Database Manual / Data Modeling / Schema Design Anti-Patterns

Reduce the Number of Collections减少集合数量

Creating too many collections can decrease performance. With every collection, MongoDB creates a default _id index, which uses additional storage. 创建太多集合会降低性能。对于每个集合，MongoDB都会创建一个默认的_id索引，该索引使用额外的存储空间。If you create excessive collections, those collections and indexes can strain replica set resources and decrease performance.如果创建了过多的集合，这些集合和索引可能会使副本集资源紧张并降低性能。

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Consider the following schema that stores daily temperature readings that are taken every hour. The temperature database stores each day's readings in separate collections.考虑以下存储每小时读取的每日温度读数的模式。温度数据库将每天的读数存储在单独的集合中。

// Temperatures for May 10, 2024
{
   _id: 1,
   timestamp: "2024-05-10T010:00:00Z",
   temperature: 60
},
{
   _id: 2
   timestamp: "2024-05-10T011:00:00Z",
   temperature: 61
},
{
   _id: 3
   timestamp: "2024-05-10T012:00:00Z",
   temperature: 64
}
...
// Temperatures for May 11, 2024
{
   _id: 1,
   timestamp: "2024-05-11T010:00:00Z",
   temperature: 68
},
{
   _id: 2
   timestamp: "2024-05-11T011:00:00Z",
   temperature: 72
},
{
   _id: 3
   timestamp: "2024-05-11T012:00:00Z",
   temperature: 72
}
...

With an unbound number of collections, the number of default _id indexes can grow to degrade performance.如果集合数量不受限制，默认_id索引的数量可能会增加，从而降低性能。

Additionally, this approach requires a $lookup operation to query across multiple collections. $lookup operations add query complexity and can strain resources.此外，这种方法需要一个$lookup操作来跨多个集合进行查询。$lookup操作增加了查询的复杂性，并可能使资源紧张。

To reduce the number of collections, drop or archive unused collections, or remodel your data schema by consolidating related collections, denormalizing data, or leveraging embedded documents where appropriate.为了减少集合的数量，请删除或存档未使用的集合，或通过整合相关集合、非规范化数据或在适当的情况下利用嵌入式文档来重塑数据模式。

Example示例

You can modify the schema to store each day's temperature readings in a single collection. For example:您可以修改模式，将每天的温度读数存储在一个集合中。例如：

db.dailyTemperatures.insertMany( [
   {
      _id: ISODate("2024-05-10T00:00:00Z"),
      readings: [
         {
            timestamp: "2024-05-10T10:00:00Z",
            temperature: 60
         },
         {
            timestamp: "2024-05-10T11:00:00Z",
            temperature: 61
         },
         {
            timestamp: "2024-05-10T12:00:00Z",
            temperature: 64
         }
      ]
   },
   {
      _id: ISODate("2024-05-11T00:00:00Z"),
      readings: [
         {
            timestamp: "2024-05-11T10:00:00Z",
            temperature: 68
         },
         {
            timestamp: "2024-05-11T11:00:00Z",
            temperature: 72
         },
         {
            timestamp: "2024-05-11T12:00:00Z",
            temperature: 72
         }
      ]
   }
] )

The updated schema requires fewer resources than the original. Instead of needing a separate index for each day, the default _id index now facilitates queries by date.更新的架构所需的资源比原始架构少。默认的_id索引现在方便了按日期查询，而不需要为每天单独设置索引。

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