Gerboise is a project that allows app administrators to collect and visualise significant logs from their servers. It was designed to be really easy to setup; however, some familiarity with Kibana and Elasticsearch is required.
There are two parts of Gerboise that need to be configured and launched. First, there is the log storage server that runs Elasticsearch and Kibana. Nginx with basic authentication runs in front of Elasticsearch. It is quite possible to run many Elasticsearch/Kibana instances for many startups on just one server: ports should not conflict. Then, log collection must be run on the application servers.
All services on both servers are configured to run inside docker containers. Thus, docker and docker-compose install need to be installed on all servers.
Install system requirements (htpasswd and python):
apt install apache2-utils python
Generate configuration files for your application:
./configure.py myapp
The application ID will be used to generate ports for all services. Thus, it needs to be unique to this application.
The configuration files will then be located in apps/myapp. You can now run Nginx, Elasticsearch and Kibana with:
cd apps/myapp
docker-compose up
You should be able to open Kibana at http://localhost:56YOURAPPID. By default, Kibana comes with no dashboard or visualisation. You can import a few pre-configured dashboards and visualisation in Management 🠦 Saved Objects 🠦 Import 🠦 Select kibana.json. (NOTE: this might not work because you don't have the same index IDs, but it's worth trying)
Copy the apps/myapp/docker-compose-fluentbit.yml generated by the configure.py myapp command to your application servers. This is a docker-compose file that collects CPU stats, memory usage and nginx access logs and sends them to your Elasticsearch instance. To start collecting logs, just run:
ES_PASSWORD=mypassword docker-compose -f docker-compose-fluentbit.yml
- Let's encrypt certificates for nginx
- run curator periodically to clean ES indices