映射化简

返回客户ID的总价¶

Perform map-reduce operation on the orders collection to group by the cust_id, and for each cust_id, 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 pair.

   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 the 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:

计算的订单数量，总数量，平均每个项目的数量¶

In this example you will perform a map-reduce operation on the orders collection, for all documents that have an ord_date value greater than 01/01/2012. The operation groups by the item.sku field, and for each sku calculates the number of orders and the total quantity ordered. The operation concludes by calculating the average quantity per order for each sku value:

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 and value pair.

   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 valuesCountObjects:

   • valuesCountObjects 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 valuesCountObjects array to a single object reducedValue that also contains the count and the qty fields.
   • In reducedValue, 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, valuesCountObjects) {
                             reducedValue = { count: 0, qty: 0 };
   
                             for (var idx = 0; idx < valuesCountObjects.length; idx++) {
                                 reducedValue.count += valuesCountObjects[idx].count;
                                 reducedValue.qty += valuesCountObjects[idx].qty;
                             }
   
                             return reducedValue;
                         };
   

3. Define a finalize function with two arguments key and reducedValue. The function modifies the reducedValue object to add a computed field named average and returns the modified object:

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

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_example" },
                          query: { ord_date: { $gt: new Date('01/01/2012') } },
                          finalize: finalizeFunction2
                        }
                      )
   

   This operation uses the query field to select only those documents with ord_date greater than new Date(01/01/2012). Then it output the results to a collection map_reduce_example. If the map_reduce_example collection already exists, the operation will merge the existing contents with the results of this map-reduce operation:

数据设置¶

The sessions collection contains documents that log users’ session each day, for example:

db.sessions.save( { userid: "a", ts: ISODate('2011-11-03 14:17:00'), length: 95 } );
db.sessions.save( { userid: "b", ts: ISODate('2011-11-03 14:23:00'), length: 110 } );
db.sessions.save( { userid: "c", ts: ISODate('2011-11-03 15:02:00'), length: 120 } );
db.sessions.save( { userid: "d", ts: ISODate('2011-11-03 16:45:00'), length: 45 } );

db.sessions.save( { userid: "a", ts: ISODate('2011-11-04 11:05:00'), length: 105 } );
db.sessions.save( { userid: "b", ts: ISODate('2011-11-04 13:14:00'), length: 120 } );
db.sessions.save( { userid: "c", ts: ISODate('2011-11-04 17:00:00'), length: 130 } );
db.sessions.save( { userid: "d", ts: ISODate('2011-11-04 15:37:00'), length: 65 } );

初始化当前集合的映射化简¶

Run the first map-reduce operation as follows:

1. Define the map function that maps the userid to an object that contains the fields userid, total_time, count, and avg_time:

   var mapFunction = function() {
                         var key = this.userid;
                         var value = {
                                       userid: this.userid,
                                       total_time: this.length,
                                       count: 1,
                                       avg_time: 0
                                      };
   
                         emit( key, value );
                     };
   

2. Define the corresponding reduce function with two arguments key and values to calculate the total time and the count. The key corresponds to the userid, and the values is an array whose elements corresponds to the individual objects mapped to the userid in the mapFunction.

   var reduceFunction = function(key, values) {
   
                           var reducedObject = {
                                                 userid: key,
                                                 total_time: 0,
                                                 count:0,
                                                 avg_time:0
                                               };
   
                           values.forEach( function(value) {
                                                 reducedObject.total_time += value.total_time;
                                                 reducedObject.count += value.count;
                                           }
                                         );
                           return reducedObject;
                        };
   

3. Define finalize function with two arguments key and reducedValue. The function modifies the reducedValue document to add another field average and returns the modified document.

   var finalizeFunction = function (key, reducedValue) {
   
                             if (reducedValue.count > 0)
                                 reducedValue.avg_time = reducedValue.total_time / reducedValue.count;
   
                             return reducedValue;
                          };
   

