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Python CloudWatch Logging: Log Analytics and Application Intelligence

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Watchtower: 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
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

_ and set your AWS credentials (run
aws configure

.. code-block:: python

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

Example: Flask logging with Watchtower ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

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':

(See also 

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

.. code-block:: python

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

under the stream name
. Instead of setting credentials via
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

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

object can then be loaded into
. The first example is a basic example that relies on the default configuration provided by

.. code-block:: yaml

# 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: watchtower
    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

option to Watchtower can be used to provide a boto3 profile name:

.. code-block:: yaml

# AWS Config Profile
version: 1
    boto3_profile_name: watchtowerlogger

Finally, the following shows how to load the configuration into the working application:

.. code-block:: python

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 = config_dict = yaml.safe_load(config_yml) logging.config.dictConfig(config_dict)

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,

attaches a
_ 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
). 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:

.. code-block:: python

import watchtower, logging
logger = logging.getLogger()

Authors ~~~~~~~ * Andrey Kislyuk

Links ~~~~~ *

Project home page (GitHub) 
_ *
_ *
Package distribution (PyPI) 
_ *
AWS CLI CloudWatch Logs plugin 
_ *
Docker awslogs adapter 

Bugs ~~~~ Please report bugs, issues, feature requests, etc. on


License ~~~~~~~ Licensed under the terms of the

Apache License, Version 2.0 

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