Setting up an Index

We are going to issue commands via Kibana's dev tools console. You can alternatively use a REST client like Postman or curl to achieve this.

First we need to create an index for our data. We can do this simply via the following request:

PUT /my-example-movies

Step 2: Setup Movies Index

Next we need to setup the index fields, ready for us to ingest data.

The mapping for an index depends on the data you want to index and the features you want.

When you index data, Elasticsearch will automatically detect the data type of the field and create a mapping for it. However, you can also explicitly define the mapping for an index. This is useful when you want to define the mapping before you index any data, or when you want to change the mapping for an existing index.

Some of the mapping features required are mentioned in filters and facets.

Examples

Searchable Fields

We want to be able to search on title. We need only one field of type text.

{
  "properties": {
    "title": {
      "type": "text"
    }
  }
}

Searchable and Filterable Fields

We want to be able to search and be facetable for the writers field. We need two fields of different types: keyword and text.

{
  "properties": {
    "writers": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword"
        }
      }
    }
  }
}

Numerical fields for Facets

We want to be able to facet and filter on the year field. We need a field of type integer. Can be other numerical types, see Numerical type documentation (opens in a new tab)

{
  "properties": {
    "year": {
      "type": "integer" // can also be other numerical types like long, short, byte, double, float
    }
  }
}

Date fields for Facets

We want to be able to facet and filter on the date field. We need a field of type date.

{
  "properties": {
    "released": {
      "type": "date"
    }
  }
}

For our movie data-set, we will be using the following fields:

• title (searchable)
• plot (searchable)
• genre (searchable, facetable)
• actors (searchable, facetable)
• directors (searchable, facetable)
• released (filterable)
• imdbRating (filterable)
• url

The mapping file will be as follows, and we'll once again use Kibana's dev tools console to update the mapping file for our index.

PUT /my-example-movies/_mapping
{
  "properties": {
    "title": {
      "type": "text"
    },
    "plot": {
      "type": "text"
    },
    "genre": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword"
        }
      }
    },
    "actors": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword"
        }
      }
    },
    "directors": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword"
        }
      }
    },
    "released": {
      "type": "date"
    },
    "imdbRating": {
      "type": "float"
    },
    "url": {
      "type": "keyword"
    }
  }
}

Elasticsearch will acknowledge the request in the response.

Step 3: Index Movies Data

Now with our index and mapping file created, we are ready to index some data! We will use the bulk API to index our data.

We will use the following request. In this example we will be indexing the first movie in the data-set to verify that the data fields is being indexed correctly.

PUT /my-example-movies/_bulk
{ "index": {}}
{"title": "The Godfather", "released": "1972-03-23T23:00:00.000Z","genre": ["Crime", "Drama"],"directors": ["Francis Ford Coppola"],"actors": ["Marlon Brando", "Al Pacino", "James Caan", "Richard S. Castellano"],"plot": "The aging patriarch of an organized crime dynasty transfers control of his clandestine empire to his reluctant son.","imdbRating": "9.2", "url": "https://www.imdb.com/title/tt0068646/"}

Your Elasticsearch instance is now ready to be used with Searchkit. See the getting started guide for more information on how to use Searchkit.