How to solve the "Digg" problem in MongoDB

Question

While the Digg developer posted this blog, http://about.digg.com/blog/looking-future-cassandra ", where he described one of the problems that were not optimized in MySQL. This was called one of the reasons they moving to Kassandra.

I played with MongoDB and I would like to understand how

implement MongoDB collections for this problem

From the article, the schema for this information in MySQL:

CREATE TABLE `Diggs` (
  `id`      INT(11),
  `itemid`  INT(11),
  `userid`  INT(11),
  `digdate` DATETIME,
  PRIMARY KEY (`id`),
  KEY `user`  (`userid`),
  KEY `item`  (`itemid`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

CREATE TABLE `Friends` (
  `id`           INT(10) AUTO_INCREMENT,
  `userid`       INT(10),
  `username`     VARCHAR(15),
  `friendid`     INT(10),
  `friendname`   VARCHAR(15),
  `mutual`       TINYINT(1),
  `date_created` DATETIME,
  PRIMARY KEY                (`id`),
  UNIQUE KEY `Friend_unique` (`userid`,`friendid`),
  KEY        `Friend_friend` (`friendid`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

This problem is ubiquitous in the implementation of social media scenarios. People are friends with many people, and they, in turn, find a lot of things. Quickly showing the user that his / her friends are very important.

I understand that since then, several blogs have provided a clean RDBM solution with indexes for this problem; however, I am curious how this can be solved in MongoDB.

Answer 1

- "" .

{
  date: Date(...)
  friends: ['me', 'you', 'thatguy']
  ...
}
db.posts.ensureIndex({friends:1, date:-1})

, : db.posts.find({friends:'me'}).sort({date:-1})

, 200 000 ; , . , 100 000

Answer 2

. diggs ( a.k.a.), db, diggs . .

, diggs ​​ . "in" items.diggs.userid.

Mongo $in operator .