Logging and monitoring work in tandem to help you understand and optimize application performance, as well as to diagnose errors and system-related issues. You should turn on summary logs for all API calls and detailed logs for failed API calls so that you can supply the API call logs when needing technical support.
Client library logging
The Google Ads API client libraries come with built-in logging. For platform-specific logging details, consult the logging documentation within your client library of choice.
| Language | Guide |
|---|---|
| Java | Logging docs for Java |
| .NET | Logging docs for .NET |
| PHP | Logging docs for PHP |
| Python | Logging docs for Python |
| Ruby | Logging docs for Ruby |
| Perl | Logging docs for Perl |
Log format
The Google Ads API client libraries generate a detailed log and a summary log for each API call. The detailed log contains all the details of the API call, whereas the summary log contains minimal details of the API call. An example of each type of log is shown, with the logs truncated and formatted for readability.
Summary log
GoogleAds.SummaryRequestLogs Warning: 1 : [2023-09-15 19:58:39Z] -
Request made: Host: , Method: /google.ads.googleads.v14.services.GoogleAdsService/SearchStream,
ClientCustomerID: 5951878031, RequestID: hELhBPNlEDd8mWYcZu7b8g,
IsFault: True, FaultMessage: Status(StatusCode="InvalidArgument",
Detail="Request contains an invalid argument.")
Detailed log
GoogleAds.DetailedRequestLogs Verbose: 1 : [2023-11-02 21:09:36Z] -
---------------BEGIN API CALL---------------
Request
-------
Method Name: /google.ads.googleads.v14.services.GoogleAdsService/SearchStream
Host:
Headers: {
"x-goog-api-client": "gl-dotnet/5.0.0 gapic/17.0.1 gax/4.2.0 grpc/2.46.3 gccl/3.0.1 pb/3.21.5",
"developer-token": "REDACTED",
"login-customer-id": "1234567890",
"x-goog-request-params": "customer_id=4567890123"
}
{ "customerId": "4567890123", "query": "SELECT ad_group_criterion.type FROM
ad_group_criterion WHERE ad_group.status IN(ENABLED, PAUSED) AND
campaign.status IN(ENABLED, PAUSED) ", "summaryRowSetting": "NO_SUMMARY_ROW" }
Response
--------
Headers: {
"date": "Thu, 02 Nov 2023 21:09:35 GMT",
"alt-svc": "h3-29=\":443\"; ma=2592000"
}
{
"results": [ {
"adGroupCriterion": {
"resourceName": "customers/4567890123/adGroupCriteria/456789456789~123456123467",
"type": "KEYWORD"
} }, {
"adGroupCriterion": {
"resourceName": "customers/4567890123/adGroupCriteria/456789456789~56789056788",
"type": "KEYWORD"
} } ],
"fieldMask": "adGroupCriterion.type", "requestId": "VsJ4F00ew6s9heHvAJ-abw"
}
----------------END API CALL----------------
What if I don't use a client library?
If you don't use a client library, implement your own logging to capture the
details of the outgoing and incoming API calls. You should log at least the
value of the request-id response header, which can then be shared with the
technical support teams as required.
Logging to the cloud
There are many tools you can use to capture logs and performance metrics for your application. For example, you can use Google Cloud Logging to log performance metrics to your Google Cloud Project. This makes it possible to set up dashboards and alerting in Google Cloud Monitoring to make use of the logged metrics.
Cloud Logging offers client libraries for all supported Google Ads API client library languages except for Perl, so in most cases it is possible to log with Cloud Logging directly from your client library integration. For other languages including Perl, Cloud Logging also offers a REST API.
There are a few options for logging to Cloud Logging, or another tool, from a Google Ads API client library. Each option comes with its own tradeoffs of time to implement, complexity, and performance. Think carefully about these tradeoffs before deciding which solution to implement.
Option 1: Write local logs to the cloud from a background process
Client library logs can be written to a local file on your machine by modifying your logging configuration. Once the logs are output to a local file, you can set up a daemon to collect the logs and send them to the cloud.
One limitation of this approach is that some performance metrics won't be captured by default. Client library logs include details from the request and response objects, so latency metrics won't be included unless additional changes are made to log these as well.
Option 2: Run your application on Compute Engine and install the Ops Agent
If your application is running on Compute Engine, you can send your logs to Google Cloud Logging by installing the Ops Agent. The Ops Agent can be configured to send your application logs to Cloud Logging, in addition to the metrics and logs that are sent by default.
