当我面对类似的解决方案时，我回溯并发现业务请求实际上是为了创建所呈现的库存的某种形式的轮换。在这种情况下，有更好的选择，它们有来自Solr这样的搜索引擎的答案，而不是MongoDB这样的数据存储。

In short, with the requirement to "intelligently rotate" content, what we should do instead of a random number across all of the documents is to include a personal q score modifier. To implement this yourself, assuming a small population of users, you can store a document per user that has the productId, impression count, click-through count, last seen date, and whatever other factors the business finds as being meaningful to compute a q score modifier. When retrieving the set to display, typically you request more documents from the data store than requested by the end user, then apply the q score modifier, take the number of records requested by the end user, then randomize the page of results, a tiny set, so simply sort the documents in the application layer (in memory).

如果用户的范围太大，可以将用户划分为行为组，按行为组而不是按用户进行索引。

如果产品范围足够小，您可以为每个用户创建一个索引。

我发现这种技术效率更高，但更重要的是在创建相关的、有价值的软件解决方案使用体验方面更有效。

您还可以使用MongoDB的地理空间索引功能来选择与随机数“最近”的文档。

首先，在集合上启用地理空间索引:

db.docs.ensureIndex( { random_point: '2d' } )

用x轴上的随机点创建一堆文档:

for ( i = 0; i < 10; ++i ) {
    db.docs.insert( { key: i, random_point: [Math.random(), 0] } );
}

然后你可以像这样从集合中随机获得一个文档:

db.docs.findOne( { random_point : { $near : [Math.random(), 0] } } )

或者你可以检索几个文档最近的随机点:

db.docs.find( { random_point : { $near : [Math.random(), 0] } } ).limit( 4 )

这只需要一个查询，没有空检查，加上代码干净，简单和灵活。您甚至可以使用地理点的y轴为查询添加第二个随机性维度。

我对php的解决方案:

/**
 * Get random docs from Mongo
 * @param $collection
 * @param $where
 * @param $fields
 * @param $limit
 * @author happy-code
 * @url happy-code.com
 */
private function _mongodb_get_random (MongoCollection $collection, $where = array(), $fields = array(), $limit = false) {

    // Total docs
    $count = $collection->find($where, $fields)->count();

    if (!$limit) {
        // Get all docs
        $limit = $count;
    }

    $data = array();
    for( $i = 0; $i < $limit; $i++ ) {

        // Skip documents
        $skip = rand(0, ($count-1) );
        if ($skip !== 0) {
            $doc = $collection->find($where, $fields)->skip($skip)->limit(1)->getNext();
        } else {
            $doc = $collection->find($where, $fields)->limit(1)->getNext();
        }

        if (is_array($doc)) {
            // Catch document
            $data[ $doc['_id']->{'$id'} ] = $doc;
            // Ignore current document when making the next iteration
            $where['_id']['$nin'][] = $doc['_id'];
        }

        // Every iteration catch document and decrease in the total number of document
        $count--;

    }

    return $data;
}

对所有记录进行计数，生成一个0到计数之间的随机数，然后执行:

db.yourCollection.find().limit(-1).skip(yourRandomNumber).next()

在Python中使用pymongo:

import random

def get_random_doc():
    count = collection.count()
    return collection.find()[random.randrange(count)]

这工作得很好，它是快速的，适用于多个文档，不需要填充rand字段，它最终会填充自己:

向集合上的.rand字段添加索引 使用查找和刷新，如下所示:

// Install packages:
//   npm install mongodb async
// Add index in mongo:
//   db.ensureIndex('mycollection', { rand: 1 })

var mongodb = require('mongodb')
var async = require('async')

// Find n random documents by using "rand" field.
function findAndRefreshRand (collection, n, fields, done) {
  var result = []
  var rand = Math.random()

  // Append documents to the result based on criteria and options, if options.limit is 0 skip the call.
  var appender = function (criteria, options, done) {
    return function (done) {
      if (options.limit > 0) {
        collection.find(criteria, fields, options).toArray(
          function (err, docs) {
            if (!err && Array.isArray(docs)) {
              Array.prototype.push.apply(result, docs)
            }
            done(err)
          }
        )
      } else {
        async.nextTick(done)
      }
    }
  }

  async.series([

    // Fetch docs with unitialized .rand.
    // NOTE: You can comment out this step if all docs have initialized .rand = Math.random()
    appender({ rand: { $exists: false } }, { limit: n - result.length }),

    // Fetch on one side of random number.
    appender({ rand: { $gte: rand } }, { sort: { rand: 1 }, limit: n - result.length }),

    // Continue fetch on the other side.
    appender({ rand: { $lt: rand } }, { sort: { rand: -1 }, limit: n - result.length }),

    // Refresh fetched docs, if any.
    function (done) {
      if (result.length > 0) {
        var batch = collection.initializeUnorderedBulkOp({ w: 0 })
        for (var i = 0; i < result.length; ++i) {
          batch.find({ _id: result[i]._id }).updateOne({ rand: Math.random() })
        }
        batch.execute(done)
      } else {
        async.nextTick(done)
      }
    }

  ], function (err) {
    done(err, result)
  })
}

// Example usage
mongodb.MongoClient.connect('mongodb://localhost:27017/core-development', function (err, db) {
  if (!err) {
    findAndRefreshRand(db.collection('profiles'), 1024, { _id: true, rand: true }, function (err, result) {
      if (!err) {
        console.log(result)
      } else {
        console.error(err)
      }
      db.close()
    })
  } else {
    console.error(err)
  }
})

ps.如何在mongodb问题中找到随机记录被标记为此问题的副本。不同之处在于，这个问题明确地询问单个记录，而另一个问题明确地询问随机文档。