我是MongoDB的新手,我正试图了解我是否可以使用MongoDB方便地执行此查询并获得不错的性能.我想将数值和数组参数传递给查询,并使用它们对集合中每个文档中的数组值执行逐元素操作.这可能吗?
例如集合包含以下文档:
{
"name" : "item1",
"m" : 5.2,
"v" : 1.1,
"data1" : [ 0, 0, 0.3, 0.7, 0.95, 0.9, 0.75, 0.4, 0.1, 0 ],
"data2" : [ 0, -1, 0, 1, 1, 0 ]
}
我有另一个“搜索”文档可能看起来像这样:
{
"x" : 8,
"K" : 1,
"dataA" : [ 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 0.5],
"dataB" : [ 0, -2, 0, 1, 1, 0 ]
}
我想运行一个查询或map-reduce,使用上面的搜索文档对上面的集合返回一个包含以下内容的集合:
{
"name",
"y" = fn(m, v, x, K) = Kvx^(1/m) (not the real formula but just an example)
"dataF" = Max(i=0..9) {data1[i] * dataA[i] }
"dataS" = Sum(j=0..5) {data2[j] * dataB[j] }
}
where y>0
因此,对于上面的示例,返回的结果将是
{
"name" : "item1",
"y" : 1 * 1.1 * 8^5.2 = 1.641
"dataF" : Max(..., 0.4*0.5, 0.1*1, 0 * 0.5 ) = 0.2
"dataS" : 0*0 + (-1)*(-2) + 0*0 + 1*1 + 1*1 + 0*0 = 4
}
使用MongoDB可以/方便吗?
注意:在我的应用程序中,使用标准的MongoDB操作将在搜索中包含更多标准条件,因此我希望在查询中包含上述处理并避免在客户端上执行此操作.
最佳答案 这是map / reduce版本:
db.data.save({
"name" : "item1",
"m" : 5.2,
"v" : 1.1,
"data1" : [ 0, 0, 0.3, 0.7, 0.95, 0.9, 0.75, 0.4, 0.1, 0 ],
"data2" : [ 0, -1, 0, 1, 1, 0 ]
});
db.data.mapReduce( function() {
var searchdoc = {
"x" : 8,
"K" : 1,
"dataA" : [ 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 0.5],
"dataB" : [ 0, -2, 0, 1, 1, 0 ]
};
var result = {name: this.name};
result.y = searchdoc.K * this.v * Math.pow(searchdoc.x, 1 / this.m);
if(result.y > 0) {
result.dataF = 0;
for(i=0;i<this.data1.length;i++) {
var f = this.data1[i] * searchdoc.dataA[i];
if(f > result.dataF) {
result.dataF = f;
}
}
result.dataS = 0;
for(i=0;i<this.data2.length;i++) {
var s = this.data2[i] * searchdoc.dataB[i];
result.dataS += s;
}
emit(this.name, result);
}
}, function(key, values){}, {out: {inline: 1}});
结果:
{
"results" : [
{
"_id" : "item1",
"value" : {
"name" : "item1",
"y" : 1.640830939540542,
"dataF" : 0.2,
"dataS" : 4
}
}
],
"timeMillis" : 0,
"counts" : {
"input" : 1,
"emit" : 1,
"reduce" : 0,
"output" : 1
},
"ok" : 1,
}
这是shell版本:
db.data.save({
"name" : "item1",
"m" : 5.2,
"v" : 1.1,
"data1" : [ 0, 0, 0.3, 0.7, 0.95, 0.9, 0.75, 0.4, 0.1, 0 ],
"data2" : [ 0, -1, 0, 1, 1, 0 ]
});
var searchdoc = {
"x" : 8,
"K" : 1,
"dataA" : [ 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 0.5],
"dataB" : [ 0, -2, 0, 1, 1, 0 ]
};
var search = function(searchdoc) {
db.data.find().forEach(function(obj) {
var result = {name:obj.name};
result.y = searchdoc.K * obj.v * Math.pow(searchdoc.x, 1 / obj.m);
if( result.y > 0 ) {
result.dataF = 0;
for(i=0;i<obj.data1.length;i++) {
var f = obj.data1[i] * searchdoc.dataA[i];
if(f > result.dataF) {
result.dataF = f;
}
}
result.dataS = 0;
for(i=0;i<obj.data2.length;i++) {
var s = obj.data2[i] * searchdoc.dataB[i];
result.dataS += s;
}
db.results.save(result);
}
});
}
search(searchdoc);
db.results.find();
{ "_id" : ObjectId("4f08ffe4264d23670eeaaadf"), "name" : "item1", "y" : 1.640830939540542, "dataF" : 0.2, "dataS" : 4 }