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Library for wrapping cluster logging for Altamira projects.

Project description

Cluster Logging library for python 3.6

This is a library for python 3.6 that will help fulfil a requirement for cluster services and logging to elastic search.


  • Python 3.6+
  • pipenv
  • Elasticsearch
  • Fluentd
  • Kibana

Pipenv & Pipfile

Pipenv combines package management and virtualenv into one tool.

To install:

$ pip install pipenv

Pipfile replaces requirements.txt and it will specify both dependencies and dev dependencies in one file.

Development Installation

# install dependencies
pipenv install --dev

# activate virtual environment
pipenv shell

# alternativly you can start your script in a virtual environment context
pipenv run python message that you want to send

Standalone Usage (Development)

It may be required to run the following command to get elastic search to run.

sudo sysctl -w vm.max_map_count=262144


$ pipenv run python -h
usage: [-h] [-n HOST] [-p PORT] [-e [ENV [ENV ...]]] [-o PROJECT]
               [-a APP] [-c COUNT]

Console Application to test Cluster Logging using fluentd.

positional arguments:
  message               Message to be logged.

optional arguments:
  -h, --help            show this help message and exit
  -n HOST, --host HOST  Hostname to listen on. (default=localhost)
  -p PORT, --port PORT  Port number to bind to. (default=24224)
  -e [ENV [ENV ...]], --env [ENV [ENV ...]]
                        Environment Keys to add to message.
  -o PROJECT, --project PROJECT
                        Tag for the project
  -a APP, --app APP     Tag for the application
  -c COUNT, --count COUNT
                        The number of times to send message.


$ docker build -t TAG_FOR_THE_BUILD:VERSION .

# all env values can be set with -e

$ docker run -e host=fluentd \
  -e port=24224 -e msg="message you want to send" TAG_FOR_THE_BUILD:VERSION

# If you want to run the whole environment you can use docker-compose up

$ docker-compose up


Follows the python logger implementation with logging levels.

Level Numeric value

Logging level can be set for logging using:

import logging

Setting the logging level will filter levels below the set logging level. For logging levels error and above the stack trace will be added to the log message.


These environment variables can be set either system-wide or added to the .env file:

There are 3 Environment variables that will be extracted by default:

  • HOST - host that the container is running on.
  • MARATHON_APP_ID - The ID that is assigned by DCOS.
  • MARATHON_APP_DOCKER_IMAGE - The image that the container was built from.

There are three ways to get application properties into log messages.

  • pass a dictionary of key value pairs
  • pass a list of keys that are on the container environment
  • or pass a JSON file with key value pairs.


  • The dictionary will overwrite values in environment and JSON file.
  • The Environment will overwrite values in the JSON file.


Activate your venv

# Linux / MacOS
source {NAME_OF_VENV}/bin/activate
REM Windows

Install cluster logger into your venv. Cluster Logger has been installed into pypi.

$ pip install cluster_logger

To Use

import cluster_logger
import logging
# Then init the ClusterLogger class

props = {'HOST', ''}

logging.basicConfig(level=logging.DEBUG)             # set logging level
cluster_logger.initLogger('Rasters',                 # Project name
                          'intent_client',           # Application name
                          'fluentd',                 # Fluentd Host name (optional) will default to fluentd if nothing is passed in and HOST isn't set on the environment
                          24224,                     # Fluentd Port Number(optional)
                          '/path/to/settings.json',  # JSON settings file (optional)
                          env_keys,                  # Environment Keys (optional)
                          props)                     # Specific Properties (optional)

# in Module where you will log
import cluster_logger

logger = cluster_logger.getLogger(__name__)

logger.exception('Custom Message to send typically an error')


# bump the version that is stored in and cluster_logger/
$ pipenv run bumpversion minor # possible: major / minor / patch
$ git push

Project details

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