2018/Data Project
From charlesreid1
Project Overview
The 2018 data project is an ongoing effort to figure out how to set up "painless" dashboards.
Phase 1: Netdata and Prometheus
First, we set up Netdata to dump to a Prometheus database.
- Pros: Netdata has a fantastic dashboard with all kinds of stuff all ready to go. Prometheus was fairly easy to integrate with Netdata.
- Cons: Netdata is custom-built for monitoring compute nodes, and not for general visualization. Prometheus was not a particularly outstanding tool, don't know much about how to use it.
- Netdata
- Prometheus
Netdata is a useful tool for monitoring an individual machine instance remotely. Need to get more involved with Prometheus and/or Grafana to monitor more than one machine.
Phase 2: MongoDB and MongoExpress
We then set up MongoDB and MongoExpress in Docker containers, and have MongoDB listening over the VPN for incoming data. We used this to scrape information from the charlesreid1 wiki.
- Pros: MongoDB is a containerized solution with persistent data. MongoDB had (has?) a high setup barrier, but a low usage barrier. Very easy to do basic CRUD operations, make new databases as needed, etc.
- Cons: No visualization tools baked in, need to define own tools. Collectd cannot dump to MongoDB because of a bunch of installation stupidity.
- MongoDB
- MongoExpress
- Pywikibot
- Link to MongoDB docker files: https://charlesreid1.com:3000/docker/d-mongodb
- Link to MongoExpress docker files: https://charlesreid1.com:3000/docker/d-mongoexpress
- Link to wiki scraping scripts: https://charlesreid1.com:3000/wiki/charlesreid1-wiki-data
Next, we deployed a Graphite container to hold time series and a Grafana container to create dashboards from it.
- Pros: Containerized solution, like MongoDB
- Link to Graphite docker files: https://charlesreid1.com:3000/docker/d-graphite
- Link to Grafana docker files: https://charlesreid1.com:3000/docker/d-graphite