Question

充斥着聚合问题。我认为,解决问题的最容易的方法是利用地图减少或单独发现问题,然后与作为同堂图书馆的帮助相处。

The schema is here:

db.keyword
keyword: String
start: Date
source: String(Only one of these (‘google’,’yahoo’,’bing’,’duckduckgo’) )
job: ref db.job
results: [
    {
            title: String
            url: String
            position: Number
    }
]


db.job
name: String
keywords: [ String ]
urls: [ String ]
sources: [ String(‘google’,’yahoo’,’bing’,’duckduckgo’) ]

现在需要将数据输入这一表格:

data = {
    categories: [  keyword1 ,  keyword2 ,  keyword3  ],
    series: [
        {
            name:  google ,
            data: [33, 43, 22]
        },
        {
            name:  yahoo ,
            data: [12, 5, 3]
        }

    ]
}

Well the biggest problem is that the series[0].data array is made of really difficult find, matching the db.job.urls against the db.keyword.results.url and then get the position. Is there any way to simplify the query_? I have looked through many of the map reduce examples, but I cant find the correct way what data to map and which to reduce.

Answer 1

它看着你试图将两个单独收集的数据(关键词和工作)结合起来。

《减少地图》和新的《综合框架》只能在一次性收集的基础上运作。

您的最好之点可能是分别询问每项收集情况,并将结果与方案结合起来,以最适合您申请的方式加以节约。

If you would like to experiment with Map Reduce, here is a link to a blog post written by a user who used an incremental Map Reduce operation to combine values from two collections.
http://tebros.com/2011/07/using-mongodb-mapreduce-to-join-2-collections/

For more information on using Map Reduce with MongoDB, please see the Mongo Documentation: http://www.mongodb.org/display/DOCS/MapReduce (The section on incremental Map Reduce is here: http://www.mongodb.org/display/DOCS/MapReduce#MapReduce-IncrementalMapreduce)

There are some additional Map Reduce examples in the MongoDB Cookbook: http://cookbook.mongodb.org/

关于如何开展“地图减少行动”的逐步行,请见MongoDB Cookbook recipe“Finding Max And Min Values with FCCC”。

希望以上将给你一些想法,说明如何取得你所期望的成果。正如我提到的那样,我认为最直截了当的解决办法只是将成果与方案结合起来。然而,如果你成功地撰写了“减少行动”地图,请提出解决办法,使共同体能够从你的经验中受益。

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