Most search engines basically boil down to counting the number of times a query word appears in a document. Some of the more sophisticated ones use some algorithm to give each document a unique but vaguely-defined float value. That makes it hard to improve the relevance of search results or use any other data format.

Algolia works differently. We designed it mainly for database search, so the query-counting approach doesn’t work anymore. Instead, our ranking algorithm rates each matching record on several criteria (like the typo count or geo-distance), to which we individually assign an integer value score. You can even apply your own criteria to model your business logic directly inside the search engine. You get to pick the order of the criteria used, and then going down the list of criteria, all results which are currently tied are sorted.

A record’s score on each criterion is explicitly listed in the search results (see _rankingInfo  below for the query “the rains”), so you can understand how one record can rank higher than another one. We will explain each of these criteria in this article.

{
"hits": [
     {
          "name": "The Rains Came",
          "url": "/title/tt0031835/",
          "rating": 6.8,
          "year": "(1939)",
          "nb_voters": 881,
          "rank": 16232,
          "objectID": "24324",
          "_highlightResult": {
               "name": {
                    "value": "The Rains Came",
                    "matchLevel": "full"
                },
                "year": {
                     "value": "(1939)",
                     "matchLevel": "none"
                }
          },
          "_rankingInfo": {
               "nbTypos": 0,
               "firstMatchedWord": 0,
               "proximityDistance": 1,
               "userScore": 2379657,
               "geoDistance": 0,
               "geoPrecision": 1,
               "nbExactWords": 2
          }
},
...
}

Search-as-you-type

Before diving in, you first need to understand that Algolia searches for matching prefixes, not matching whole-words. For example, if you are searching for “Joe B”, we would consider all the following records as matches:

• Joe Black
• Joe Benson
• Joe Bolick

Prefix matching is what enables us to return relevant results even when a user has only typed a single letter. When Google introduced instant search, they claimed that showing results before you finish typing can save 2-5 seconds per search.

Note: By default, when the query contains multiple terms, Algolia only uses the last term as a prefix. This is because when searching, say, for a person by name, it’s quite normal to type their entire first name but not their last (like George Cloo). Not so for the reverse (like Geo Clooney). You can override this behavior by setting queryType=prefixAll.

Ranking algorithm criteria

By default, Algolia ranks every matching record by using the following criteria, in the order listed below. The higher up the criterion on the list, the more importance it has on ranking. You can easily change this order if you want, but we have found that this default order is the best one in 90% of the use cases.

1. Typos
2. Geo-location (if applicable)
3. Proximity
4. Attributes
5. Exact
6. Custom

Let’s understand each one of these criteria by applying them to an example:

[
  {
    "objectID": 1,
    "name": "Jo Blak",
    "company": "Utility Trailer Sales",
    "nbCalls": 4
  },
  {
    "objectID": 2,
    "name": "Jo T. Black",
    "company": "Steritek Inc",
    "nbCalls": 45
  },
  {
    "objectID": 3,
    "name": "Joe Black",
    "company": "Pip Printing",
    "nbCalls": 9
  },
  {
    "objectID": 4,
    "name": "Joe Thompson",
    "company": "Black Birds inc",
    "nbCalls": 9
  },
  {
    "objectID": 5,
    "name": "Deanna Gerbi",
    "company": "Thompson, Joey & Blackburn ltd",
    "nbCalls": 7
  }
]

1. Typos

Are there words that start (that is, are prefixed) with a term typed by the user? And if so, do they match exactly the query?

• 0 points means there are prefixes that exactly match all the terms in the query.
• 1 point means there is a 1-character discrepancy between the matching prefixes and the query terms.
• 2 points means there is a 2-character discrepancy, and so on.

Example: for the query “joe black”, here is how each result would rank for typos only (joey is considered as a typo as only the last word of the query is searched as a prefix):