Log Aggregation made to Scale

Humio is a tool for aggregating, exploring, reporting and analysing machine data and system logs in real-time. Machine data is a fast-growing, complex area in big data, which provides immediate value to your business.

Humio gathers data from a range of sources, both cloud and on-premise systems, and makes it readily available for searching and monitoring business performance, and for identifying and solving problems in your infrastructure.


Seamless Integration

Humio supports collecting data from a range of sources, and makes it easy to do custom integrations. Examples include:

  • Log file monitoring
  • LogStash clients
  • Syslog
  • NetFlow

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Live Dashboards

Building live dashboards is as easy as making a query. You get an overview of the real-time status of your system. Use the build-in dashboards in the UI or use your own monitoring tools and get the data through a REST API.

204 | loglevel != ERROR
Matched 20,102,120 events in 12ms
2016-02-10 16:13
[FATAL] The firewall 'Amadeus' has been killed by a grue.
2016-02-10 16:11
[INFO] Segment fault averted 0x12D318AA2820499292.
2016-02-10 16:10
[WARN] 404 File not found. path=/admin/login ip=
2016-02-10 15:59
[INFO] 302 No Content. path=/img/profile2041.jpg ip=
2016-02-10 15:58
[WARN] 404 File not found. path=/admin ip=

Map-Reduce Query Language

Humio is build as a distributed system from the ground up. The query language combined with the web UI allow you to step by step explore your data and reshape your data to get a better understanding of it.

You can quickly create Real-Time charts or lists. You can perform blazingly fast full-text search on all records, or search for specific field values for further control.



Apart from interacting with Humio through the built-in UI, all Humio's features are availible through a HTTP API. That makes it easy to integrate with external systems.

Example: Query for counting number of logins per user

eventname="login" | groupby(user, function=count())

Example: Query Request

 "queryString": "eventname=login | groupby(user, function=count())",
 "start": "24h",
 "end": "now",
 "isLive": false
Example: Query Response
    "timeMillis": 42,
    "done": true,
    "events": [{
      "_count": "5159",
      "user": "Wonderman",
      "@id": "1768355452"
    }, {
      "_count": "212",
      "user": "peter",
      "@id": "422350914"
    }, {
      "_count": "9221",
      "user": "xorg1985",
      "@id": "1394554284"