watchtower 2.0.0

Installation

pip install watchtower

Synopsis

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.

IAM permissions

The process running watchtower needs to have access to IAM credentials to call the CloudWatch Logs API. The process 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 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, it is recommended 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

import watchtower, flask, logging

logging.basicConfig(level=logging.INFO)
app = flask.Flask("loggable")
handler = watchtower.CloudWatchLogHandler()
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/.)

Example: Django logging with Watchtower

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

import boto3

AWS_REGION_NAME = 'your region'

boto3_logs_client = boto3.client(region_name=AWS_REGION_NAME)

LOGGING = {
    'version': 1,
    'disable_existing_loggers': False,
    'root': {
        'level': logging.ERROR,
        'handlers': ['console'],
    },
    'formatters': {
        'simple': {
            'format': "%(asctime)s [%(levelname)-8s] %(message)s",
            'datefmt': "%Y-%m-%d %H:%M:%S"
        },
        'aws': {
            # you can add specific format for aws here
            'format': "%(asctime)s [%(levelname)-8s] %(message)s",
            'datefmt': "%Y-%m-%d %H:%M:%S"
        },
    },
    'handlers': {
        'watchtower': {
            'level': 'DEBUG',
            'class': 'watchtower.CloudWatchLogHandler',
            'boto3_client': boto3_client,
            'log_group_name': 'MyLogGroupName',
            'log_stream_name': 'MyStreamName',
            'formatter': 'aws',
        },
    },
    'loggers': {
        'django': {
            'level': 'INFO',
            'handlers': ['watchtower'],
            'propagate': False,
        },
        # add your other loggers here...
    },
}

Using this configuration, every log statement from Django will be sent to Cloudwatch in the log group MyLogGroupName under the stream name MyStreamName. Instead of setting credentials via AWS_ACCESS_KEY_ID and other variables in settings.py, it is recommended that you assign an IAM role to your instance, prompting 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.

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
formatters:
  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.StreamHandler
    formatter: plaintext
    level: DEBUG
    stream: ext://sys.stdout
  logfile:
    class: logging.handlers.RotatingFileHandler
    formatter: plaintext
    level: DEBUG
    filename: watchtower.log
    maxBytes: 1000000
    backupCount: 3
  watchtower:
    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
root:
  level: DEBUG
  propagate: True
  handlers: [console, logfile, watchtower]
loggers:
  botocore:
    level: INFO
  urllib3:
    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
...
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()

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
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
logger.addHandler(watchtower.CloudWatchLogHandler())

Authors

• Andrey Kislyuk

Links

• Project home page (GitHub)

• Documentation

• Package distribution (PyPI)

• AWS CLI CloudWatch Logs plugin

• Docker awslogs adapter

Bugs

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

License

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