4. Perform map-reduce on the session collection using the mapFunction, the reduceFunction, and the finalizeFunction functions. Output the results to a collection session_stat. If the session_stat collection already exists, the operation will replace the contents:

   db.sessions.mapReduce( mapFunction,
                          reduceFunction,
                          {
                            out: { reduce: "session_stat" },
                            finalize: finalizeFunction
                          }
                        )
   

后续的增量 Map-Reduce¶

Later as the sessions collection grows, you can run additional map-reduce operations. For example, add new documents to the sessions collection:

db.sessions.save( { userid: "a", ts: ISODate('2011-11-05 14:17:00'), length: 100 } );
db.sessions.save( { userid: "b", ts: ISODate('2011-11-05 14:23:00'), length: 115 } );
db.sessions.save( { userid: "c", ts: ISODate('2011-11-05 15:02:00'), length: 125 } );
db.sessions.save( { userid: "d", ts: ISODate('2011-11-05 16:45:00'), length: 55 } );

At the end of the day, perform incremental map-reduce on the sessions collection but use the query field to select only the new documents. Output the results to the collection session_stat, but reduce the contents with the results of the incremental map-reduce:

db.sessions.mapReduce( mapFunction,
                       reduceFunction,
                       {
                         query: { ts: { $gt: ISODate('2011-11-05 00:00:00') } },
                         out: { reduce: "session_stat" },
                         finalize: finalizeFunction
                       }
                     );

片式输入¶

When using sharded collection as the input for a map-reduce operation, mongos will automatically dispatch the map-reduce job to each shard in parallel. There is no special option required. mongos will wait for jobs on all shards to finish.

片式输出¶

By default the output collection is not sharded. The process is:

• mongos dispatches a map-reduce finish job to the shard that will store the target collection.

• The target shard pulls results from all other shards, and runs a final reduce/finalize operation, and write to the output.

• If using the sharded option to the out parameter, MongoDB shards the output using _id field as the shard key.

  在 2.2 版更改.

• If the output collection does not exist, MongoDB creates and shards the collection on the _id field. If the collection is empty, MongoDB creates chunks using the result of the first stage of the map-reduce operation.

• mongos dispatches, in parallel, a map-reduce finish job to every shard that owns a chunk.

• Each shard will pull the results it owns from all other shards, run a final reduce/finalize, and write to the output collection.

In MongoDB 2.0:

• mongos retrieves the results from each shard, and performs merge sort to order the results, and performs a reduce/finalize as needed. mongos then writes the result to the output collection in sharded mode.
• This model requires only a small amount of memory, even for large datasets.
• Shard chunks are not automatically split during insertion. This requires manual intervention until the chunks are granular and balanced.

映射功能故障排除¶

You can verify the key and value pairs emitted by the map function by writing your own emit function.

Consider a collection orders that contains documents of the following prototype:

{
     _id: ObjectId("50a8240b927d5d8b5891743c"),
     cust_id: "abc123",
     ord_date: new Date("Oct 04, 2012"),
     status: 'A',
     price: 250,
     items: [ { sku: "mmm", qty: 5, price: 2.5 },
              { sku: "nnn", qty: 5, price: 2.5 } ]
}

1. Define the map function that maps the price to the cust_id for each document and emits the cust_id and price pair:

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

2. Define the emit function to print the key and value:

   var emit = function(key, value) {
       print("emit");
       print("key: " + key + "  value: " + tojson(value));
   }
   

3. Invoke the map function with a single document from the orders collection:

   var myDoc = db.orders.findOne( { _id: ObjectId("50a8240b927d5d8b5891743c") } );
   map.apply(myDoc);
   

4. Verify the key and value pair is as you expected.

   emit
   key: abc123 value:250
   

5. Invoke the map function with multiple documents from the orders collection:

   var myCursor = db.orders.find( { cust_id: "abc123" } );
   
   while (myCursor.hasNext()) {
       var doc = myCursor.next();
       print ("document _id= " + tojson(doc._id));
       map.apply(doc);
       print();
   }
   

6. Verify the key and value pairs are as you expected.

化简功能故障排除¶