Package: watchtower
Watchtower is a log handler forAmazon Web Services CloudWatch Logs.CloudWatch Logs is a log management service built into AWS. It is conceptually similar to services like Splunk, Datadog,
and Loggly, but is more lightweight, cheaper, and tightly integrated with the rest of AWS.Watchtower, in turn, is a lightweight adapter between thePython logging systemand CloudWatch Logs. It uses theboto3 AWS SDK, and lets you plug your application logging directly into CloudWatch without the need
to install a system-wide log collector likeawscli-cwlogsand round-trip
your logs through the instance’s syslog. It aggregates logs into batches to avoid sending an API request per each log
message, while guaranteeing a delivery deadline (60 seconds by default).Installationpip install watchtowerSynopsisInstallawscliand set your AWS credentials (runaws configure).importwatchtower,logginglogging.basicConfig(level=logging.INFO)logger=logging.getLogger(__name__)logger.addHandler(watchtower.CloudWatchLogHandler())logger.info("Hi")logger.info(dict(foo="bar",details={}))After running the example, you can see the log output in yourAWS consoleunder thewatchtowerlog group.IAM permissionsThe process running watchtower needs to have access to IAM credentials to call the CloudWatch Logs API. The standard
procedure for loading and configuring credentials is described in theBoto3 Credentials documentation.
When running Watchtower on an EC2 instance or other AWS compute resource, boto3 automatically loads credentials frominstance metadata(IMDS) or
container credentials provider (AWS_WEB_IDENTITY_TOKEN_FILE or AWS_CONTAINER_CREDENTIALS_FULL_URI). The easiest way to
grant the right permissions to the IAM role associated with these credentials is by attaching an AWSmanaged IAM policyto the
role. While AWS provides no generic managed CloudWatch Logs writer policy, we recommend that you use thearn:aws:iam::aws:policy/AWSOpsWorksCloudWatchLogsmanaged policy, which has just the right permissions without being
overly broad.Example: Flask logging with WatchtowerUse the following configuration to send Flask logs to a CloudWatch Logs stream called “loggable”:importwatchtower,flask,logginglogging.basicConfig(level=logging.INFO)app=flask.Flask("loggable")handler=watchtower.CloudWatchLogHandler(log_group_name=app.name)app.logger.addHandler(handler)logging.getLogger("werkzeug").addHandler(handler)@app.route('/')defhello_world():return'Hello World!'if__name__=='__main__':app.run()(See alsohttp://flask.pocoo.org/docs/errorhandling/.)Example: Django logging with WatchtowerThis is an example of Watchtower integration with Django. In your Django project, add the following tosettings.py:importboto3AWS_REGION_NAME="us-west-2"boto3_logs_client=boto3.client("logs",region_name=AWS_REGION_NAME)LOGGING={'version':1,'disable_existing_loggers':False,'root':{'level':'DEBUG',# Adding the watchtower handler here causes all loggers in the project that# have propagate=True (the default) to send messages to watchtower. If you# wish to send only from specific loggers instead, remove "watchtower" here# and configure individual loggers below.'handlers':['watchtower','console'],},'handlers':{'console':{'class':'logging.StreamHandler',},'watchtower':{'class':'watchtower.CloudWatchLogHandler','boto3_client':boto3_logs_client,'log_group_name':'YOUR_DJANGO_PROJECT_NAME',# Decrease the verbosity level here to send only those logs to watchtower,# but still see more verbose logs in the console. See the watchtower# documentation for other parameters that can be set here.'level':'DEBUG'}},'loggers':{# In the debug server (`manage.py runserver`), several Django system loggers cause# deadlocks when using threading in the logging handler, and are not supported by# watchtower. This limitation does not apply when running on production WSGI servers# (gunicorn, uwsgi, etc.), so we recommend that you set `propagate=True` below in your# production-specific Django settings file to receive Django system logs in CloudWatch.'django':{'level':'DEBUG','handlers':['console'],'propagate':False}# Add any other logger-specific configuration here.}}Using this configuration, logs from Django will be sent to Cloudwatch in the log groupYOUR_DJANGO_PROJECT_NAME.
