The NoSQL document database is designed to overcome the constraints of other NoSQL alternatives and the relational database methodology. MongoDB is well renowned for its horizontal scaling and load balancing features, which have provided application developers with previously unheard-of levels of flexibility and scalability.
MongoDB Atlas is the industry’s most popular worldwide cloud database service for modern applications. Atlas enables developers to deploy fully managed cloud databases on AWS, Azure, and Google Cloud. Best-in-class data security and privacy standards procedures ensure that developers have rapid access to the availability, scalability, and compliance required for enterprise-level application development.
MongoDB has been downloaded over 30 million times as of 2020, with over 730,000 MongoDB University registrations. There are drivers for more than ten languages, with dozens more being added by the community. The best part is that MongoDB is entirely free to use.
MongoDB delivers a number of valuable out-of-the-box options for developers, whether you need to run confidentially on-premises or in the public cloud.
Let’s take a look at MongoDB’s top five technical features:
Ad-hoc queries for optimised, real-time analytics
It is impossible to predict all of the queries that will be executed by end users while building a database structure. An ad hoc query is a short-lived command whose value depends on a variable. Depending on the variables in question, the outcome of an ad hoc query may alter each time it is conducted.
When thousands to millions of variables must be examined, optimising the way ad-hoc queries are handled can make a substantial impact. This is why MongoDB, a document-oriented, flexible schema database, is the preferred cloud database platform for enterprise applications requiring real-time analytics. Ad-hoc query capability, which allows developers to change ad-hoc queries in real time, can result in game-changing performance improvements.
MongoDB supports field and range queries, as well as regular expression searches. Queries can return specified fields as well as user-defined functions. Because MongoDB indexes BSON documents and uses the MongoDB Query Language, this is possible (MQL).
Indexing appropriately for better query executions
In our experience, indexing is the most common issue that many technical support teams neglect to address with their users. When done correctly, indexes are intended to increase search speed and performance. Failure to properly specify appropriate indices can and typically will result in a slew of accessibility concerns, such as query execution and load balancing issues.
Without the appropriate indices, a database is forced to scan documents one by one to find those that match the query statement. However, if an adequate index exists for each query, the server may optimally process user requests. MongoDB provides a diverse variety of indexes and features, including language-specific sort orders, to facilitate complex access patterns to datasets.
Notably, MongoDB indices can be generated on demand to satisfy real-time, ever-changing query patterns and application requirements. They can also be specified on any field in any of your documents, including those nested within arrays.
Replication for better data availability and stability
When your data is only stored in one database, it is vulnerable to multiple points of failure, such as a server crash, service outages, or even plain old hardware failure. Any of these situations would make it nearly impossible to access your data.
By implementing numerous servers for disaster recovery and backup, you can avoid these issues. Horizontal scaling over numerous servers that contain the same data (or shards of the same data) results in much improved data availability and stability. Naturally, replication aids in load balancing
Replica sets are used for this purpose in MongoDB. A primary server or node receives all write activities and replicates the data among other servers by performing identical operations. If the primary server ever fails catastrophically, any of the secondary servers can be chosen to become the new primary node. If the previous primary node is brought back up, it will function as a backup server for the new primary node.
Sharding
When dealing with very large datasets, sharding—the technique of dividing larger datasets into numerous dispersed collections, or “shards”—allows the database to spread and better perform queries that would otherwise be troublesome and cumbersome. Scaling a huge online application with millions of daily visitors is practically impossible without sharding.
Sharding in MongoDB, like replication via replication sets, enables significantly better horizontal scalability. Horizontal scaling means that each shard in each cluster contains a subset of the dataset in question, thereby acting as a distinct database. The collection of distributed server shards comprises a single, complete database that is far more suited to addressing the needs of a popular, expanding application while ensuring zero downtime.
Load balancing
Finally, optimal load balancing remains one of the holy grails of large-scale database management for expanding enterprise systems. Properly allocating millions of client requests to hundreds or thousands of servers can result in a considerable (and greatly welcomed) improvement in performance.
Fortunately, MongoDB offers large-scale load balancing via horizontal scaling features such as replication and sharding. With best-in-class concurrency control and locking mechanisms that maintain data consistency, the platform can handle several concurrent read and write requests for the same data. There is no need to deploy an external load balancer because MongoDB ensures that each user has a consistent view and a quality experience with the data they require.
