MongoDB middle level (replace groupby with mapreduce in large data volumes)

In fact, mapreduce in MongoDB is more similar to groupby in relational databases.

Just after doing this experiment, the groupby (mapreduce) for large data volumes is still ideal, generating million 3-bit random strings

  for (var i=0; i<1000000; i++)   {  var x = "0123456789";  var tmp="";  for (var j=0; j<3; j++)  {  tmp += x.charAt(Math.ceil(Math.random()*100000000)%x.length);|  }  var u = {_id:i,v1:tmp};  db.RandomNum.insert(u);  }

Then perform the same random number to get the Count number, so it must be groupby

VaR M = function () {emit (this. v1, {count: 1}) ;}; // map key is similar to the group by of relational data. The second value is the field to be aggregated (Sum ...) vaR r = function (Key, values) {var Total = 0; For (VAR I = 0; I <values. length; I ++) {total + = values [I]. count ;}return {count: Total };};. // reduce var res = dB. randomnum. mapreduce (M, R, {out: {Replace: 'result'}); DB [res. result]. find ()

Tested:

var startTime = new Date();var m = function(){emit(this.v1,{count:1}); };var r = function (key,values) { var total = 0;for (var i=0; i<values.length; i++) { total += values[i].count; } return {count : total}; }; var res = db.RandomNum.mapReduce(m, r, {out:{replace:'Result'}});db[res.result].find()(new Date().getTime()-startTime.getTime())/1000

The result is as follows:

> db[res.result].find(){ "_id" : "000", "value" : { "count" : 1075 } }{ "_id" : "001", "value" : { "count" : 1045 } }{ "_id" : "002", "value" : { "count" : 1022 } }{ "_id" : "003", "value" : { "count" : 968 } }{ "_id" : "004", "value" : { "count" : 994 } }{ "_id" : "005", "value" : { "count" : 1009 } }{ "_id" : "006", "value" : { "count" : 948 } }{ "_id" : "007", "value" : { "count" : 1003 } }{ "_id" : "008", "value" : { "count" : 983 } }{ "_id" : "009", "value" : { "count" : 993 } }{ "_id" : "010", "value" : { "count" : 987 } }{ "_id" : "011", "value" : { "count" : 982 } }{ "_id" : "012", "value" : { "count" : 957 } }{ "_id" : "013", "value" : { "count" : 1031 } }{ "_id" : "014", "value" : { "count" : 971 } }{ "_id" : "015", "value" : { "count" : 1053 } }{ "_id" : "016", "value" : { "count" : 974 } }{ "_id" : "017", "value" : { "count" : 975 } }{ "_id" : "018", "value" : { "count" : 978 } }{ "_id" : "019", "value" : { "count" : 1010 } }has more> > (new Date().getTime()-startTime.getTime())/100063.335s> bye

Test Machine performance: