On-Demand Materialized Views按需物化视图

Indexes索引

Standard views use the indexes of the underlying collection. As a result, you cannot create, drop or re-build general indexes on a standard view directly, nor get a list of general indexes on the view.标准视图使用基础集合的索引。因此，您无法直接在标准视图上创建、删除或重新构建常规索引，也无法在视图上获取常规索引列表。

MongoDB stores search indexes and vector search indexes on disk. Accordingly, you can create MongoDB Search indexes and Vector Search indexes on compatible views that contain only the following stages:MongoDB在磁盘上存储搜索索引和矢量搜索索引。因此，您可以在仅包含以下阶段的兼容视图上创建MongoDB搜索索引和矢量搜索索引：

• $addFields
• $set
• $match wrapping an 包装了$expr operation操作

You can also create indexes directly on on-demand materialized views because MongoDB stores those indexes on disk.您还可以直接在按需物化视图上创建索引，因为MongoDB将这些索引存储在磁盘上。

Performance性能

On-demand materialized views provide better read performance than standard views because they are read from disk instead of computed as part of the query. This performance benefit increases based on the complexity of the pipeline and size of the data being aggregated.按需物化视图提供了比标准视图更好的读取性能，因为它们是从磁盘读取的，而不是作为查询的一部分计算的。这种性能优势会随着管道的复杂性和聚合数据的大小而增加。

1. Define the On-Demand Materialized View定义按需物化视图

The following updateMonthlySales function defines a monthlybakesales materialized view that contains the cumulative monthly sales information. In the example, the function takes a date parameter to only update monthly sales information starting from a particular date.以下updateMonthlySales函数定义了一个包含累积月度销售信息的monthlybakesales物化视图。在该示例中，该函数采用日期参数，仅更新从特定日期开始的每月销售信息。

updateMonthlySales = function(startDate) {
   db.bakesales.aggregate( [
      { $match: { date: { $gte: startDate } } },
      { $group: { _id: { $dateToString: { format: "%Y-%m", date: "$date" } }, sales_quantity: { $sum: "$quantity"}, sales_amount: { $sum: "$amount" } } },
      { $merge: { into: "monthlybakesales", whenMatched: "replace" } }
   ] );
};

• The $match stage filters the data to process only those sales greater than or equal to the startDate.$match阶段筛选数据，仅处理大于或等于startDate的销售。
• The $group stage groups the sales information by the year-month. The documents output by this stage have the form:$group阶段按年月对销售信息进行分组。此阶段输出的文档具有以下形式：

  { "_id" : "<YYYY-mm>", "sales_quantity" : <num>, "sales_amount" : <NumberDecimal> }

• The $merge stage writes the output to the monthlybakesales collection.$merge阶段将输出写入monthlybakesales集合。

  Based on the _id field (the default for unsharded output collections), the stage checks if the document in the aggregation results matches an existing document in the collection:根据_id字段（未记录输出集合的默认值），该阶段检查聚合结果中的文档是否与集合中的现有文档匹配：

  • When there is a match (i.e. a document with the same year-month already exists in the collection), the stage replaces the existing document with the document from the aggregation results.当存在匹配时（即集合中已存在具有相同年月的文档），该阶段将用聚合结果中的文档替换现有文档。
  • When there is not a match, the stage inserts the document from the aggregation results into the collection (the default behavior when not matched).当不匹配时，该阶段会将聚合结果中的文档插入到集合中（不匹配时的默认行为）。

2. Perform Initial Run执行初始运行

For the initial run, you can pass in a date of new ISODate("1970-01-01"):对于首次运行，您可以传入日期new ISODate("1970-01-01")：

updateMonthlySales(new ISODate("1970-01-01"));

After the initial run, the monthlybakesales contains the following documents; i.e. db.monthlybakesales.find().sort( { _id: 1 } ) returns the following:初次运行后，monthlybakesales（月度烘焙产品）包含以下文件：；即db.monthlybakesales.find().sort( { _id: 1 } )返回以下结果：

{ "_id" : "2018-12", "sales_quantity" : 41, "sales_amount" : Decimal128("506") }
{ "_id" : "2019-01", "sales_quantity" : 86, "sales_amount" : Decimal128("896") }

3. Refresh Materialized View刷新物化视图

Assume that by the first week in February 2019, the bakesales collection is updated with newer sales information; specifically, additional January and February sales.假设到2019年2月的第一周，bakesales集合已更新为新的销售信息；特别是1月和2月的额外销售。

db.bakesales.insertMany( [
   { date: ISODate("2019-01-28"), item: "Cake - Chocolate", quantity: 3, amount: Decimal128("90") },
   { date: ISODate("2019-01-28"), item: "Cake - Peanut Butter", quantity: 2, amount: Decimal128("32") },
   { date: ISODate("2019-01-30"), item: "Cake - Red Velvet", quantity: 1, amount: Decimal128("20") },
   { date: ISODate("2019-01-30"), item: "Cookies - Chocolate Chip", quantity: 6, amount: Decimal128("24") },
   { date: ISODate("2019-01-31"), item: "Pie - Key Lime", quantity: 2, amount: Decimal128("40") },
   { date: ISODate("2019-01-31"), item: "Pie - Banana Cream", quantity: 2, amount: Decimal128("40") },
   { date: ISODate("2019-02-01"), item: "Cake - Red Velvet", quantity: 5, amount: Decimal128("100") },
   { date: ISODate("2019-02-01"), item: "Tarts - Apple", quantity: 2, amount: Decimal128("8") },
   { date: ISODate("2019-02-02"), item: "Cake - Chocolate", quantity: 2, amount: Decimal128("60") },
   { date: ISODate("2019-02-02"), item: "Cake - Peanut Butter", quantity: 1, amount: Decimal128("16") },
   { date: ISODate("2019-02-03"), item: "Cake - Red Velvet", quantity: 5, amount: Decimal128("100") }
] )

To refresh the monthlybakesales data for January and February, run the function again to rerun the aggregation pipeline, starting with new ISODate("2019-01-01").要刷新1月和2月的monthlybakesales（月度烘焙销售）数据，请再次运行该函数以重新运行聚合管道，从new ISODate("2019-01-01")开始。

updateMonthlySales(new ISODate("2019-01-01"));

The content of monthlybakesales has been updated to reflect the most recent data in the bakesales collection; i.e. db.monthlybakesales.find().sort( { _id: 1 } ) returns the following:monthlybakesales的内容已经更新，以反映bakesales集合中的最新数据；即db.monthlybakesales.find().sort( { _id: 1 } )返回以下结果：

{ "_id" : "2018-12", "sales_quantity" : 41, "sales_amount" : Decimal128("506") }
{ "_id" : "2019-01", "sales_quantity" : 102, "sales_amount" : Decimal128("1142") }
{ "_id" : "2019-02", "sales_quantity" : 15, "sales_amount" : Decimal128("284") }