If your application is already running in a Google Cloud environment, or if you are considering moving your application to Google Cloud, this is a great option to consider.
Option 3: Implement logging in your application code
Logging directly from the application code can be done in one of two ways:
Incorporating metrics calculations and log statements in every applicable location in your code. This option is more feasible for smaller codebases, where the scope and maintenance costs of such a change would be minimal.
Implementing a logging interface. If the application logic can be abstracted so that different pieces of the application inherit from the same base class, logging logic can be implemented in that base class. This option is generally preferred over incorporating log statements throughout the application code, as it is easier to maintain and scale. For larger codebases, the maintainability and scalability of this solution are all the more relevant.
One limitation of this approach is that the full request and response logs are not available from the application code. Full request and response objects can be accessed from gRPC interceptors; this is how the built-in client library logging obtains request and response logs. In the event of an error, additional information might be available in the exception object, but fewer details are available for successful responses within the application logic. For example, in most cases, the request ID for a successful request is not accessible from the Google Ads API response objects.
Option 4: Implement a custom gRPC logging interceptor
gRPC supports unary and streaming interceptors which can access the request and response objects as they pass between the client and the server. The Google Ads API client libraries use gRPC interceptors to offer built-in logging support. Similarly, you can implement a custom gRPC interceptor to access the request and response objects, extract information for logging and monitoring purposes, and write that data to the location of your choice.
Unlike some of the other solutions presented here, implementing a custom gRPC interceptor gives you flexibility to capture request and response objects on every request, and implement additional logic to capture details of the request. For example, you can calculate elapsed time of a request by implementing performance timing logic within the custom interceptor itself, then log the metric to Google Cloud Logging to make it available for latency monitoring within Google Cloud Monitoring.
Custom Google Cloud Logging interceptor in Python
To demonstrate this solution, we have written an example of a custom logging interceptor in Python. The custom interceptor is created and passed into the service client. It then accesses the request and response objects that pass through on every service method call, processes data from those objects, and sends the data to Google Cloud Logging.
In addition to the data that comes from the request and response objects, the example implements some additional logic to capture the elapsed time of the request, and some other metadata that would be useful for monitoring purposes, like whether or not the request was successful. For more information on how this information can be useful, both generally for monitoring, and specifically when combining Google Cloud Logging and Google Cloud Monitoring, see the Monitoring guide.
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A custom gRPC Interceptor that logs requests and responses to Cloud Logging.
The custom interceptor object is passed into the get_service method of the
GoogleAdsClient. It intercepts requests and responses, parses them into a
human readable structure and logs them using the logging service instantiated
within the class (in this case, a Cloud Logging client).
"""
import logging
import time
from google.cloud import logging
from grpc import UnaryUnaryClientInterceptor, UnaryStreamClientInterceptor
from google.ads.googleads.interceptors import LoggingInterceptor, mask_message
class CloudLoggingInterceptor(LoggingInterceptor):
"""An interceptor that logs rpc request and response details to Google Cloud Logging.
This class inherits logic from the LoggingInterceptor, which simplifies the
implementation here. Some logic is required here in order to make the
underlying logic work -- comments make note of this where applicable.
NOTE: Inheriting from the LoggingInterceptor class could yield unexpected side
effects. For example, if the LoggingInterceptor class is updated, this class would
inherit the updated logic, which could affect its functionality. One option to avoid
this is to inherit from the Interceptor class instead, and selectively copy whatever
logic is needed from the LoggingInterceptor class."""
def __init__(self, api_version):
"""Initializer for the CloudLoggingInterceptor.
Args:
api_version: a str of the API version of the request.
"""
super().__init__(logger=None, api_version=api_version)
# Instantiate the Cloud Logging client.
logging_client = logging.Client()
self.logger = logging_client.logger("cloud_logging")
def log_successful_request(
self,
method,
customer_id,
metadata_json,
request_id,
request,
trailing_metadata_json,
response,
):
"""Handles logging of a successful request.
Args:
method: The method of the request.
customer_id: The customer ID associated with the request.
metadata_json: A JSON str of initial_metadata.
request_id: A unique ID for the request provided in the response.
request: An instance of a request proto message.
trailing_metadata_json: A JSON str of trailing_metadata.
response: A grpc.Call/grpc.Future instance.
