Migrating to NoSQL – MongoDB

Data Shards

Article from Issue 153/2013
Author(s):

MongoDB combines the usual advantages of NoSQL databases with sharding, load balancing, replication, and failover.

Many products describe themselves with the NoSQL label. MongoDB [1] is arguably the most popular of these because it has seen incredible developer adoption. The NoSQL concept has advanced through the technology hype cycle [2], including wild claims about scalability [3], and MongoDB is making its way up the slope of enlightenment, having dealt with the trough of fear, uncertainty, and doubt [4]. With the help of 10gen, the MongoDB company, the product is now well on its way to addressing the enterprise market; however, what does that mean for you? Why should you migrate to MongoDB, and how should you do so?

After having switched an in-production app from MySQL to MongoDB back in early 2009, before even version 1.0 of MongoDB had been released, my company database now processes more than 20TB of data each month. With this experience under my belt, I'm going to cover several important areas that should help you understand when to consider switching to MongoDB.

Client Libraries

Like MySQL, Postgres, or most other databases, MongoDB is language agnostic and provides a custom wire protocol that you use to talk to the database. Some NoSQL databases operate over plain HTTP, so although having a wire protocol means using a custom driver [5], it does offer advantages, such as lower overhead and a query language implementation that makes sense to whatever programming language you're working with.

Like most databases, MongoDB is open source, and with a clear spec, anyone can create their own client. The community drivers, I have found, were not well tested at high throughput, and with little or no support, you either have to fix bugs yourself or rely on author availability. However, the official 10gen drivers are well maintained and tested. This becomes particularly important once your project goes into production: You know you're dealing with a stable driver, and any issues are likely to be fixed in a timely manner. These official MongoDB drivers [6] are available for a wide range of languages, including C, C++, C#, Erlang, Java, JavaScript, Node.js, Perl, PHP, Python, and Ruby (Figure 1).

Figure 1: MongoDB has a number of drivers and client libraries for many programming languages.

Schema Freedom

A big advantage of NoSQL databases is that users are not required to define a schema up front. Making structural changes with MySQL and other relational databases can require a great deal of effort, but documents in MongoDB are simply JSON files that can be changed on the fly and include structures like arrays and timestamps, or even other documents [7].

However, just because you can add fields at will doesn't mean you shouldn't think about document design at the beginning, especially if it leads to an increase in document size, threatening performance problems. For example, if you add new fields or if the size of your document (&0x2245;  field names+field values) grows past the allocated space, your document will have to be rewritten elsewhere in the data file, creating a performance hit. If this happens a lot, Mongo will adjust paddingFactor [8], so documents are allocated more space by default; in-place updates are faster.

One way to avoid rewriting an entire document and modifying documents in place is to specify only those fields you want to change and use modifiers where possible. Instead of sending a whole new document to update an existing one, you can set or remove specific fields.

The following query in the mongo shell (Figure 2) updates the cats field to 5 and sets the hats field to 2:

Figure 2: Targeted updates of individual fields conserve resources; here, updates take place with the use of the $set, $unset, and $inc operators.
db.collection.update( { cats: 5 }, { $set: { hats: 2 } } );

The next query does the same thing, but instead of setting one of the fields, it removes it from the document:

db.collection.update( { cats: 5 }, { $unset: { hats: "" } } );

For certain operations, like incrementing, you can use modifiers. Instead of setting the hats field to 2 as before, you can increment it by a certain number. Assuming its current value is 1, the following query increments the field by 2, yielding a final value of 3.

db.collection.update( { cats: 5 }, { $inc: { hats: 2 } } );

These operations are more efficient in communicating with the database, as well as modifying the file.

Because a document might be rewritten just by changing a field data type, consider the format in which you want to store your data. For example, if you rewrite (float)0.0 to (int)0, you are changing the BSON data type; therefore, familiarize yourself with the BSON specification [9].

Failover and Redundancy

Server Density, my company's product, handles a huge amount of data from monitoring customer servers and websites. Billions of metrics are recorded, and we need to ensure uptime and reliability, so we can alert customers when problems occur. This means replication across data centers, which was one of the biggest difficulties I had with MySQL – getting replication up and running and automating failover.

MongoDB uses the concept of replica sets, essentially a master/slave setup requiring a minimum of three nodes, any of which can become the master (by default). The instances communicate constantly via a heartbeat protocol, and if a failure is detected, one of the nodes is elected as master. This process all happens internally and is then picked up by the client drivers: An exception will be raised, and a reconnect happens automatically, so you can decide whether to show an error to the user or just retry silently.

This approach means only a minimal amount of work achieves redundancy with multiple copies of your database and automatic failover. Further options [10] allow you to:

  • Define priorities to determine which nodes should become master, and in which order;
  • Hide nodes, so they can store copies of the data, but never become master;
  • Add and remove nodes with minimal or no effect on the replica set;
  • Have some members deliberately stay behind the master by a certain amount of time (e.g., to allow recovery in the case of a catastrophic event, such as deleting a whole database); and
  • Set up tiny nodes that help elect new masters but don't store any data (e.g., to make up the majority and help decide what happens in the event of a network split).

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