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https://github.com/kislyuk/watchtower

Python CloudWatch Logging: Log Analytics and Application Intelligence
https://github.com/kislyuk/watchtower

aws aws-cloudwatch-logs cloudwatch-logs python python-logging

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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, 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).



Installation

~~~~~~~~~~~~

::



    pip install watchtower



Synopsis

~~~~~~~~

Install `awscli `_ and set your AWS credentials (run ``aws configure``).



.. code-block:: python



    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.



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.



Log Group Tagging

~~~~~~~~~~~~~~~~~

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.)



Example: Flask logging with Watchtower

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



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



.. code-block:: python



    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/ `_.)



Example: Django logging with Watchtower

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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



.. code-block:: python



    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 `_.)



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``:



.. code-block:: yaml



    # 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: 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:



.. code-block:: yaml



    # AWS Config Profile

    version: 1

    ...

    handlers:

      ...

      watchtower:

        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 = log_config.read()

            config_dict = yaml.safe_load(config_yml)

            logging.config.dictConfig(config_dict)

            app.run()



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:



.. code-block:: python



    import watchtower, logging

    logging.basicConfig(level=logging.DEBUG)

    logger = logging.getLogger()

    logger.addHandler(watchtower.CloudWatchLogHandler())



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.



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 `_.



Versioning

~~~~~~~~~~

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.



License

~~~~~~~

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



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