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느린 쿼리 분석

검색 성능 향상을 위해 MongoDB Search로 $text 쿼리 업데이트하기

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 기능의 장점

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:

예시

인덱스 만들기

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
MongoDB 검색 인덱스
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.

MongoDB Search로 전체 텍스트 쿼리의 유연성 향상

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.

다음 쿼리는 plot 필드에 'poet'이 포함된 가장 최근 영화 5편을 반환하고 제목, 장르, 줄거리 및 개봉 연도를 표시합니다.

Regex Index
MongoDB 검색 인덱스
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,
     }
   }
])

이 두 쿼리 모두 다음과 같은 결과를 반환합니다.

{
 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:

1db.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])

위 쿼리의 결과에는 모든 일치가 발생한 컨텍스트와 각각의 관련성 점수가 모두 포함된 highlights 필드가 포함됩니다. 예를 들어 다음은 $search 결과의 첫 번째 문서에 대한 highlights 필드를 보여줍니다.

{
  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' }
      ]
    }
  ]
}

MongoDB Search를 사용하여 쿼리 성능 향상

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.

다음 쿼리를 실행합니다.

Text Index
MongoDB 검색 인덱스
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
    }
  }
])

이 두 쿼리 모두 다음 세 가지 문서를 반환합니다.

   {
  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.

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