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Atualize queries $text com o MongoDB Search para melhorar o desempenho da pesquisa

If your queries rely heavily on the $text aggregation pipeline stage, you can modify these queries to use $search instead to improve both the flexibility and performance of these queries.

Vantagens dos recursos de pesquisa do MongoDB

The $search aggregation stage provides the following features which are either not available through the $text operator, available but less performant, or available only with significant implementation work by the user:

Consciência do idioma
Pesquisa sem distinção entre maiúsculas e minúsculas e diacríticos
Result text highlighting
Geospatial-aware queries with geoWithin and geoShape.
Character and word autocompletion with different tokenization strategies
Correspondência difusa
Filtering on 10 or more strings with the compound operator
Customizable relevance scoring and sorting
Índice composto único em arrays
Synonym search
Bucketing for faceted navigation
Analisadores personalizados
Phrase queries

Exemplos

Criar os índices

The examples in the following sections use queries against the sample_mflix.movies collection in the sample data to illustrate the improvements to flexibility and performance that MongoDB Search offers over $text. You can run the queries from both examples using the following indexes:

Text Index

Índice de pesquisa do MongoDB

db.movies.createIndex(
  {
    genres: "text",
    plot: "text",
    year: -1
  }
)

{
  "mappings": {
    "dynamic": false,
    "fields": {
      "genres": {
        "type": "string"
      },
      "plot": {
        "type": "string"
      },
      "year": {
        "type": "number"
      }
    }
  }
}

Either index definition will index the genres and plot fields as text, and the year field as numeric. For instructions on creating $text indexes, see Create Text Index. For instructions on creating MongoDB Search indexes, see Create a MongoDB Search Index.

Melhore a flexibilidade das queries de texto completo com o MongoDB Search

You can update your $text-based queries to use $search for greater flexibility and convenience. In this example, you will query the sample_mflix.movies collection in the sample data to retrieve entries with the word 'poet' in the plot field, sorted in ascending order by year.

The index definitions laid out in the previous section illustrate one of the flexibility enhancements of $search. In order to create the $text index on sample_mflix.movies, you must first delete any existing text index on the sample data using the db.collection.dropIndex() method, as MongoDB supports only a single text index per collection.

In contrast, you can create multiple MongoDB Search indexes for a single collection, allowing your applications to leverage distinct full text queries in parallel.

As query a seguir retornam os cinco filmes mais recentes com "poeta" no campo plot , mostrando seus títulos, gêneros, tramas e anos de lançamento.

Regex Index

Índice de pesquisa do MongoDB

db.movies.find(
   {
     $text: { $search: "poet" }
   },
   {
     _id: 0,
     title: 1,
     genres: 1,
     plot: 1,
     year: 1
   }
).limit(5)

db.movies.aggregate([
   {
     "$search": {
       "text": {
         "path": "plot",
         "query": "poet"
       }
     }
   },
   {
     "$limit": 5
   },
   {
     "$project": {
       "_id": 0,
       "title": 1,
       "genres": 1,
       "plot": 1,
       "year": 1,
     }
   }
])

Ambas as query retornam os seguintes resultados:

{
 plot: `It's the story of the murder of a poet, a man, a great film director: Pier Paolo Pasolini. The story begin with the arrest of "Pelosi", a young man then accused of the murder of the poet. ...`,
 genres: [ 'Crime', 'Drama' ],
 title: 'Who Killed Pasolini?',
 year: 1995
},
{
 plot: 'Friendship and betrayal between two poets during the French Revolution.',
 genres: [ 'Biography', 'Drama' ],
 title: 'Pandaemonium',
 year: 2000
},
{
 year: 2003,
 plot: 'Story of the relationship between the poets Ted Hughes and Sylvia Plath.',
 genres: [ 'Biography', 'Drama', 'Romance' ],
 title: 'Sylvia'
},
{
 year: 2003,
 plot: 'Story of the relationship between the poets Ted Hughes and Sylvia Plath.',
 genres: [ 'Biography', 'Drama', 'Romance' ],
 title: 'Sylvia'
},
{
 plot: 'A love-struck Italian poet is stuck in Iraq at the onset of an American invasion.',
 genres: [ 'Comedy', 'Drama', 'Romance' ],
 title: 'The Tiger and the Snow',
 year: 2005
}

Unique to MongoDB Search, you can add highlights to the results, displaying matches in the contexts in which they were found. To do so, replace the MongoDB Search query above with the following:

1 db.movies.aggregate([
2   {
3     "$search": {
4       "text": {
5         "path": "plot",
6         "query": "poet"
7       },
8       "highlight": {
9         "path": "plot"
10       }
11     }
12   },
13   {
14     "$limit": 1
15   },
16   {
17     "$project": {
18       "_id": 0,
19       "title": 1,
20       "genres": 1,
21       "plot": 1,
22       "year": 1,
23       "highlights": { "$meta": "searchHighlights" }
24     }
25   }
26 ])

Os resultados da query acima incluem o campo highlights , que contém o contexto em que todas as correspondências ocorreram e as pontuações de relevância para cada uma. Por exemplo, o seguinte mostra o campo highlights para o primeiro documento nos resultados do $search .

