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1: Process Data with Analyzers

Simple Analyzer

The simple analyzer divides text into searchable terms (tokens) wherever it finds a non-letter character, such as whitespace, punctuation, or one or more digits. It converts all text to lower case.

You can see the tokens that the simple analyzer creates for a built-in static string in the Atlas UI Visual Editor when you Refine Your Index. The Index Configurations section displays the index and search tokens that the simple analyzer creates if you expand View text analysis of your selected index configuration to help you select the analyzer to use in your index.

Important

Atlas Search won't index string fields where analyzer tokens exceed 32766 bytes in size. If using the keyword analyzer, string fields which exceed 32766 bytes will not be indexed.

Example

The following example index definition specifies an index on the title field in the sample_mflix.movies collection using the simple analyzer. If you loaded the collection on your cluster, you can create the example index using the Atlas UI Visual Editor or the JSON Editor. After you select your preferred configuration method, select the database and collection.

  1. Click Refine Your Index to configure your index.

  2. In the Field Mappings section, click Add Field to open the Add Field Mapping window.

  3. Click Customized Configuration.

  4. Select title from the Field Name dropdown.

  5. Click the Data Type dropdown and select String if it isn't already selected.

  6. Expand String Properties and make the following changes:

    Index Analyzer

    Select lucene.simple from the dropdown.

    Search Analyzer

    Select lucene.simple from the dropdown.

    Index Options

    Use the default offsets.

    Store

    Use the default true.

    Ignore Above

    Keep the default setting.

    Norms

    Use the default include.

  7. Click Add.

  8. Click Save Changes.

  9. Click Create Search Index.

  1. Replace the default index definition with the following index definition.

    {
      "mappings": {
        "fields": {
          "title": {
            "type": "string",
            "analyzer": "lucene.simple"
          }
        }
      }
    }

  2. Click Next.

  3. Click Create Search Index.

The following query searches for the term lion in the title field and limits the output to five results.

1db.movies.aggregate([
2  {
3    "$search": {
4      "text": {
5         "query": "lion",
6         "path": "title"
7      }
8    }
9  },
10  {
11    "$limit": 5
12  },
13  {
14    "$project": {
15      "_id": 0,
16      "title": 1
17    }
18  }
19])
[
  { title: 'White Lion' },
  { title: 'The Lion King' },
  { title: 'The Lion King 1 1/2' },
  { title: 'The Lion King 1 1/2' },
  { title: 'Lion's Den' },
]

Atlas Search returns these documents by doing the following for the text in the title field using the lucene.simple analyzer:

  • Convert text to lowercase.

  • Create separate tokens by dividing text wherever there is a non-letter character.

The following table shows the tokens that Atlas Search creates using the Simple Analyzer and, by contrast, the Standard Analyzer and Whitespace Analyzer for the documents in the results:

Title
Simple Analyzer Tokens
Standard Analyzer Tokens
Whitespace Analyzer Tokens

White Lion

white, lion

white, lion

White, Lion

The Lion King

the, lion, king

the, lion, king

The, Lion, King

The Lion King 1 1/2

the, lion, king

the, lion, king, 1, 1, 2

The, Lion, King, 1, 1/2

Lion's Den

lion, s, den

lion's, den

Lion's, Den

Atlas Search returns document Lion's Den in the results because the simple analyzer creates a separate token for lion, which matches the query term lion. By contrast, if you index the field using the Standard Analyzer or Whitespace Analyzer, Atlas Search would return some of the documents in the results for the query, but not Lion's Den because these analyzers would create the tokens lion's and Lion's respectively, but don't create a token for lion.

Back

Standard

Next

Whitespace