Dec 7th, 2022: [EN] Map your distributed team in Kibana

Elastic Community and Ecosystem Advent Calendar
1

christmas
christmas1202×630 148 KB

Working in a distributed company like Elastic is great, but can sometimes be hard to grasp. Answering some seemingly simple questions can be surprisingly complicated, like:

  • Show our team on a map, so I can explain our reach and structure.
  • Let me find anyone in a certain area, so we know who is "local".
  • Find someone with a specific skill close by.
  • Show the team growth over time.
  • Document region coverage, so anyone can look it up.

Luckily, Kibana can help here and the goal of this post is to create a map like this:

Community team in Europe
Community team in Europe1920×1031 118 KB

Mapping and Data in Elasticsearch

Before you can jump into the visualization, you need to create the correct mapping (or schema — this isn't directly related to a map yet :sweat_smile:). Run the following request in Kibana's Console to create the mapping:

PUT team
{
  "mappings": {
    "dynamic": "strict",
    "properties": {
      "name": {
        "type": "keyword"
      },
      "team": {
        "type": "keyword"
      },
      "role": {
        "type": "keyword"
      },
      "start_date": {
        "type": "date"
      },
      "country_code": {
        "type": "keyword"
      },
      "city": {
        "type": "keyword"
      },
      "location": {
        "type": "geo_point"
      },
      "technology": {
        "type": "keyword"
      },
      "region": {
        "type": "keyword"
      },
      "picture": {
        "type": "keyword"
      }
    }
  }
}

Most important in the mapping is:

  • geo_point is the most important data type to use on a map.
  • The date field is necessary to show the development over time.

To keep it simple, the sample data set used only includes the Community team in Europe:

PUT team/_doc/david
{
  "name": "David Pilato",
  "team": "community",
  "role": "Developer 🥑",
  "start_date": "2013-01-10",
  "country_code": "fr",
  "city": "Cergy",
  "location": {
    "lat": 49.05,
    "lon": 2.04
  },
  "technology": ["java", "elasticsearch", "search"],
  "picture": "https://avatars.githubusercontent.com/u/274222"
}
PUT team/_doc/philipp
{
  "name": "Philipp Krenn",
  "team": "community",
  "role": "Developer 🥑 | EMEA Lead",
  "start_date": "2016-04-01",
  "country_code": "at",
  "city": "Vienna",
  "location": {
    "lat": 48.21,
    "lon": 16.37
  },
  "technology": ["java", "automation", "observability"],
  "picture": "https://avatars.githubusercontent.com/u/432211"
}
PUT team/_doc/adrienne
{
  "name": "Adrienne de Vries",
  "team": "community",
  "role": "Community Programs",
  "start_date": "2019-12-02",
  "country_code": "nl",
  "city": "Rotterdam",
  "location": {
    "lat": 51.92,
    "lon": 4.48
  },
  "region": ["FR", "ES", "PT", "IT", "GR", "TR", "CY", "NL", "BE", "DK", "LU", "NO", "SE", "FI", "IS", "EE", "LV", "LT", "MA", "TN", "DZ"],
  "picture": "https://ca.slack-edge.com/T0CUZ52US-UQQ55JABU-56457843fc81-512"
}
PUT team/_doc/naoise
{
  "name": "Naoise Rush",
  "team": "community",
  "role": "Community Programs",
  "start_date": "2022-02-28",
  "country_code": "gb",
  "city": "Belfast",
  "location": {
    "lat": 54.60,
    "lon": -5.93
  },
  "region": ["GB", "IE", "DE", "AT", "CH", "PL", "CZ", "SK", "SI", "HU", "RO", "BG", "MD", "UA", "HR", "RS", "BA", "AL", "XK", "ME", "MK", "IL", "SA", "EG", "AE", "JO", "LB", "KW", "QA", "OM", "BH"],
  "picture": "https://ca.slack-edge.com/T0CUZ52US-U033ZF1HPM3-3f8511dbc26f-512"
}
PUT team/_doc/carly
{
  "name": "Carly Richmond",
  "team": "community",
  "role": "Developer 🥑",
  "start_date": "2022-04-25",
  "country_code": "uk",
  "city": "London",
  "location": {
    "lat": 51.55,
    "lon": 0.16
  },
  "technology": ["javascript", "frontend", "observability"],
  "picture": "https://avatars.githubusercontent.com/u/74931905"
}

Adding the Data in Kibana

Before you can use the data in Kibana, you need to add the indexed data to a Data View:

Add the Data View
Add the Data View2128×1454 181 KB

And to use the pictures, you need to customize the picture field to a Url of the type Image and set the URL template to {{rawValue}}:

