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使用MongoDB Search 更新 $text 查询以提高搜索性能

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.

MongoDB Search 功能优势

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:

语言认知
不区分大小写和不区分变音符号的搜索
Result text highlighting
Geospatial-aware queries with geoWithin and geoShape.
Character and word autocompletion with different tokenization strategies
模糊匹配
Filtering on 10 or more strings with the compound operator
Customizable relevance scoring and sorting
数组上的单个复合索引
Synonym search
Bucketing for faceted navigation
自定义分析器
Phrase queries

示例

创建索引

以下部分中的示例使用针对示例数据中的sample_mflix.movies 集合的查询来说明MongoDB Search 相对于$text 在灵活性和性能方面的改进。您可以使用以下索引运行这两个示例中的查询：

Text Index

MongoDB搜索索引

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.

使用MongoDB Search 提高全文查询的灵活性

您可以更新基于$text的查询以使用$search ，从而提高灵活性和便利性。在此示例中，您将查询示例数据中的sample_mflix.movies集合，以检索plot字段中包含单词“poet”的条目（按年份升序排序）。

上一个部分中列出的索引定义说明了 $search 的一项灵活性增强功能。为了在 sample_mflix.movies 上创建 $text索引，您必须首先使用 db.collection.dropIndex() 方法删除示例数据上的任何现有文本索引，因为MongoDB仅支持每个集合一个文本索引。

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

以下查询返回plot字段中包含“poet”的五部最新电影，显示它们的标题、类型、情节和上映年份。

Regex Index

MongoDB搜索索引

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,
     }
   }
])

这两个查询都返回以下结果：

{
 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 ])

上述查询的结果包括highlights字段，其中包含所有匹配项发生的上下文以及每个匹配项的相关性分数。例如，以下显示了$search结果中第一个文档的highlights字段。

{
  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' }
      ]
    }
  ]
}

使用MongoDB Search 提高查询性能

除了更高的灵活性和便利性之外，与类似的 $text 查询相比， MongoDB Search 还具有显着的性能优势。考虑对 sample_mflix.movies集合进行查询，以检索2000 和 2010 之间上映的喜剧类型电影，plot字段中包含“poet”。

运行以下查询：

Text Index

MongoDB搜索索引

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
    }
  }
])

这两个查询都将返回以下三个文档。

   {
  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.

了解详情

To learn more about MongoDB Search queries, see Queries and Indexes.
MongoDB University 提供有关优化 MongoDB 性能的免费课程。要了解更多信息，请参阅监控和见解。

后退

实时性能

来年

配置和解决警报

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	])