Since MongoDB is a highly popular NoSQL database, there have been several use cases where it has succeeded in supporting quality applications, with a great delivery time to the market.

Many of its most successful use cases center around the following areas:

• Integration of siloed data, providing a single view of them
• IoT
• Mobile applications
• Real-time analytics
• Personalization
• Catalog management
• Content management

All of these success stories share some common characteristics. We will try to break them down in order of relative importance:

• Schema flexibility is probably the most important one. Being able to store documents inside of a collection that can have different properties can help during both the development phase and in ingesting data from heterogeneous sources that may or may not have the same properties. This is in contrast with an RDBMS, where columns need to be predefined and having sparse data can be penalized. In MongoDB, this is the norm, and it is a feature that most use cases share. Having the ability to deeply nest attributes into documents and add arrays of values into attributes while also being able to search and index these fields helps application developers to exploit the schema-less nature of MongoDB.
• Scaling and sharding are the most common patterns for MongoDB use cases. Easily scaling using built-in sharding and using replica sets for data replication and offloading primary servers from read load can help developers store data effectively.
• Many use cases also use MongoDB as a way of archiving data. Used as a pure data store (and not having the need to define schemas), it is fairly easy to dump data into MongoDB to be analyzed at a later date by business analysts, using either the shell or some of the numerous BI tools that can easily integrate with MongoDB. Breaking data down further, based on time caps or document counts, can help serve these datasets from RAM, the use case in which MongoDB is most effective.
• Keeping datasets in RAM helps performance, and that's why it is commonly used in practice. MongoDB uses MMAP storage (called MMAPv1) in most versions, up to the most recent, which delegates data mapping to the underlying operating system. This means that most GNU/Linux-based systems, working with collections that can be stored in RAM, will dramatically increase performance. This is less of an issue with the introduction of pluggable storage engines, such as WiredTiger (there will be more on that in Chapter 8, Monitoring, Backup, and Security).
• Capped collections are also a feature used in many use cases. Capped collections can restrict documents in a collection by count or by the overall size of the collection. In the latter case, we need to have an estimate of the size per document, in order to calculate how many documents will fit into our target size. Capped collections are a quick and dirty solution to answering requests such as give me the last hour's overview of the logs without the need for maintenance and running async background jobs to clean our collection. Oftentimes, these may be used to quickly build and operate a queuing system. Instead of deploying and maintaining a dedicated queuing system, such as ActiveMQ, a developer can use a collection to store messages, and then use the native tailable cursors provided by MongoDB to iterate through the results as they pile up and feed an external system.
• Low operational overhead is also a common pattern in many use cases. Developers working in agile teams can operate and maintain clusters of MongoDB servers without the need for a dedicated DBA. MongoDB Management Service (MMS) can greatly help in reducing administrative overhead, whereas MongoDB Atlas, the hosted solution by MongoDB, Inc., means that developers do not need to deal with operational headaches.
• In terms of business sectors using MongoDB, there is a huge variety coming from almost all industries. Where there seems to be a greater penetration, however, is in cases that have to deal with lots of data with a relatively low business value in each single data point. Fields such as IoT can benefit the most by exploiting the availability over consistency design, storing lots of data from sensors in a cost-efficient way. Financial services, on the other hand, have absolutely stringent consistency requirements, aligned with proper ACID characteristics that make MongoDB more of a challenge to adapt. A financial transaction may be small in size but big in impact, which means that we cannot afford to leave a single message without proper processing.
• Location-based data is also a field where MongoDB has thrived, with Foursquare being one of the most prominent early clients. MongoDB offers quite a rich set of features around two-dimensional and three-dimensional geolocation data, offering features such as searching by distance, geofencing, and intersections between geographical areas.
• Overall, the rich feature set is the common pattern across different use cases. By providing features that can be used in many different industries and applications, MongoDB can be a unified solution for all business needs, offering users the ability to minimize operational overhead and, at the same time, iterate quickly in product development.