mapReduce

Requirements for the map Function

The map function is responsible for transforming each input document into zero or more documents. It can access the variables defined in the scope parameter, and has the following prototype:

function() {
   ...
   emit(key, value);
}

The map function has the following requirements:

• In the map function, reference the current document as this within the function.

• The map function should not access the database for any reason.

• The map function should be pure, or have no impact outside of the function (i.e. side effects.)

• The map function may optionally call emit(key,value) any number of times to create an output document associating key with value.

The following map function will call emit(key,value) either 0 or 1 times depending on the value of the input document's status field:

function() {
    if (this.status == 'A')
        emit(this.cust_id, 1);
}

The following map function may call emit(key,value) multiple times depending on the number of elements in the input document's items field:

function() {
    this.items.forEach(function(item){ emit(item.sku, 1); });
}

Requirements for the reduce Function

The reduce function has the following prototype:

function(key, values) {
   ...
   return result;
}

The reduce function exhibits the following behaviors:

• The reduce function should not access the database, even to perform read operations.

• The reduce function should not affect the outside system.

• MongoDB can invoke the reduce function more than once for the same key. In this case, the previous output from the reduce function for that key will become one of the input values to the next reduce function invocation for that key.

• The reduce function can access the variables defined in the scope parameter.

• The inputs to reduce must not be larger than half of MongoDB's maximum BSON document size. This requirement may be violated when large documents are returned and then joined together in subsequent reduce steps.

Because it is possible to invoke the reduce function more than once for the same key, the following properties need to be true:

• the type of the return object must be identical to the type of the value emitted by the map function.

• the reduce function must be associative. The following statement must be true:

  reduce(key, [ C, reduce(key, [ A, B ]) ] ) == reduce( key, [ C, A, B ] )

• the reduce function must be idempotent. Ensure that the following statement is true:

  reduce( key, [ reduce(key, valuesArray) ] ) == reduce( key, valuesArray )

• the reduce function should be commutative: that is, the order of the elements in the valuesArray should not affect the output of the reduce function, so that the following statement is true:

  reduce( key, [ A, B ] ) == reduce( key, [ B, A ] )

Requirements for the finalize Function

The finalize function has the following prototype:

function(key, reducedValue) {
   ...
   return modifiedObject;
}

The finalize function receives as its arguments a key value and the reducedValue from the reduce function. Be aware that:

• The finalize function should not access the database for any reason.

• The finalize function should be pure, or have no impact outside of the function (i.e. side effects.)

• The finalize function can access the variables defined in the scope parameter.

out Options

You can specify the following options for the out parameter:

out: <collectionName>

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

out: { inline: 1 }

Return the Total Price Per Customer

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:

1. Define the map function to process each input document:

   • In the function, this refers to the document that the map-reduce operation is processing.

   • 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.

   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

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.

The operation in the example:

1. Groups by the item.sku field, and calculates the number of orders and the total quantity ordered for each sku.

2. Calculates the average quantity per order for each sku value and merges the results into the output collection.

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.

Example steps:

1. Define the map function to process each input document:

   • In the function, this refers to the document that the map-reduce operation is processing.

   • 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:

   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" } }
] )