Watchtower is a log handler for Amazon 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 the Python logging system and CloudWatch Logs. It uses the boto3 AWS SDK, and lets you plug your application logging directly into CloudWatch without the need to install a system-wide log collector like awscli-cwlogs and 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).
pip install watchtower
Install awscli and set your AWS credentials (run aws configure).
import watchtower, logging
logging.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 your AWS console under the watchtower log group.
The 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 the
Boto3 Credentials documentation.
When running Watchtower on an EC2 instance or other AWS compute resource, boto3 automatically loads credentials from
instance 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 AWS
managed IAM policy to the
role. While AWS provides no generic managed CloudWatch Logs writer policy, we recommend that you use the
arn:aws:iam::aws:policy/AWSOpsWorksCloudWatchLogs managed policy, which has just the right permissions without being
overly broad.
Watchtower supports the tagging of log groups. This can be done by adding the parameter log_group_tags to the
CloudWatchLogHandler constructor. This parameter should be a dictionary of tags to apply to the log group.
If you want to add tags to the log group you will need to add permission for the logs:TagResource action to your
policy. This will need to be in addition to the AWSOpsWorksCloudWatchLogs policy. (Note: the older logs:TagLogGroup
permission is for the tag_log_group() call which is on the path to deprecation and is not used by Watchtower.)
Use the following configuration to send Flask logs to a CloudWatch Logs stream called "loggable":
import watchtower, flask, logging
logging.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('/')
def hello_world():
return 'Hello World!'
if __name__ == '__main__':
app.run()(See also http://flask.pocoo.org/docs/errorhandling/.)
This is an example of Watchtower integration with Django. In your Django project, add the following to settings.py:
import boto3
AWS_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 group YOUR_DJANGO_PROJECT_NAME.
To supply AWS credentials to this configuration in development, set your
AWS CLI profile settings with
aws 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 from
instance metadata.
(See also the Django logging documentation.)
This 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 supports
extraction of your logs into an S3 bucket,
log analysis with a query language,
and alerting and dashboards based on metric 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 called
aegea that includes the commands aegea logs and aegea grep to easily
access the S3 Export and Insights features.
The Python logging.config module 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 resulting dict object
can then be loaded into logging.config.dictConfig. The first example is a basic example that relies on the default
configuration provided by boto3:
# Default AWS Config
version: 1
disable_existing_loggers: False
handlers:
console:
class: logging.StreamHandler
level: DEBUG
stream: ext://sys.stdout
logfile:
class: logging.handlers.RotatingFileHandler
level: DEBUG
filename: watchtower.log
maxBytes: 1000000
backupCount: 3
watchtower:
class: watchtower.CloudWatchLogHandler
level: DEBUG
log_group_name: watchtower
log_stream_name: "{logger_name}-{strftime:%y-%m-%d}"
send_interval: 10
create_log_group: False
root:
level: DEBUG
propagate: True
handlers: [console, logfile, watchtower]
loggers:
botocore:
level: INFO
urllib3:
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 the boto3_profile_name option to Watchtower can be used to provide a boto3 profile name:
# AWS Config Profile
version: 1
...
handlers:
...
watchtower:
boto3_profile_name: watchtowerlogger
...Finally, the following shows how to load the configuration into the working application:
import logging.config
import flask
import yaml
app = flask.Flask("loggable")
@app.route('/')
def hello_world():
return 'Hello World!'
if __name__ == '__main__':
with open('logging.yml') as log_config:
config_yml = log_config.read()
config_dict = yaml.safe_load(config_yml)
logging.config.dictConfig(config_dict)
app.run()For 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.
Because 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.CloudWatchLogHandler attaches a
filter to itself which drops all DEBUG
level messages from these libraries, and drops all messages at all levels from them when shutting down (specifically,
in watchtower.CloudWatchLogHandler.flush() and watchtower.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:
import watchtower, logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
logger.addHandler(watchtower.CloudWatchLogHandler())Watchtower 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, into log groups under the /aws/lambda/ prefix.
AWS Lambda suspends (freezes) all processes in its execution environment once 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.
- Andrey Kislyuk
- Project home page (GitHub)
- Documentation
- Package distribution (PyPI)
- AWS CLI CloudWatch Logs plugin
- Docker awslogs adapter
Please report bugs, issues, feature requests, etc. on GitHub.
This package follows the Semantic Versioning 2.0.0 standard. To control changes, it is recommended that application developers pin the package version and manage it using pip-tools or similar. For library developers, pinning the major version is recommended.
Licensed under the terms of the Apache License, Version 2.0.
