tbobm / trail

Simple and scalable Docker container "tail -f". Centralize logs to a single web page. Built using ansible, docker and elastic tools.

Trail

School project built during the 168h "codecamp".

Goal

Create a painless web tail for containers based on logstash / fluentbit

See the subject for further description.

Usage

Start the project

In order to bring the whole stack up:

docker-compose up -d

Deploying agents

The ansible role trail will deploy a docker-enabled filebeat container with the required permissions (read capability on the docker socket and container directories).

An example playbook demonstrates a basic usage of this role. The LOGSTASH_TARGET is required, as it is used in the filebeat config file.

The trail role will:

1. Template the filebeat configuration in /etc/filebeat.yml
2. Ensure pip is available on the remote host
3. Ensure the python docker package is available on the remote host and install it if necessary
4. Run a container named filebeat_agent on the remote host

Running the frontend

By running docker-compose up -d, you will have the api on your local port 3000 and the frontend on the port 8080.

Architecture

Example projects

A Vagrantfile is provided to experiment with trail. It will create 5 VMs with multiple docker-based example projects (wordpress, rails, django, a failing nginx configuration and a flog container).

Configuration

Install the required ansible role (docker installation and configuration) by running the following:

ansible-galaxy install -r requirements.yml

The LOGSTASH_TARGET variable in the provisioning playbook is supposed to be modified to suit your needs.

Running the example projects

In order to use the provisioning part of the Vagrantfile, simply bring the VMs up and Vagrant will automatically run the corresponding playbook based on the node name.

vagrant up

Backend

Stack

name	role
filebeat	log collection - fetch logs from containers, either by mapping the docker sockets/directories to filebeat, or deploy as a kubernetes pod
logstash	centralize, parse and enrich logs
rabbitmq	access point for logs through the topic exchange called logs
flog	open source log generation tool
mongodb	short term storage (6h)

Running the backend only

I highly advise running each items one after the other, to ensure everything is running smooth.

1. docker-compose up -d broker
2. docker-compose up -d logstash
3. docker-compose up -d filebeat
4. docker-compose up -d storage
5. docker-compose up log-generator (in order to have it in the foreground)

How to consume logs from the broker

The Exchange is automatically created by Logstash at startup time, based on the

• Connect to RabbitMQ
• Create a Queue (NOTE: define if queue have to be exclusive and/or durable)
• Bind your Queue to the Exchange, specify if needed a routing key
• Consume the messages from your Queue

Short term log retention

The logstash pipeline will, in addition to forwarding the logs in RabbitMQ, store the logs in MongoDB.

As seen in the mongoscripts configuration directory, an unoptimized index based on the @timestamp field will enable the deletion of each log after 6 hours.

Querying these logs through the frontend was not implemented, a helper script search.py was added instead as a lightweight replacement.

Note: Implementing this in the frontend should be quite quick

Example usage

# install pymongo
pip install --user -r requirements.txt

export MONGODB_URI=mongodb://localhost:27017/trail  # This script uses the environment variable MONGODB_URI to connect to MongoDB
python search.py term
# ...
# <@timestamp>, <message>

Logs

Schema

See the log schema.