When weighing Cassandra vs. MongoDB, performance, design, and operational variables top the list of considerations. While both systems' approaches to data storage and manipulation lure users in, the actual processes are different enough to polarize their user bases.
This post explores the differences you'll encounter when using Cassandra and MongoDB databases. Retaining a pragmatic mindset, we'll also help the reader understand how a shrewd analyst/engineer can implement both options. This way, you can experience the best in terms of performance, compatibility, and ease of use.
Quick disclaimer: We're not going to favor either data storage system. Neither will we limit you to using just one of the two. Instead, and most importantly, you should know which option works best for explicit use cases.
Here's what we'll cover in this comparison of Cassandra and MongoDB:
- Architectural and handling differences between Cassandra and MongoDB
- The robustness of Cassandra compared to MongoDB
- The support provided for each database
- The top use cases for Cassandra and MongoDB
We could go on and on, doing side-by-side contrasts of the features, capabilities, and shortcomings of the two database solutions. However, these four factors should give every reader the knowledge necessary to pick Cassandra, MongoDB, or a blend of both.
Let's start with a brief history of both platforms, shall we?
MongoDB started back in 2007 as an in-house project to handle specific problems faced by DoubleClick. The privately owned marketing company was running thousands of ads concurrently and needed agility and scalability. This need inspired them to create MongoDB. The platform has always been free, but certain features and dedicated instances attract a subscription fee to the team supporting the platform.
Cassandra was created at Facebook's HQ in Menlo Park, California, in 2008, for use in Facebook's messaging search module. The developers named Cassandra after a Trojan priestess cursed to speak true prophecies. Cassandra has always been free, and it's now managed by the Apache foundation.
Architectural differences: Cassandra vs. MongoDB
To start with, both database solutions are distributed by design. With Cassandra, your data is stored in non-relational partitions just as you insert them—much like any other NoSQL platform would. MongoDB takes the NoSQL concept a step further by being document-based. This means every time you insert data into a MongoDB instance, a (JSON-type) document with the values and their metadata is generated.
It's advisable to install Cassandra instances on multiple machines to create a network of nodes. This way, you get more storage options and increased availability over your desired access points. In fact, Cassandra thrives on this node-based topology. You'll see how as we discuss other features in ensuing sections.
Cassandra's robustness compared to MongoDB
Several differences start showing up when accessing stored data from the two database options. Firstly, how do you get access? When spread across nodes, Cassandra maintains copies of your data based on a decided replication factor. Cassandra is famed for being one of the most resilient data storage options. Each node you add to your cluster adds to its performance and reliability factors.
Every node on the distributed machines running Cassandra whispers its contents to the rest of the network. This means data created on a Cassandra node in Europe (for example) is instantly available to machines in the U.S. If for any reason a node is down, you can still access its data through a coordinator system that retains copies of the data it allocated to remote partitions upon storage. This is much like how cache works.
This Cassandra feature is not available by default with MongoDB. To start with, you have to install the cluster version of the platform to get anything similar. When done, you get more of the network benefits of cluster instances than hard coded functions as is the case with Cassandra. This is not to say you can't build these as your MongoDB database grows. In fact, the community around MongoDB has been instrumental to this end.
Community is a key feature in the growth of both database options. Let's examine that in more detail.
Support strategies for MongoDB and Cassandra
Every database storage option attracts a community of developers around it. In addition to the vendor's efforts, such a crowd is continuously improving how we use the platform. The MongoDB community includes a university resource pool to learn as you build alongside thousands of users managing over a million instances across the world.
Compared to MongoDB, Cassandra is an Apache project. This fact alone attracts thousands of contributors actively participating in an open-source repo. Couple this with fully packed Slack channels constantly discussing new patches and builds, and you're sure to get help whenever you face a problem with your Cassandra instances.
Top use cases for Cassandra and MongoDB
Cassandra is perfect for highly scalable applications in the cloud. The fact that it improves resilience as you add nodes reduces the hardware/resource thirst over time. Its design also makes it a favored data handling platform for copious amounts of data with speed and sustained availability.
MongoDB is useful when building apps across all business fronts—particularly mobile apps with infinite scaling possibilities. The document storage method it uses makes it quick to access—and later share—locally created information across networks. This makes it perfect for single-view data application development.
Companies using Cassandra include Yelp, Uber, and The New York Times. By contrast, eBay, Google, and Adobe currently use MongoDB.
Getting the best from your database management system
While both of these database storage options carry specific advantages you might want to leverage, you can get the best from both through careful planning. For instance, you might want nodes running across the world to handle large amounts of data from your apps. At the same time, you may be building mobile apps that will benefit from the document storage method consistent with MongoDB.
Which option is best for your company?
Well, that depends on what matters more for your needs: structure flexibility or extended availability across regions and add that to how much data you're handling. In all fairness, any database can handle the load startups need at the very beginning, so you must consider your growth when picking out which to use for development and which works for corporate data management.
One of the surest ways to get the best of both worlds is to have both databases handling your data. This can even be compounded by having their instances running on different cloud services providers across different regions to guarantee uptime as well as security by default.
Once your database is live, you can then pull data for presentation and other ETL functions through third-party platforms like Panoply. This way, it won't matter so much what platform you're using for your application's backend and which one you prefer to store internationally accessible data with. Instead, you get to combine both and present it where it matters: in the hands of decision-makers.