How to map on existing indices for sorting?

I am looking for sorting in Elasticsearch. Before I insert the indices, I execute following

PUT product
{
  "mappings": {
    "properties": {
      "manufacturer": {
        "type": "keyword"
      }
    }
  }
}

After I can make a sort with manufacturer. But now I want to sort by brand.

GET product/_search
{
  "size": 5, 
  "query": {
    "match": {
      "manufacturer": "abc"
    }
  },
  "sort": [
    {
      "brand": {
        "order": "desc"
      }
    }
  ]
}

It gives me following error:

"type" : "illegal_argument_exception",
        "reason" : "Text fields are not optimised for operations that require per-document field data like aggregations and sorting, so these operations are disabled by default. Please use a keyword field instead. Alternatively, set fielddata=true on [brand] in order to load field data by uninverting the inverted index. Note that this can use significant memory."

Is it possible to make a mapping on already existing indices ?

>Solution :

You can use below command to check current index mapping:

GET product

If your current index mapping is look like below then you can use brand.keyword.

{
  "mappings": {
    "properties": {
      "manufacturer": {
        "type": "keyword"
      },
      "brand": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      }
    }
  }
}

You can use below query to sort on brand field if your current index mapping is same as above:

GET product/_search
{
  "size": 5, 
  "query": {
    "match": {
      "manufacturer": "abc"
    }
  },
  "sort": [
    {
      "brand.keyword": {
        "order": "desc"
      }
    }
  ]
}

Is it possible to make a mapping on already existing indices ?

Yes, you can update mapping of existing index using below command but you need to reindex data if you are updating mapping of existing field.

PUT product/_mapping
{
  "properties": {
    "brand": {
      "type": "text",
      "fields": {
        "keyword": {
          "type": "keyword",
          "ignore_above": 256
        }
      }
    }
  }
}