mapReduce (database command)（数据库命令）

Output to a Collection输出到集合

This option outputs to a new collection, and is not available on secondary members of replica sets.此选项输出到新集合，在副本集的辅助成员上不可用。

out: <collectionName>

Output to a Collection with an Action通过操作输出到集合

This option is only available when passing a collection that already exists to out. It is not available on secondary members of replica sets.此选项仅在将已存在的集合传递到out时可用。它在副本集的辅助成员上不可用。

out: { <action>: <collectionName>
        [, db: <dbName>]
        [, sharded: <boolean> ] }

When you output to a collection with an action, the out has the following parameters:当您通过操作输出到集合时，out具有以下参数：

• <action>: Specify one of the following actions:：指定以下操作之一：

  • replace

    Replace the contents of the <collectionName> if the collection with the <collectionName> exists.如果存在具有<collectionName>的集合，则替换<collectionName>的内容。

  • merge

    Merge the new result with the existing result if the output collection already exists. If an existing document has the same key as the new result, overwrite that existing document.如果输出集合已存在，则将新结果与现有结果合并。如果现有文档与新结果具有相同的键，请覆盖该现有文档。

  • reduce

    Merge the new result with the existing result if the output collection already exists. If an existing document has the same key as the new result, apply the reduce function to both the new and the existing documents and overwrite the existing document with the result.如果输出集合已存在，则将新结果与现有结果合并。如果现有文档与新结果具有相同的键，则对新文档和现有文档都应用reduce函数，并用结果覆盖现有文档。

• db:

  Optional. 可选。The name of the database that you want the map-reduce operation to write its output. By default this will be the same database as the input collection.您希望映射缩减操作写入其输出的数据库的名称。默认情况下，这将是与输入集合相同的数据库。

Output Inline输出内联

Perform the map-reduce operation in memory and return the result. This option is the only available option for out on secondary members of replica sets.在内存中执行map-reduce操作并返回结果。此选项是副本集次要成员上out的唯一可用选项。

out: { inline: 1 }

The result must fit within the maximum size of a BSON document.结果必须符合BSON文档的最大大小。

Return the Total Price Per Customer返回每位客户的总价

Perform the map-reduce operation on the orders collection to group by the cust_id, and calculate the sum of the price for each cust_id:对按cust_id分组的orders集合执行map-reduce操作，并计算每个cust_id的价格之和：

1. Define the map function to process each input document:定义映射函数来处理每个输入文档：

   • In the function, this refers to the document that the map-reduce operation is processing.在函数中，this引用map-reduce操作正在处理的文档。
   • The function maps the price to the cust_id for each document and emits the cust_id and price.该函数将price映射到每个文档的cust_id，并发出cust_id和price。

   var mapFunction1 = function() {
      emit(this.cust_id, this.price);
   };

2. Define the corresponding reduce function with two arguments keyCustId and valuesPrices:使用两个参数keyCustId和valuesPrices定义相应的reduce函数：

   • The valuesPrices is an array whose elements are the price values emitted by the map function and grouped by keyCustId.valuesPrices是一个数组，其元素是map函数发出的price值，并按keyCustId分组。
   • The function reduces the valuesPrice array to the sum of its elements.该函数将valuesPrice数组缩减为其元素的总和。

   var reduceFunction1 = function(keyCustId, valuesPrices) {
      return Array.sum(valuesPrices);
   };

3. Perform map-reduce on all documents in the orders collection using the mapFunction1 map function and the reduceFunction1 reduce function:使用mapFunction1映射函数和reduceFunction1缩减函数对orders集合中的所有文档执行map-reduce：

   db.orders.mapReduce(
      mapFunction1,
      reduceFunction1,
      { out: "map_reduce_example" }
   )

   This operation outputs the results to a collection named map_reduce_example. 此操作将结果输出到名为map_reduce_example的集合中。If the map_reduce_example collection already exists, the operation will replace the contents with the results of this map-reduce operation.如果map_reduce_example集合已存在，则该操作将用此map-reduce操作的结果替换内容。

4. Query the map_reduce_example collection to verify the results:查询map_reduce_example集合以验证结果：

   db.map_reduce_example.find().sort( { _id: 1 } )

   The operation returns these documents:该操作返回以下文档：

   { "_id" : "Ant O. Knee", "value" : 95 }
   { "_id" : "Busby Bee", "value" : 125 }
   { "_id" : "Cam Elot", "value" : 60 }
   { "_id" : "Don Quis", "value" : 155 }

db.orders.aggregate([
   { $group: { _id: "$cust_id", value: { $sum: "$price" } } },
   { $out: "agg_alternative_1" }
])

Calculate Order and Total Quantity with Average Quantity Per Item使用每个项目的平均数量计算订单和总数量

In the following example, you will see a map-reduce operation on the orders collection for all documents that have an ord_date value greater than or equal to 2020-03-01.在下面的示例中，您将看到对所有ord_date值大于或等于2020-03-01的文档的orders集合执行map-reduce操作。

The operation in the example:示例中的操作：

1. Groups by the item.sku field, and calculates the number of orders and the total quantity ordered for each sku.按item.sku字段分组，并计算每个sku的订单数和订购总量。
2. Calculates the average quantity per order for each sku value and merges the results into the output collection.计算每个sku值的每个订单的平均数量，并将结果合并到输出集合中。

