mapReduce

Return the Total Price Per Customer返回每个客户的总价

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orders集合执行map-reduce操作，以cust_id进行分组，并计算每个cust_id的价格之和：

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

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

2. Define the corresponding reduce function with two arguments keyCustId and valuesPrices:
   • The valuesPrices is an array whose elements are the price values emitted by the map function and grouped by keyCustId.
   • The function reduces the valuesPrice array to the sum of its elements.该函数将valuesPrice数组的值减少为其元素的总和。

   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:

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

   This operation outputs the results to a collection named map_reduce_example. If the map_reduce_example collection already exists, the operation will replace the contents with the results of this map-reduce operation.

4. Query the map_reduce_example collection to verify the results:

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

   The operation returns these documents:

   { "_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.在以下示例中，您将看到orders集合上的映射减少操作，该操作适用于ord_date值大于或等于2020-03-01的所有文档。

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的订单数量和订购总量。
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，则该操作将覆盖现有文档。如果没有具有相同键的现有文档，则操作将插入该文档。

Example steps:示例步骤：

1. Define the map function to process each input document:定义映射函数以处理每个输入文档：
   • In the function, this refers to the document that the map-reduce operation is processing.在函数中，this指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.

    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:
   • countObjVals is an array whose elements are the objects mapped to the grouped keySKU values passed by map function to the reducer function.
   • The function reduces the countObjVals array to a single object reducedValue that contains the count and the qty fields.
   • 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.

   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:

   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:

   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.

   If the map_reduce_example2 collection already exists, the operation will merge the existing contents with the results of this map-reduce operation. 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.

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:

   { "_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" } }
] )