Mongodb mapreduce usage summary, mongodbmapreduce
This article is from my blog: mongodb mapreduce usage Summary
As we all know, mongodb is a non-relational database. That is to say, each table in the mongodb database exists independently and
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
Map/reduce in MongoDB makes some compound queries, because MongoDB does not support group by queries, and MapReduce is similar to SQL's group by, so it can be thought that MapReduce is the MongoDB version of group B Y
The command is as follows:
1. IntroductionMongoDB's mapreduce is equivalent to the group by in MySQL, so it's easy to use map/reduce on MongoDB, using MapReduce to implement two function map functions and the reduce function, the map function calls emit (Key,value),
MongoDB mapreduce-based statistical analysis
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MapReduce is a computational model that simply executes a large amount of work (data) decomposition (MAP) and then merges the results into the final result (REDUCE). The advantage of this is that after the task is decomposed, it
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
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
Topics on MongoDB data aggregation
MapreduceMapreduce is a computing model. Simply put, it is to execute a large number of jobs (data) into maps, and then merge the results into the final result (reduce ).
It's been a long time. Here, life is at a new beginning. This blog content long ago wanted to update up, but has not found the right time point (haha, in fact, lazy), the main content focused on the use of MongoDB when some of the hidden MapReduce
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