watchtower 1.0.5

Python CloudWatch Logging

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 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).

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.

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:

from boto3.session import Session

AWS_ACCESS_KEY_ID = 'your access key'
AWS_SECRET_ACCESS_KEY = 'your secret access key'
AWS_REGION_NAME = 'your region'

boto3_session = Session(aws_access_key_id=AWS_ACCESS_KEY_ID,
                        aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
                        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_session': boto3_session,
            'log_group': 'MyLogGroupName',
            '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, you can also assign an IAM role to your instance and omit those parameters, prompting boto3 to ingest 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'

CloudWatch Logs supports 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: watchtower
    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.