用于查询优化的索引

// List the indexes on the collection and output them as an arrayconst result = await collection.listIndexes().toArray();
// Print the list of indexesconsole.log("Existing indexes:\n");for(const doc in result){    console.log(doc);}

const database = client.db("sample_mflix");const movies = database.collection("movies");
// Create an ascending index on the "title" field in the// "movies" collection.const result = await movies.createIndex({ title: 1 });console.log(`Index created: ${result}`);

// Define the query parametersconst query = { title: "Batman" }const sort = { title: 1 };const projection = { _id: 0, title: 1 };// Execute the query using the defined parametersconst cursor = movies  .find(query)  .sort(sort)  .project(projection);
for await (const doc of cursor) {  console.log(doc);}

// Connect to the "sample_mflix" databaseconst database = client.db("sample_mflix");// Access the database's "movies" collectionconst movies = database.collection("movies");
// Create an ascending index on the "type" and "genre" fields// in the "movies" collection.const result = await movies.createIndex({ type: 1, genre: 1 });console.log(`Index created: ${result}`);

// Define a query to find movies in the "Drama" genreconst query = { type: "movie", genre: "Drama" };// Define sorting criteria for the query resultsconst sort = { type: 1, genre: 1 };// Include only the type and genre fields in the query resultsconst projection = { _id: 0, type: 1, genre: 1 };
// Execute the query using the defined criteria and projectionconst cursor = movies  .find(query)  .sort(sort)  .project(projection);
for await (const doc of cursor) {  console.log(doc);}

const database = client.db("sample_mflix");const movies = database.collection("movies");
// Create a multikey index on the "cast" field in the "movies" collectionconst result = await movies.createIndex({ cast: 1 });

const query = { cast: "Viola Davis" };const projection = { _id: 0, cast: 1 , title: 1 };
// Perform a find operation with the preceding filter and projectionconst cursor = movies  .find(query)  .project(projection);
for await (const doc of cursor) {  console.log(doc);}

const db = client.db('tea');await db.createCollection('ratings', {  clusteredIndex: {    key: { _id: 1 },    unique: true  }});

// Get the database and collection on which to create the index const myDB = client.db("testDB");const myColl = myDB.collection("blogPosts");
// Create a text index on the "title" and "body" fieldsconst result = await myColl.createIndex(  { title: "text", body: "text" },  {     default_language: "english",     weights: { body: 10, title: 3 }  });

// Query for documents where body or title contain "life ahead"const query = { $text: { $search: "life ahead" } };
// Show only the title fieldconst projection = { _id: 0, title: 1 };
// Execute the find operationconst cursor = myColl.find(query).project(projection);
for await (const doc of cursor) {  console.log(doc);}

{   "_id" : ObjectId("59a47286cfa9a3a73e51e75c"),   "theaterId" : 104,   "location" : {      "address" : {         "street1" : "5000 W 147th St",         "city" : "Hawthorne",         "state" : "CA",         "zipcode" : "90250"      },      "geo" : {         "type" : "Point",         "coordinates" : [            -118.36559,            33.897167         ]      }   }}

const database = client.db("sample_mflix");const movies = database.collection("movies");
/* Create a 2dsphere index on the "location.geo" field in the"movies" collection */const result = await movies.createIndex({ "location.geo": "2dsphere" });
// Print the result of the index creationconsole.log(`Index created: ${result}`);

const database = client.db("sample_mflix");const movies = database.collection("movies");
// Create a unique index on the "theaterId" field in the "theaters" collection.const result = await movies.createIndex({ theaterId: 1 }, { unique: true });console.log(`Index created: ${result}`);

E11000 duplicate key error index

// Creates a MongoDB Search indexconst index1 = {    name: "search1",    definition: {        "mappings": {            "dynamic": true        }    }}await collection.createSearchIndex(index1);

// Creates a MongoDB Vector Search indexconst vectorSearchIdx = {    name: "vsidx1",    type: "vectorSearch",    definition: {        fields: [{            type: "vector",            numDimensions: 384,            path: "summary",            similarity: "dotProduct"        }]    }}
await collection.createSearchIndex(vectorSearchIdx);

// Lists search indexesconst result = await collection.listSearchIndexes().toArray();console.log("Existing search indexes:\n");for (const doc in result) {    console.log(doc);}

// Updates a search indexconst index2 = {    "mappings": {        "dynamic": true,        "fields": {            "description": {                "type": "string"            }        }    }}await collection.updateSearchIndex("search1", index2);

// Drops (deletes) a search indexawait collection.dropSearchIndex("search1");