On-Demand Materialized Views按需物化视图

Indexes索引

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

You can create indexes directly on on-demand materialized views because they are stored on disk.您可以直接在按需具体化视图上创建索引，因为它们存储在磁盘上。

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. 随需应变物化视图比标准视图提供更好的读取性能，因为它们是从磁盘读取的，而不是作为查询的一部分进行计算。This performance benefit increases based on the complexity of the pipeline and size of the data being aggregated.这种性能优势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. 下面的updateMonthlySales函数定义了一个包含累计月度销售信息的monthlybakesales物化视图。In the example, the function takes a date parameter to only update monthly sales information starting from a particular date.在本例中，该函数使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阶段筛选数据以仅处理那些大于或等于startDate的销售额。
• The $group stage groups the sales information by the year-month. $group阶段按年度月份对销售信息进行分组。The documents output by this stage have the form: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:基于_id字段（未排序输出集合的默认值），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.如果存在匹配项（即集合中已存在具有相同年月的文档），则阶段将使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，阶段将聚合结果中的文档插入集合（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"):对于初次运行，您可以输入new ISODate("1970-01-01")的日期：

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包含以下文档；db.monthlybakesales.find().sort( { _id: 1 } )返回以下内容：

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

3. Refresh Materialized View刷新实体化视图

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集合将更新为更新的销售信息；特别是1月和2月的额外销售额。

db.bakesales.insertMany( [
   { date: new ISODate("2019-01-28"), item: "Cake - Chocolate", quantity: 3, amount: new NumberDecimal("90") },
   { date: new ISODate("2019-01-28"), item: "Cake - Peanut Butter", quantity: 2, amount: new NumberDecimal("32") },
   { date: new ISODate("2019-01-30"), item: "Cake - Red Velvet", quantity: 1, amount: new NumberDecimal("20") },
   { date: new ISODate("2019-01-30"), item: "Cookies - Chocolate Chip", quantity: 6, amount: new NumberDecimal("24") },
   { date: new ISODate("2019-01-31"), item: "Pie - Key Lime", quantity: 2, amount: new NumberDecimal("40") },
   { date: new ISODate("2019-01-31"), item: "Pie - Banana Cream", quantity: 2, amount: new NumberDecimal("40") },
   { date: new ISODate("2019-02-01"), item: "Cake - Red Velvet", quantity: 5, amount: new NumberDecimal("100") },
   { date: new ISODate("2019-02-01"), item: "Tarts - Apple", quantity: 2, amount: new NumberDecimal("8") },
   { date: new ISODate("2019-02-02"), item: "Cake - Chocolate", quantity: 2, amount: new NumberDecimal("60") },
   { date: new ISODate("2019-02-02"), item: "Cake - Peanut Butter", quantity: 1, amount: new NumberDecimal("16") },
   { date: new ISODate("2019-02-03"), item: "Cake - Red Velvet", quantity: 5, amount: new NumberDecimal("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的内容已更新，以反映bakesales集合中的最新数据；db.monthlybakesales.find().sort( { _id: 1 } )返回以下内容：

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