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).
pip install watchtower
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.
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/.)
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': u"%(asctime)s [%(levelname)-8s] %(message)s",
'datefmt': "%Y-%m-%d %H:%M:%S"
},
'aws': {
# you can add specific format for aws here
'format': u"%(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](https://docs.djangoproject.com/en/dev/topics/logging/)).
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. The following example shows logging structured JSON data using Watchtower, setting up a metric filter to extract data from the log stream, a dashboard to visualize it, and an alarm that sends an email:
TODO
Python has the ability to provide a configuration file that can be loaded in order to separate the logging
configuration from the code. Historically, Python has used the logging.config.fileConfig function to do
so, however, that feature lacks the ability to use keyword args. Python 2.7 introduced a new feature to
handle logging that is more robust - logging.config.dictConfig which profiles the ability to do more
advanced Filters, but more importantly adds keyword args, thus allowing the logging.config functionality
to instantiate Watchtower.
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
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:
(): logging.StreamHandler
level: DEBUG
formatter: plaintext
stream: sys.stdout
watchtower:
formatter: json
level: DEBUG
(): watchtower.CloudWatchLogHandler
log_group: logger
stream_name: loggable
send_interval: 1
create_log_group: False
loggers:
root:
handlers: [console, watchtower, logfile]
boto:
handlers: [console]
boto3:
handlers: [console]
botocore:
handlers: [console]
requests:
handlers: [console]The above works well if you can use the default configuration, or rely on environmental 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 profile a profile name:
# AWS Config Profile
version: 1
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:
(): logging.StreamHandler
level: DEBUG
formatter: plaintext
stream: sys.stdout
watchtower:
formatter: json
level: DEBUG
(): watchtower.CloudWatchLogHandler
log_group: logger
stream_name: loggable
boto3_profile_name: watchtowerlogger
send_interval: 1
create_log_group: False
loggers:
root:
handlers: [console, watchtower, logfile]
boto:
handlers: [console]
boto3:
handlers: [console]
botocore:
handlers: [console]
requests:
handlers: [console]For the more advanced configuration, the following configuration file will profile
the matching credentials to the watchtowerlogger profile:
[profile watchtowerlogger]
aws_access_key_id=MyAwsAccessKey
aws_secret_access_key=MyAwsSecretAccessKey
region=us-east-1Finally, 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', 'r') as log_config:
config_yml = log_config.read()
config_dict = yaml.load(config_yml)
logging.config.dictConfig(config_dict)
app.run()- Andrey Kislyuk
- Project home page (GitHub)
- Documentation (Read the Docs)
- Package distribution (PyPI)
- AWS CLI CloudWatch Logs plugin
- Docker awslogs adapter
Please report bugs, issues, feature requests, etc. on GitHub.
Licensed under the terms of the Apache License, Version 2.0.