To supply AWS credentials to this configuration in development, set yourAWS CLI profile settingswithaws configure. To supply credentials in production or when running on an EC2 instance,
assign an IAM role to your instance, which will cause boto3 to automatically ingest IAM role credentials frominstance metadata.(See also theDjango logging documentation.)Examples: Querying CloudWatch logsThis section is not specific to Watchtower. It demonstrates the use of awscli and jq to read and search CloudWatch logs
on the command line.For the Flask example above, you can retrieve your application logs with the following two commands:aws logs get-log-events --log-group-name watchtower --log-stream-name loggable | jq '.events[].message'
aws logs get-log-events --log-group-name watchtower --log-stream-name werkzeug | jq '.events[].message'In addition to the raw get-log-events API, CloudWatch Logs supportsextraction of your logs into an S3 bucket,log analysis with a query language,
and alerting and dashboards based onmetric filters, which are pattern
rules that extract information from your logs and feed it to alarms and dashboard graphs. If you want to make use of
these features on the command line, the author of Watchtower has published an open source CLI toolkit calledaegeathat includes the commandsaegea logsandaegea grepto easily
access the S3 Export and Insights features.Examples: Python Logging ConfigThe Pythonlogging.configmodule has the ability to provide a configuration file that can be loaded in order to
separate the logging configuration from the code.The following are two example YAML configuration files that can be loaded using PyYAML. The resultingdictobject
can then be loaded intologging.config.dictConfig. The first example is a basic example that relies on the default
configuration provided byboto3:# Default AWS Configversion:1disable_existing_loggers:Falseformatters:json:format:"[%(asctime)s]%(process)d%(levelname)s%(name)s:%(funcName)s:%(lineno)s-%(message)s"plaintext:format:"[%(asctime)s]%(process)d%(levelname)s%(name)s:%(funcName)s:%(lineno)s-%(message)s"handlers:console:class:logging.StreamHandlerformatter:plaintextlevel:DEBUGstream:ext://sys.stdoutlogfile:class:logging.handlers.RotatingFileHandlerformatter:plaintextlevel:DEBUGfilename:watchtower.logmaxBytes:1000000backupCount:3watchtower:class:watchtower.CloudWatchLogHandlerformatter:jsonlevel:DEBUGlog_group_name:watchtowerlog_stream_name:"{logger_name}-{strftime:%y-%m-%d}"send_interval:10create_log_group:Falseroot:level:DEBUGpropagate:Truehandlers:[console,logfile,watchtower]loggers:botocore:level:INFOurllib3:level:INFOThe above works well if you can use the default boto3 credential configuration, or rely on environment variables.
However, sometimes one may want to use different credentials for logging than used for other functionality;
in this case theboto3_profile_nameoption to Watchtower can be used to provide a boto3 profile name:# AWS Config Profileversion:1...handlers:...watchtower:boto3_profile_name:watchtowerlogger...Finally, the following shows how to load the configuration into the working application:importlogging.configimportflaskimportyamlapp=flask.Flask("loggable")@app.route('/')defhello_world():return'Hello World!'if__name__=='__main__':withopen('logging.yml')aslog_config:config_yml=log_config.read()config_dict=yaml.safe_load(config_yml)logging.config.dictConfig(config_dict)app.run()Log stream namingFor high volume logging applications that utilize process pools, it is recommended that you keep the default log stream
name ({machine_name}/{program_name}/{logger_name}/{process_id}) or otherwise make it unique per source using a
combination of these template variables. Because logs must be submitted sequentially to each log stream, independent
processes sending logs to the same log stream will encounter sequence token synchronization errors and spend extra resources
automatically recovering from them. As the number of processes increases, this overhead will grow until logs fail to
deliver and get dropped (causing a warning on stderr). Partitioning logs into streams by source avoids this contention.Boto3/botocore/urllib3 logsBecause watchtower uses boto3 to send logs, the act of sending them generates a number of DEBUG level log messages
from boto3’s dependencies, botocore and urllib3. To avoid generating a self-perpetuating stream of log messages,watchtower.CloudWatchLogHandlerattaches afilterto itself which drops all DEBUG
level messages from these libraries, and drops all messages at all levels from them when shutting down (specifically,
inwatchtower.CloudWatchLogHandler.flush()andwatchtower.CloudWatchLogHandler.close()). The filter does not
apply to any other handlers you may have processing your messages, so the following basic configuration will cause
botocore debug logs to print to stderr but not to Cloudwatch:importwatchtower,logginglogging.basicConfig(level=logging.DEBUG)logger=logging.getLogger()logger.addHandler(watchtower.CloudWatchLogHandler())AWS LambdaWatchtower is not suitable or necessary for applications running on AWS Lambda. All AWS Lambda logs (i.e. all lines
printed to stderr by the runtime in the Lambda) are automatically sent to CloudWatch Logs, intolog groups under the /aws/lambda/ prefix.AWS Lambdasuspends (freezes) all processes in its execution environmentonce the invocation is complete and until the next invocation, if any. This means any asynchronous background
processes and threads, including watchtower, will be suspended and inoperable, so watchtower cannot function
correctly in this execution model.AuthorsAndrey KislyukLinksProject home page (GitHub)DocumentationPackage distribution (PyPI)AWS CLI CloudWatch Logs pluginDocker awslogs adapterBugsPlease report bugs, issues, feature requests, etc. onGitHub.LicenseLicensed under the terms of theApache License, Version 2.0.