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Python CloudWatch Logging

Project description

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
logger = logging.getLogger(__name__)
logger.addHandler(watchtower.CloudWatchLogHandler())"Hi")"bar", details={}))

After running the example, you can see the log output in your AWS console under the watchtower log group.

IAM permissions

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.

Example: Flask logging with Watchtower

Use the following configuration to send Flask logs to a CloudWatch Logs stream called “loggable”:

import watchtower, flask, logging

app = flask.Flask("loggable")
handler = watchtower.CloudWatchLogHandler(

def hello_world():
    return 'Hello World!'

if __name__ == '__main__':

(See also

Example: Django logging with Watchtower

This is an example of Watchtower integration with Django. In your Django project, add the following to

import boto3

AWS_REGION_NAME = "us-west-2"

boto3_logs_client = boto3.client("logs", region_name=AWS_REGION_NAME)

    '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 (` 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.)

Examples: Querying CloudWatch logs

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.

Examples: Python Logging Config

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
    format: "[%(asctime)s] %(process)d %(levelname)s %(name)s:%(funcName)s:%(lineno)s - %(message)s"
    format: "[%(asctime)s] %(process)d %(levelname)s %(name)s:%(funcName)s:%(lineno)s - %(message)s"
    class: logging.StreamHandler
    formatter: plaintext
    level: DEBUG
    stream: ext://sys.stdout
    class: logging.handlers.RotatingFileHandler
    formatter: plaintext
    level: DEBUG
    filename: watchtower.log
    maxBytes: 1000000
    backupCount: 3
    class: watchtower.CloudWatchLogHandler
    formatter: json
    level: DEBUG
    log_group_name: watchtower
    log_stream_name: "{logger_name}-{strftime:%y-%m-%d}"
    send_interval: 10
    create_log_group: False
  level: DEBUG
  propagate: True
  handlers: [console, logfile, watchtower]
    level: INFO
    level: INFO

The 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
    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")

def hello_world():
    return 'Hello World!'

if __name__ == '__main__':
    with open('logging.yml') as log_config:
        config_yml =
        config_dict = yaml.safe_load(config_yml)

Log stream naming

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.

Boto3/botocore/urllib3 logs

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
logger = logging.getLogger()

AWS Lambda

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


Please report bugs, issues, feature requests, etc. on GitHub.


Licensed under the terms of the Apache License, Version 2.0.

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