"""
# Retrieve and mask the RPC result from the response future.
# This method is available from the LoggingInterceptor class.
# Ensure self._cache is set in order for this to work.
# The response result could contain up to 10,000 rows of data,
# so consider truncating this value before logging it, to save
# on data storage costs and maintain readability.
result = self.retrieve_and_mask_result(response)
# elapsed_ms is the approximate elapsed time of the RPC, in milliseconds.
# There are different ways to define and measure elapsed time, so use
# whatever approach makes sense for your monitoring purposes.
# rpc_start and rpc_end are set in the intercept_unary_* methods below.
elapsed_ms = (self.rpc_end - self.rpc_start) * 1000
debug_log = {
"method": method,
"host": metadata_json,
"request_id": request_id,
"request": str(request),
"headers": trailing_metadata_json,
"response": str(result),
"is_fault": False,
"elapsed_ms": elapsed_ms,
}
self.logger.log_struct(debug_log, severity="DEBUG")
info_log = {
"customer_id": customer_id,
"method": method,
"request_id": request_id,
"is_fault": False,
# Available from the Interceptor class.
"api_version": self._api_version,
}
self.logger.log_struct(info_log, severity="INFO")
def log_failed_request(
self,
method,
customer_id,
metadata_json,
request_id,
request,
trailing_metadata_json,
response,
):
"""Handles logging of a failed request.
Args:
method: The method of the request.
customer_id: The customer ID associated with the request.
metadata_json: A JSON str of initial_metadata.
request_id: A unique ID for the request provided in the response.
request: An instance of a request proto message.
trailing_metadata_json: A JSON str of trailing_metadata.
response: A JSON str of the response message.
"""
exception = self._get_error_from_response(response)
exception_str = self._parse_exception_to_str(exception)
fault_message = self._get_fault_message(exception)
info_log = {
"method": method,
"endpoint": self.endpoint,
"host": metadata_json,
"request_id": request_id,
"request": str(request),
"headers": trailing_metadata_json,
"exception": exception_str,
"is_fault": True,
}
self.logger.log_struct(info_log, severity="INFO")
error_log = {
"method": method,
"endpoint": self.endpoint,
"request_id": request_id,
"customer_id": customer_id,
"is_fault": True,
"fault_message": fault_message,
}
self.logger.log_struct(error_log, severity="ERROR")
def intercept_unary_unary(self, continuation, client_call_details, request):
"""Intercepts and logs API interactions.
Overrides abstract method defined in grpc.UnaryUnaryClientInterceptor.
Args:
continuation: a function to continue the request process.
client_call_details: a grpc._interceptor._ClientCallDetails
instance containing request metadata.
request: a SearchGoogleAdsRequest or SearchGoogleAdsStreamRequest
message class instance.
Returns:
A grpc.Call/grpc.Future instance representing a service response.
"""
# Set the rpc_end value to current time when RPC completes.
def update_rpc_end(response_future):
self.rpc_end = time.perf_counter()
# Capture precise clock time to later calculate approximate elapsed
# time of the RPC.
self.rpc_start = time.perf_counter()
# The below call is REQUIRED.
response = continuation(client_call_details, request)
response.add_done_callback(update_rpc_end)
self.log_request(client_call_details, request, response)
# The below return is REQUIRED.
return response
def intercept_unary_stream(
self, continuation, client_call_details, request
):
"""Intercepts and logs API interactions for Unary-Stream requests.
Overrides abstract method defined in grpc.UnaryStreamClientInterceptor.
Args:
continuation: a function to continue the request process.
client_call_details: a grpc._interceptor._ClientCallDetails
instance containing request metadata.
request: a SearchGoogleAdsRequest or SearchGoogleAdsStreamRequest
message class instance.
Returns:
A grpc.Call/grpc.Future instance representing a service response.
"""
def on_rpc_complete(response_future):
self.rpc_end = time.perf_counter()
self.log_request(client_call_details, request, response_future)
# Capture precise clock time to later calculate approximate elapsed
# time of the RPC.
self.rpc_start = time.perf_counter()
# The below call is REQUIRED.
response = continuation(client_call_details, request)
# Set self._cache to the cache on the response wrapper in order to
# access the streaming logs. This is REQUIRED in order to log streaming
# requests.
self._cache = response.get_cache()
response.add_done_callback(on_rpc_complete)
# The below return is REQUIRED.
return response