{
  plot: `It's the story of the murder of a poet, a man, a great film director: Pier Paolo Pasolini. The story begin with the arrest of "Pelosi", a young man then accused of the murder of the poet. ...`,
  genres: [ 'Crime', 'Drama' ],
  title: 'Who Killed Pasolini?',
  year: 1995,
  highlights: [
    {
      score: 1.0902210474014282,
      path: 'plot',
      texts: [
        { value: "It's the story of the murder of a ", type: 'text' },
        { value: 'poet', type: 'hit' },
        {
          value: ', a man, a great film director: Pier Paolo Pasolini. ',
          type: 'text'
        }
      ]
    },
    {
      score: 1.0202842950820923,
      path: 'plot',
      texts: [
        {
          value: 'The story begin with the arrest of "Pelosi", a young man then accused of the murder of the ',
          type: 'text'
        },
        { value: 'poet', type: 'hit' },
        { value: '. ...', type: 'text' }
      ]
    }
  ]
}

Melhorar o desempenho das queries usando o MongoDB Search

In addition to greater flexibility and convenience, MongoDB Search provides significant performance advantages over analogous $text queries. Consider a query against the sample_mflix.movies collection to retrieve movies released between 2000 and 2010, in the comedy genre, with 'poet' in the plot field.

Execute a seguinte query:

Text Index

Índice de pesquisa do MongoDB

db.movies.aggregate([
  {
    $match: {
      year: {$gte: 2000, $lte: 2010},
      $text: { $search: "poet" },
      genres : { $eq: "Comedy" }
    }
  },
  { "$sort": { "year": 1 } },
  {
    "$limit": 3
  },
  {
    "$project": {
      "_id": 0,
      "title": 1,
      "genres": 1,
      "plot": 1,
      "year": 1
    },
  }
])

db.movies.aggregate([
  {
    "$search": {
      "compound": {
        "filter": [{
          "range": {
            "gte": 2000,
            "lte": 2010,
            "path": "year"
          }
        },
        {
          "text": {
            "path": "plot",
            "query": "poet"
          }
        },
        {
          "text": {
            "path": "genres",
            "query": "comedy"
          }
        }]
      }
    }
  },
  { "$sort": { "year": 1 } },
  {
    "$limit": 3
  },
  {
    "$project": {
      "_id": 0,
      "title": 1,
      "genres": 1,
      "plot": 1,
      "year": 1
    }
  }
])

Ambas as queries retornarão os três documentos a seguir.

   {
  year: 2000,
  plot: 'A film poem inspired by the Peruvian poet Cèsar Vallejo. A story about our need for love, our confusion, greatness and smallness and, most of all, our vulnerability. It is a story with many...',
  genres: [ 'Comedy', 'Drama' ],
  title: 'Songs from the Second Floor'
},
{
  plot: 'When his mother, who has sheltered him his entire 40 years, dies, Elling, a sensitive, would-be poet, is sent to live in a state institution. There he meets Kjell Bjarne, a gentle giant and...',
  genres: [ 'Comedy', 'Drama' ],
  title: 'Elling',
  year: 2001
},
{
  plot: 'Heart-broken after several affairs, a woman finds herself torn between a Poet and a TV Host.',
  genres: [ 'Comedy', 'Romance', 'Drama' ],
  title: 'Easy',
  year: 2003
}

Although $text is adequate for simple, narrow searches such as this, as the size of the datasets and breadth of your queries increases, the performance advantages of $search will significantly improve the responsiveness of your applications. We recommend that you use a MongoDB Search query through the $search aggregation pipeline stage.

Saiba mais

To learn more about MongoDB Search queries, see Queries and Indexes.
MongoDB University offers a free course on optimizing MongoDB Performance. To learn more, see Monitoring and Insights.

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Desempenho em Tempo Real

Configurar e resolver alertas

1	db.movies.aggregate([
2	{
3	"$search": {
4	"text": {
5	"path": "plot",
6	"query": "poet"
7	},
8	"highlight": {
9	"path": "plot"
10	}
11	}
12	},
13	{
14	"$limit": 1
15	},
16	{
17	"$project": {
18	"_id": 0,
19	"title": 1,
20	"genres": 1,
21	"plot": 1,
22	"year": 1,
23	"highlights": { "$meta": "searchHighlights" }
24	}
25	}
26	])