reduce function

The map () and reduce () functions are built in Python. If you've read Google's famous paper, "Mapreduce:simplified Data processing on Large clusters," you can probably understand the concept of map/reduce. Let's look at the map first. The map ()

Google's three core technologies (ii) Google mapreduce Chinese version Google mapreduce Chinese version Translator: Alex Summary MapReduce is a programming model and a related implementation of an algorithmic model for processing and generating very

Test data is inserted into the group of MongoDB: for (vari1; i20; I ++) {varnumi % 6; db. test. insert ({_ id: I, name: user _ + I, age: num});} 1. queries the database by common groups. test. group ({key: {age: true}, initial: {num: 0}, $ reduce:

reduce()Method receives a function as an accumulator (accumulator), and each value (from left to right) in the array begins to merge and eventually a value.concept: invokes the specified callback function for all elements in the array. The return

The aggregation made by group is somewhat complex. Select the key on which the group is based, and then mongodb the collection according to the selected key values to several groups. You can then produce a result document by aggregating the

1. MongoDB MapReduce is equivalent to Mysql's groupby, so it is easy to use MapReduce for parallel statistics on MongoDB. MapReduce is used to implement two functions: Map function and Reduce function. Map function calls emit (key, value), traverses

The small partners who have played Hadoop should be no stranger to MapReduce, MapReduce is powerful and flexible, it can divide a big problem into a number of small problems, the small problems sent to different machines to process, all the machines

Data Flow (Unified: Job translated into jobs, task translated into tasks) First, say some terminology. A mapreduce job is a unit of work that the client executes, including: input data, a MapReduce program, and configuration information. Hadoop

Insert test Data First: for (var i=1; iIf you have _id this, MongoDB will not generate _ID, will use the _id you provide. 1. General packet Query Db.test.group ({key:{age:true},initial:{num:0}, $reduce: function (Doc,prev) {prev.num++}});[ {

This article mainly introduces the implementation method and example of the group grouping in MongoDB, it is very simple and practical, the need of small partners can be consulted.Group aggregation is somewhat complex. The key that the group is

Date Grouping Db.msds_accessrecord.group ({keyf:function (doc) {var date = new Date (doc.addtime);  var DateKey = "" +date.getfullyear () + "-" + (Date.getmonth () +1) + "-" +date.getdate (); return {' Day ':d Atekey};  Initial: {"Count": 0}, Reduce:

MongDB MapReduce is equivalent to MySQL's groupby, so it is easy to use MapReduce for parallel statistics on MongoDB. MapReduce implements two functions: Map function and Reduce function. Map function calls emit (key, value), traverses all records

Recently looking at Google's classic MapReduce paper, the Chinese version can refer to the Meng Yan recommended mapreduce Chinese version of the Chinese translation As mentioned in the paper, the MapReduce programming model is: The

Transferred from: http://blog.csdn.net/opennaive/article/details/7514146Legends of the rivers and lakes: Google technology has "three treasures", GFS, MapReduce and Big Table (BigTable)!Google has published three influential articles in the past 03-0

First, IntroductionMongoDB's aggregation framework, which is used primarily to transform and combine the documents in a collection to make use of the data for analysis.The basic idea of the aggregation framework is:Multiple artifacts are used to

Legends of the rivers and lakes: Google technology has "three treasures", GFS, MapReduce and Big Table (BigTable)!Google has published three influential articles in the past 03-06 years, namely the gfs,04 of the 03 Sosp osdi, and 06 Osdi bigtable.

Legends of the rivers and lakes: Google technology has "three treasures", GFS, MapReduce and Big Table (BigTable)!Google has published three influential articles in the past 03-06 years, namely the gfs,04 of the 03 Sosp osdi, and 06 Osdi bigtable.

about MongoDB's MapReduceCategory: MongoDB2012-12-06 21:378676 People read Comments (2) favorite reports MongoDB Mapreducemapreduce is a computational model that simply executes a large amount of work (data) decomposition (MAP) and then merges

As with databases, group is often used for statistics. MongoDB Group also has many restrictions, such as: The return result set can not exceed 16M, the group operation will not handle more than 10,000 unique keys, as if the index is not available

About Hadoop Hadoop is an open source system that implements Google's cloud computing system, including parallel computing model Map/reduce, Distributed File System HDFs, and distributed database HBase, along with a wide range of Hadoop related