logger = DataDogLogger()
_in_memory_loggers.append(_datadog_logger)
return _datadog_logger # type: ignore
elif logging_integration == "gcs_bucket":
for callback in _in_memory_loggers:
if isinstance(callback, GCSBucketLogger):
@ -2251,6 +2232,10 @@ def get_custom_logger_compatible_class(
for callback in _in_memory_loggers:
if isinstance(callback, PrometheusLogger):
return callback
elif logging_integration == "datadog":
for callback in _in_memory_loggers:
if isinstance(callback, DataDogLogger):
return callback
elif logging_integration == "gcs_bucket":
for callback in _in_memory_loggers:
if isinstance(callback, GCSBucketLogger):