A NoSQL document database is MongoDB. It is a document-based open-source application for large-scale data storage.

Since its founding in 2007, MongoDB has grown to include a large developer community. Due to its qualities, which fit well with many businesses' goals, it has grown and reached out to many.

It makes use of both an unstructured query language and a document-oriented data model. Developers can easily use and understand the product because of its scalability and versatility.

The scale-out architecture of MongoDB enables programmers to use agile development techniques to create apps. It is well-liked by developers since it makes it easier for them to create web and enterprise apps.

Let's examine MongoDB's benefits and drawbacks now that we have seen its functionality. This database is not relational.

It is therefore wise to compare with RDBMS and determine where it lives up to our expectations. In this advantages of MongoDB tutorial, we'll look at some of the primary advantages and constraints of MongoDB.

The benefits and drawbacks of utilizing MongoDB for businesses

  • Ad-hoc Queries
  • Schema-Less Database
  • Document-Oriented
  • Indexing
  • Replication
  • Aggregation
  • GridFS
  • Sharding
  • High Performance

Disadvantages of MongoDB

  • Transactions
  • Joins
  • Indexing
  • Limited Data Size and Nesting
  • Duplicates
  • High Memory Usage

Advantages of MongoDB

Ad-hoc Queries

Typically, we don't know the exact queries we'll be running when designing a database's structure. Ad-hoc queries are those that weren't anticipated while the database was being organised.

As a result, MongoDB offers ad-hoc query capabilities, which is what distinguishes it in this situation. Performance is enhanced as a result of real-time updates to ad-hoc queries.

Schema-Less Database

One collection in MongoDB contains a variety of documents. Since it lacks a schema, it is capable of having many different fields, contents, and sizes than other documents in the same collection. Because of this, MongoDB handles databases with flexibility.


The fact that MongoDB is a document-oriented database is a fantastic feature. Tables and rows are used in relational databases to organise the data. Each row contains a defined number of columns that can each hold a particular kind of data.

Here is where NoSQL's versatility shines, when fields are used in place of tables and rows. There are various papers that can store various kinds of data. There are groups of related documents. Each document has a distinct key id or object id, which may be defined by the user or the system.


Indexing is crucial for enhancing the effectiveness of search queries. We should index the fields in a document that fit our search criteria as we conduct ongoing searches on it.

We can index any field in MongoDB that has both primary and secondary indices. The performance of MongoDB is improved via indexing, which speeds up query searches.


Replication is the method that MongoDB employs for redundancy. Data is distributed across numerous machines via this feature. It is possible for it to have primary nodes and one or more replica sets. Replication essentially prepares for unexpected events.

The secondary node takes over as the primary node for the instance when the primary node is unavailable for some reason. This expedites maintenance and streamlines business processes.


A framework for aggregation is available in MongoDB for effective usage. Even after carrying out many actions on the group of data, we can batch process data and obtain a single output.

The three ways to provide an aggregate framework are the aggregation pipeline, the map-reduce function, and single purpose aggregation techniques. In later articles, we'll examine them in more detail.


A function for storing and retrieving files is called GridFS. This feature is particularly helpful for files bigger than 16 MB. A document is divided into chunks by GridFS, and each chunk is stored as a separate document. Except for the last chunk, each of these chunks has a default size of 255 kB.

When we ask GridFS for a file, it puts all the pieces together as necessary.


Essentially, the idea of sharding is used when dealing with larger datasets. When a query is made for them, this enormous amount of data may pose some issues. This function aids in spreading out the troublesome data among several MongoDB instances.

The larger collections in the MongoDB are split up into different collections. These groups are referred to as "shards." Clusters put shards in place.

High Performance

High-performance open source database MongoDB is available for free. This demonstrates scalability and high availability. It responds to queries more quickly thanks to replication and indexing. Because of this, it is a superior option for real-time and big data applications.

Disadvantages of MongoDB


Transactions are the process of going over and removing unnecessary data. Multi-document ACID transactions (Atomicity, Consistency, Isolation, and Durability) are used by MongoDB.

Although certain applications may need transactions to update numerous documents and collections, the majority of applications do not. This is a significant drawback of MongoDB because it could result in data corruption.


In MongoDB, joining documents can be an extremely time-consuming process. It is not a relational database and cannot handle joins.

Although teams have been set up to address this problem, it is still in its infancy and will take time to develop.

The joins functionality can be used by users by manually inserting the code. However, obtaining information from numerous collections necessitates a number of queries, which might take time and result in codes that are dispersed.


MongoDB enables quick performance when the appropriate indexes are used. If the indexing is carried out poorly or contains any errors, MongoDB will operate at a very slow speed.

It would take time to fix the indexing issues. This is yet another one of MongoDB's significant drawbacks.

Limited Data Size and Nesting

MongoDB only permits documents with a maximum size of 16 MB. Document performance nesting is likewise capped at 100 levels.


The duplication of data is another one of MongoDB's key drawbacks. The shortcoming makes handling data sets challenging because the relations are not clearly stated.

Data duplication may eventually result in corruption because it is not ACID compliant.

High Memory Usage

Due to the lack of join functionality, which causes data duplication, MongoDB has a high storage requirement. The amount of redundant data has increased, consuming unneeded memory space.

As always, there are advantages and disadvantages, according to BI consultant, speaker, and author Barry Devlin. It's always a good idea to avoid keeping a second duplicate of huge amounts of data on hand. Using the data from the original source can be helpful if the analysis doesn't call for merging it with data from another source. Concerns regarding the operating source's performance effects and occasionally security consequences are common.

He continued by listing the benefits and drawbacks of MongoDB. The key concern, however, revolves around the kinds of queries that can be made against a NoSQL store in general or a document-oriented database in this particular case. It is generally acknowledged that normalising data in a relational database creates a structure that is more query-neutral and capable of handling a larger range of queries.


The benefits of MongoDB outweigh its drawbacks, which is why many companies use its solutions to improve their goods, services, and apps.

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