So, you are now confused in choosing between MongoDB and Cassandra for your next project. If you are considering the two options, you most probably have decided that you are going for a NoSQL solution. Both MongoDB and Cassandra can be categorized as NoSQL databases. In addition, both are also cross-platform and open-source. Both MongoDB and Cassandra are well known for their exceptional scalability, great reliability, and easy set-up.
Picking a NoSQL database can be very tricky, much more than choosing for an SQL solution. So, it is very important to consider the strengths of the database that you want to use and align them with your use case. Is fault tolerance a high priority, or are you more concerned about high latency? Below, we will see the overview of MongoDB and Cassandra, as well as the comparisons between these two popular NoSQL solutions.
On the other hand, Cassandra was first released in 2008. It is a free, open-source distributed NoSQL database management system written in Java. It was born at Facebook, combining Amazon’s Dynamo storage system and Google’s Bigtable paradigm, and is currently developed by Apache. It has been designed for handling large amounts of data on multiple commodity servers, thus providing high availability with literally no single point of failure. Cassandra has been greatly praised for its performance.
Perhaps the headline of MongoDB is that it offers the best things from two worlds: relational and NoSQL databases. The advantages offered by relational databases are also available in MongoDB, including the ease of set-up that enables programmers to build apps quickly.
Due to being a document-based database, MongoDB offers amazing flexibility. It is schema-free. It allows you to perform most things that you can do in MySQL or any other relational database, but it does not limit you to predefined columns. It can scale horizontally and handle unstructured data, making it an ideal pair to mobile apps and content management systems. It can evolve along with your application.
MongoDB is able to analyze any kind of data in the database. Here, each document possesses its own data and its own unique key. MongoDB is a great option for document-oriented data that are still structured to a degree – an excellent alternative from relational databases on semi-structured and unstructured data.
MongoDB comes with full index support for high replication and fail-over for high real-time availability. It also has auto-sharding for unlimited horizontal scalability. It already comes with lots of features right out of the box. The powerful query system enables you to leverage location-based data efficiently, and the built-in special functions allow you to harvest data from specific locations and put them to use without complicated extraction processes.
MongoDB’s dynamic schemas will allow you to test and experiment things on the fly. Since the data don’t need to be prepared ahead of time to go into tables, you and your team will be able to incorporate new types of data and schemas into the database more quickly and at a lower cost. Its ability to support fast iterations also means that it will allow your app to scale up, be modified, and get renewed more quickly with less cost.
All in all, MongoDB is a great choice if you need a relational database replacement (especially for semi-structured and unstructured data), real-time analysis, high-speed logging and caching, and high scalability. However, it is not very good if you require traditional database requirements, such as foreign key constraints. MongoDB is notably used by eBay, Expedia, Craiglist, Foursquare, and The Weather Channel.
If you are looking for a NoSQL database that can handle large amounts of data without any single point of failure, Cassandra is the way to go. Cassandra has been used on many data-heavy apps, such as Instagram (which handles roughly 80 million photos uploaded daily) and Spotify (which has over 20 million songs in the database). Even if you think that your data will no grow to be that big, you can still benefit from the compression tools and high availability.
Cassandra uses an eventual consistency model, which is a great choice if immediate consistency is not a top priority on your list. It tends to compromise consistency and latency for availability. Rather than maintaining a strict consistency that can greatly slow things down at scale, Cassandra’s eventual consistency model allows data not to be synced up across all servers right away in order to ensure high availability. Thus, Cassandra becomes very popular on distributed setups.
Cassandra is famous for the ease of management at scale. So, it can be your choice if you anticipate lots of growth in the future. Cassandra is guaranteed to run safely and reliably nearly all the time. It has been designed to handle big data in a distributed manner, and it can be deployed across multiple servers easily. In Cassandra, there is no master/slave setup, fail-over, or leader election; instead, it utilizes a peer-to-peer fault tolerance technology. Any node in the cluster can be delegated to execute your query in the case of a failure.
Cassandra uses a wide-column system. It is a variation of key-value stores. Wide-column stores also have rows and columns, but, unlike in relational databases, the names and formats of those columns can change.
Cassandra is incredibly easy to maintain even after you have scaled way up. No matter how large your data grow to be, it is still easy to maintain, and it will still deliver incredibly fast performance.
Cassandra is great if your app needs a simple setup, easy maintenance regardless of huge data growth, flexible wide column requirements, and massive scalability. But it is not good for transactional operations, financial records, as well as apps requiring dynamic queries, low latency, and on-the-fly aggregations.
|- Master-slave model and failover||- Peer-to-peer fault tolerance with no single point of failure|
|- Low latency for read and write requests, good scalability and availability||- Very fast write operations but high latency on read requests, very high scalability and availability|
|- Replication provides automatic failover and data redundancy, but needs to be set up||- Replication is very simple, you just need to indicate how many nodes for your data to be copied to and it automatically handles the rest|
|- The shards are replications of servers||- Each shard is a server, whose data are replicated across the other servers|
So, MongoDB or Cassandra? MongoDB is great if you prefer a document-based store and if immediate consistency is a priority. MongoDB is also great if you need real-time analysis as well as high-speed logging and caching. However, if you expect lots of data growth, Cassandra is the way to go. Cassandra is a wide-column store. Cassandra is incredibly convenient for distributed setups, and it can be your choice if you don’t mind to compromise with eventual consistency for the sake of availability.