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Diskover – File System Crawler, Storage Search Engine And Analytics Powered By Elasticsearch

diskover is an open source file system crawler and disk usage software that uses Elasticsearch to index and manage data across heterogeneous storage systems. Using diskover, you are able to more effectively search and organize files and system administrators are able to manage storage infrastructure, efficiently provision storage, monitor and report on storage use, and effectively make decisions about new infrastructure purchases.
As the amount of file data generated by business’ continues to expand, the stress on expensive storage infrastructure, users and system administrators, and IT budgets continues to grow.
Using diskover, users can identify old and unused files and give better insights into data change, file duplication and wasted space.
diskover is written and maintained by Chris Park (shirosai) and runs on Linux and OS X/macOS using Python 2/3.

Screenshots
diskover-web (diskover’s web file manager, analytics app, file system search engine, rest-api)

Kibana dashboards/saved searches/visualizations and support for Gource

Diskover Gource videos

[youtube https://www.youtube.com/watch?v=InlfK8GQ-kM]
[youtube https://www.youtube.com/watch?v=qKLJjZ0TMqA]

Installation Guide

Requirements

  • Linux or OS X/macOS (tested on OS X 10.11.6, Ubuntu 16.04)
  • Python 2.7. or Python 3.5./3.6. (tested on Python 2.7.14, 3.5.3, 3.6.4)
  • Python elasticsearch client module
  • Python requests module
  • Python scandir module
  • Python progressbar2 module
  • Python redis module
  • Python rq module
  • Elasticsearch 5 (local or AWS ES Service, tested on Elasticsearch 5.4.2, 5.6.4) Elasticsearch 6 is not supported yet.
  • Redis (tested on 4.0.8)

Install the above Python modules using pip.

Optional Installs

  • diskover-web (diskover’s web file manager and analytics app)
  • Redis RQ Dashboard (for monitoring redis queue)
  • sharesniffer (for scanning your network for file shares and auto-mounting for crawls)
  • Kibana (for visualizing Elasticsearch data, tested on Kibana 5.4.2, 5.6.4)
  • X-Pack (Kibana plugin for graphs, reports, monitoring and http auth)
  • Gource (for Gource visualizations of diskover Elasticsearch data, see videos above)

Download

$ git clone https://github.com/shirosaidev/diskover.git
$ cd diskover

Download latest version

Requirements
You need to have at least Python 2.7. or Python 3.5. and have installed required Python dependencies using pip.

$ pip install -r requirements.txt

Getting Started
Copy diskover config diskover.cfg.sample to diskover.cfg and edit for your environment.
Start diskover worker bots (as many as you want, a good number might be cores x 2) with:

$ cd /path/with/diskover
$ python diskover_worker_bot.py

Worker bots can be added during a crawl to help with the queue. To run a worker bot in burst mode (quit after all jobs done), use the -b flag. If the queue is empty these bots will die, so use rq info or rq-dashboard to see if they are running. Run diskover-bot-launcher.sh to spawn and kill multiple bots.
Start diskover main job dispatcher and file tree crawler with:

$ python /path/to/diskover.py -d /rootpath/you/want/to/crawl -i diskover-indexname -a

Defaults for crawl with no flags is to index from . (current directory) and files >0 Bytes and 0 days modified time. Empty files and directores are skipped (unless you use -s 0 and -e flags). Use -h to see cli options.

User Guide
Read the wiki for more documentation on how to use diskover.


Source: FeedBurner

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