Customize the picture field
Customize the picture field1920×1049 95 KB

Once set up, you can view the underlying documents in Discover and change the timeframe to 10 years (from the default of 15 minutes):

Documents in Discover
Documents in Discover2844×1446 477 KB

Adding the First Map — at Last

Maps in Kibana have a lot of features, but the example will keep it simple. Add a layer of the type Documents:

Add a map based on documents
Add a map based on documents1920×1038 135 KB

And in the team index, pick the only available geo-point location:

Select the data source
Select the data source1920×1038 112 KB

Add the most relevant tooltip fields:

Customize the fields
Customize the fields902×1186 57.6 KB

And adjust the color of each document by the role:

Differentiate by color
Differentiate by color892×1198 51.9 KB

And that's all we need to get the team on the map. If you don't see anything, double-check that you have picked the past 10 years and not just 15 minutes again. Hovering over one of them reveals their profile and based on the color you can tell their role:

Showing the result
Showing the result1920×1031 118 KB

Mapping Time

Based on the start_date field for each team member, you can show how it developed over time or even autoplay it year by year:

Development over time
Development over time1920×938 136 KB

Putting Search on the Map

Search is core to what we do. So if you want to figure out who on our team could help you out with all things "frontend", you could also find that on the map:

Searching for skills and showing the results on the map
Searching for skills and showing the results on the map1920×973 115 KB

Mapping Regions

Our team also has the concept of owning certain regions. Time to add another layer to the map — this time it's the Choropleth. The region array of the Elasticsearch documents maps to the ISO 3166-1 alpha-2 code of countries. And we can limit it to the role of "Community Programs", since only they own regions:

Mapping regions to the team for ownership
Mapping regions to the team for ownership1620×1334 126 KB

Sprinkling a bit of color on top by name:

Coloring by name
Coloring by name1618×1342 262 KB

And we have neatly mapped out the ownership of the entire region:

Ownership in the region
Ownership in the region1920×1042 147 KB

Helping Santa Find the Team

To add something more Christmas related, let's help Santa find the team. This information should go in a new index with the simple mapping for source and destination geo-points in one document:

PUT santa
{
  "mappings": {
    "dynamic": "strict",
    "properties": {
      "@timestamp": {
        "type": "date"
      },
      "source": {
        "type": "geo_point"
      },
      "destination": {
        "type": "geo_point"
      }
    }
  }
}

And these are the documents for the actual travel route:

POST santa/_doc
{
  "@timestamp": "2022-12-24T19:30Z",
  "source": {
    "lat": 48.21,
    "lon": 100
  },
  "destination": {
    "lat": 48.21,
    "lon": 16.37
  }
}
POST santa/_doc
{
  "@timestamp": "2022-12-24T19:40Z",
  "source": {
    "lat": 48.21,
    "lon": 16.37
  },
  "destination": {
    "lat": 49.05,
    "lon": 2.04
  }
}
POST santa/_doc
{
  "@timestamp": "2022-12-24T19:50Z",
  "source": {
    "lat": 49.05,
    "lon": 2.04
  },
  "destination": {
    "lat": 51.92,
    "lon": 4.48
  }
}
POST santa/_doc
{
  "@timestamp": "2022-12-24T20:00Z",
  "source": {
    "lat": 51.92,
    "lon": 4.48
  },
  "destination": {
    "lat": 51.55,
    "lon": 0.16
  }
}
POST santa/_doc
{
  "@timestamp": "2022-12-24T20:10Z",
  "source": {
    "lat": 51.55,
    "lon": 0.16
  },
  "destination": {
    "lat": 54.60,
    "lon": -5.93
  }
}
POST santa/_doc
{
  "@timestamp": "2022-12-24T20:20Z",
  "source": {
    "lat": 54.60,
    "lon": -5.93
  },
  "destination": {
    "lat": 54.60,
    "lon": -20.00
  }
}

Add one more layer, this time with Point to point. In which you map source and destination:

Mapping source and destination in a point-to-point map
Mapping source and destination in a point-to-point map1624×1332 196 KB

And with some minor customization, you can show Santa the way to everyone on the team. Picking the right timeframe, you could even follow along as the reindeers are traversing every segment on the route:

Following Santa
Following Santa1920×1038 143 KB

Conclusion

We hope that this gave you some ideas on how to put distributed teams on the map. Kibana can still do a lot more — the documentation for maps is a great starting point there.

5 Likes
2

This topic was automatically closed 28 days after the last reply. New replies are no longer allowed.