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Restoring Faith in Humanity - helping others in need
I do not build database engines. I build web applications. I see apps with different requirements and different data storage needs. This is a story about one of those times — why we picked it originally, how we discovered it was wrong, and how we recovered. It all happened on an open source project called Diaspora. The project Diaspora is a distributed social network with a long history. They sent it out to friends and family, and hoped for the best. But they hit a nerve. Diaspora was the first Kickstarter project to vastly overrun its goal. The fallout from that was actually how I first heard about the project. As a result of their Kickstarter success, the guys left school and came out to San Francisco to start writing code. They ended up in my office. I worked with official clients during the day, then hung out with them after work and contributed code on weekends. They ended up staying at Pivotal for more than two years. By the end of that first summer, though, they already had a minimal but working for some definition implementation of a distributed social network built in Ruby on Rails and backed by MongoDB. The main technical difference between Diaspora and Facebook is invisible to end users: The Diaspora infrastructure is not located behind a single web address. There are hundreds of independent Diaspora servers. The code is open source, so if you want to, you can stand up your own server. Each server, called a pod, has its own database and its own set of users, and will interoperate with all the other Diaspora pods that each have their own database and set of users. Pods of different sizes communicate with each other, without a central hub. You can follow other users on your pod, and you can also follow people who are users on other pods. Your pod is notified over the API. You look at your activity feed and see that post mixed in with posts from the other people you follow. Comments work the same way. Everyone who has permission to see the post sees all the comments, just as you would expect if everyone were on a single logical server. There are technical and legal advantages to this architecture. The main technical advantage is fault tolerance. Here is a very important fault tolerant system that every office should have. The system survives, and even expects, network partitioning. The main legal advantage is server independence. Each pod also sets their own terms of service. On most of them, you can post content without giving up your rights to it, unlike on Facebook. But in other ways it is anything but typical. The internal structure of one Diaspora pod The visual UI is of course how website users interact with Diaspora. And of course, MongoDB is an atypical choice for data storage. Conceptually, we do think about it as a network — an undirected graph in which we are in the center, and our friends radiate out around us. Photos all from rubyfriends. But what are the alternatives? Other folks say that document databases are perfect for social data, and those are mainstream enough to actually be used. Instead of storing your data in tables made out of individual rows, like a relational database does, it stores your data in collections made out of individual documents. At the root, we have a set of TV shows. Each show has many seasons, each season has many episodes, and each episode has many reviews and many cast members. When users come into this site, typically they go directly to the page for a particular TV show. On that page they see all the seasons and all the episodes and all the reviews and all the cast members from that show, all on one page. There are a number of ways you could model this data. In a typical relational store, each of these boxes would be a table. We could also model this data as a set of nested hashes. Within each season, an array of episodes, each of which is a hash, and so on. This is how MongoDB models the data. Each TV show is a document that contains all the information we need for one show. Each season is itself a hash with metadata and an array of episodes. In turn, each episode has some metadata and arrays for both reviews and cast members. Sets of sets of sets of sets. On my laptop, PostgreSQL takes about a minute to get denormalized data for 12, episodes, while retrieval of the equivalent document by ID in MongoDB takes a fraction of a second. So in many ways, this application presented the ideal use case for a document store. But what about social data? The activity stream query gets all of the posts from the people you follow, ordered by most recent. Each of those posts have nested information within them, such as photos, likes, reshares, and comments. The nested structure of activity stream data looks very similar to what we were looking at with the TV shows. Users have friends, friends have posts, posts have comments and likes, each comment has one commenter and each like has one liker. And just like with TV shows, we want to pull all this data at once, right after the user logs in. Diaspora chose MongoDB for their social data in this zeitgeist. It was not an unreasonable choice at the time, given the information they had. What could possibly go wrong? With TV shows, each box in the relationship diagram is a different type. TV shows are different from seasons are different from episodes are different from reviews are different from cast members. None of them is even a sub-type of another type. But with social data, some of the boxes in the relationship diagram are the same type. In fact, all of these green boxes are the same type — they are all Diaspora users. A user has friends, and each friend may themselves be a user. In the same way, commenters and likers may also be users. This type duplication makes it way harder to denormalize an activity stream into a single document. The user who liked that post in your activity stream may also be the user who commented on a different post. Duplication is any easy option. All the information for that friend is copied and saved to the like on the first post, and then a separate copy is saved to the comment on the second post. The advantage is that all the data is present everywhere you need it, and you can still pull the whole activity stream back as a single document. Here we have copies of user data inlined. You can see why this is attractive: You can also see why this is dangerous. This is very error-prone, and often leads to inconsistent data and mysterious errors, particularly when dealing with deletions. Is there no hope? There is another approach you can take to this problem in MongoDB, which will more familiar if you have a relational background. Instead of duplicating user data, you can store references to users in the activity stream documents. With this approach, instead of inlining this user data wherever you need it, you give each user an ID. New IDs are in green below. This eliminates our duplication problem. This is less efficient and more complex. Constructing an activity stream now requires us to 1 retrieve the stream document, and then 2 retrieve all the user documents to fill in names and avatars. Because all the boxes in the relationship diagram are different entities, the entire query can be denormalized into one document with no duplication and no references. In this document database, there are no links between documents. It requires no joins. On a social network, however, nothing is that self-contained. Any time you see something that looks like a name or a picture, you expect to be able to click on it and go see that user, their profile, and their posts. Once we started doing ugly MongoDB joins manually in the Diaspora code, we knew it was the first sign of trouble. It was a sign that our data was actually relational, that there was value to that structure, and that we were going against the basic concept of a document data store. MongoDB is not the right solution for you. They mean one of these two things: But there are actually very few concepts in the world that are naturally modeled as normalized tables.
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