When merging results, if an existing document has the same key as the new result, the operation overwrites the existing document. If there is no existing document with the same key, the operation inserts the document.合并结果时，如果现有文档与新结果具有相同的键，则该操作会覆盖现有文档。如果没有具有相同键的现有文档，则操作将插入文档。

Example steps:示例步骤：

1. Define the map function to process each input document:定义映射函数来处理每个输入文档：

   • In the function, this refers to the document that the map-reduce operation is processing.在函数中，this引用map-reduce操作正在处理的文档。
   • For each item, the function associates the sku with a new object value that contains the count of 1 and the item qty for the order and emits the sku (stored in the key) and the value.对于每个项目，该函数将sku与一个新的对象value相关联，该对象值包含订单的count为1和项目qty，并发出sku（存储在key中）和value。

    var mapFunction2 = function() {
       for (var idx = 0; idx < this.items.length; idx++) {
          var key = this.items[idx].sku;
          var value = { count: 1, qty: this.items[idx].qty };
   
          emit(key, value);
       }
   };

2. Define the corresponding reduce function with two arguments keySKU and countObjVals:使用两个参数keySKU和countObjVals定义相应的reduce函数：

   • countObjVals is an array whose elements are the objects mapped to the grouped keySKU values passed by map function to the reducer function.是一个数组，其元素是映射到map函数传递给reducer函数的分组keySKU值的对象。
   • The function reduces the countObjVals array to a single object reducedValue that contains the count and the qty fields.该函数将countObjVals数组缩减为一个包含count和qty字段的单个对象reducedValue。
   • In reducedVal, the count field contains the sum of the count fields from the individual array elements, and the qty field contains the sum of the qty fields from the individual array elements.在reducedVal中，count字段包含来自各个数组元素的count字段的总和，qty字段包含来自单个数组元素的qty字段的总和。

   var reduceFunction2 = function(keySKU, countObjVals) {
      reducedVal = { count: 0, qty: 0 };
   
      for (var idx = 0; idx < countObjVals.length; idx++) {
          reducedVal.count += countObjVals[idx].count;
          reducedVal.qty += countObjVals[idx].qty;
      }
   
      return reducedVal;
   };

3. Define a finalize function with two arguments key and reducedVal. The function modifies the reducedVal object to add a computed field named avg and returns the modified object:定义一个带有两个参数key和reducedVal的finalize函数。该函数修改reducedVal对象以添加一个名为avg的计算字段，并返回修改后的对象：

   var finalizeFunction2 = function (key, reducedVal) {
     reducedVal.avg = reducedVal.qty/reducedVal.count;
     return reducedVal;
   };

4. Perform the map-reduce operation on the orders collection using the mapFunction2, reduceFunction2, and finalizeFunction2 functions:使用mapFunction2、reduceFunction2和finalizeFunction2函数对orders集合执行map-reduce操作：

   db.orders.mapReduce(
      mapFunction2,
      reduceFunction2,
      {
        out: { merge: "map_reduce_example2" },
        query: { ord_date: { $gte: new Date("2020-03-01") } },
        finalize: finalizeFunction2
      }
    );

   This operation uses the query field to select only those documents with ord_date greater than or equal to new Date("2020-03-01"). Then it outputs the results to a collection map_reduce_example2.此操作使用query字段仅选择ord_date大于或等于new Date("2020-03-01")的文档。然后，它将结果输出到集合map_reduce_example2。

   If the map_reduce_example2 collection already exists, the operation will merge the existing contents with the results of this map-reduce operation. 如果map_reduce_example2集合已存在，则该操作将把现有内容与此map-reduce操作的结果合并。That is, if an existing document has the same key as the new result, the operation overwrites the existing document. If there is no existing document with the same key, the operation inserts the document.也就是说，如果现有文档与新结果具有相同的键，则操作会覆盖现有文档。如果没有具有相同键的现有文档，则操作将插入文档。

5. Query the map_reduce_example2 collection to verify the results:查询map_reduce_example2集合以验证结果：

   db.map_reduce_example2.find().sort( { _id: 1 } )

   The operation returns these documents:该操作返回以下文档：

   { "_id" : "apples", "value" : { "count" : 4, "qty" : 35, "avg" : 8.75 } }
   { "_id" : "carrots", "value" : { "count" : 2, "qty" : 15, "avg" : 7.5 } }
   { "_id" : "chocolates", "value" : { "count" : 3, "qty" : 15, "avg" : 5 } }
   { "_id" : "oranges", "value" : { "count" : 7, "qty" : 63, "avg" : 9 } }
   { "_id" : "pears", "value" : { "count" : 1, "qty" : 10, "avg" : 10 } }

db.orders.aggregate( [
   { $match: { ord_date: { $gte: new Date("2020-03-01") } } },
   { $unwind: "$items" },
   { $group: { _id: "$items.sku", qty: { $sum: "$items.qty" }, orders_ids: { $addToSet: "$_id" } }  },
   { $project: { value: { count: { $size: "$orders_ids" }, qty: "$qty", avg: { $divide: [ "$qty", { $size: "$orders_ids" } ] } } } },
   { $merge: { into: "agg_alternative_3", on: "_id", whenMatched: "replace",  whenNotMatched: "insert" } }
] )