从2.4版开始，您可以在字段上创建一个文本索引来进行搜索，并使用$text操作符进行查询。

首先，创建索引:

db.users。createIndex({"username": "text"})

然后，搜索:

db.users。查找({$text: {$search: "son"}})

基准测试(~150K文档):

Regex(其他答案)=> 5.6-6.9秒 文本搜索=> .164-。201秒

注:

A collection can have only one text index. You can use a wildcard text index if you want to search any string field, like this: db.collection.createIndex( { "$**": "text" } ). A text index can be large. It contains one index entry for each unique post-stemmed word in each indexed field for each document inserted. A text index will take longer to build than a normal index. A text index does not store phrases or information about the proximity of words in the documents. As a result, phrase queries will run much more effectively when the entire collection fits in RAM.

由于Mongo shell支持正则表达式，这是完全可能的。

db.users.findOne({"username" : /.*son.*/});

如果我们想让查询不区分大小写，我们可以使用"i"选项，如下所示:

db.users.findOne({"username" : /.*son.*/i});

参见:http://www.mongodb.org/display/DOCS/Advanced + # AdvancedQueries-RegularExpressions查询

https://docs.mongodb.com/manual/reference/sql-comparison/

http://php.net/manual/en/mongo.sqltomongo.php

MySQL

SELECT * FROM users WHERE username LIKE "%Son%"

MongoDB

db.users.find({username:/Son/})

您可以使用以下代码来实现。

db.users.findOne({"username" : {$regex : "son"}});

下面是通过Python连接MongoDB时必须做的事情

db.users.find({"username": {'$regex' : '.*' + 'Son' + '.*'}})

你也可以使用一个变量名来代替'Son'，因此字符串连接。

从2.4版开始，您可以在字段上创建一个文本索引来进行搜索，并使用$text操作符进行查询。

首先，创建索引:

db.users。createIndex({"username": "text"})

然后，搜索:

db.users。查找({$text: {$search: "son"}})

基准测试(~150K文档):

Regex(其他答案)=> 5.6-6.9秒 文本搜索=> .164-。201秒

注:

A collection can have only one text index. You can use a wildcard text index if you want to search any string field, like this: db.collection.createIndex( { "$**": "text" } ). A text index can be large. It contains one index entry for each unique post-stemmed word in each indexed field for each document inserted. A text index will take longer to build than a normal index. A text index does not store phrases or information about the proximity of words in the documents. As a result, phrase queries will run much more effectively when the entire collection fits in RAM.