v1.13.5 released

We are regularly releasing new versions and did it again today. The latest release contains several UX improvements, fixes 4 reported bugfixes (thanks!) and 10+ unreported issues we found ourselves.

Please keep reporting issues, we appreciate any feedback!

Mingo should suggest the update automatically, or you can download it directly from our Download page.

Mingo is a suitable solution for a GUI for MongoDB and its open-source alternative, FerretDB. Since FerretDB is open source, developers can fully control and adapt it to suit specific needs. FerretDB with Mingo will offer you a visual representation of data and a user-friendly interaction, among other benefits.

What is FerretDB

FerretDB is the open-source alternative for MongoDB. Initially, MongoDB was open source, and due to its robust documentation, it was preferred by developers needing an open-source database. FerretDB was founded when MongoDB switched to an SSPL license abandoning its open-source roots. The change in license by MongoDB made it unusable for many Open Source and Commercial Projects. Unlike MongoDB, FerretDB is under Apache License 2.0. FerretDB is the perfect solution for MongoDB users searching for an open-source alternative. The icing on the cake is that you can run similar MongoDB protocol queries without learning a new language.

In a recent interview, Alexey Palazhchenko, the CTO at FerretDB, stated that they aimed for their software to work with unmodified MongoDB drivers and applications. Another main idea before creating the software is that it should not store data by itself. True to it, FerretDB does not store data but is built on top of PostgreSQL. This implies that PostgreSQL is FerretDB’s main backend which is fully supported.  FerretDB also supports SQLite back end and is working on having an SQLite backend and SAP on HANA compatibility. The reason for having multiple backend alternatives is that users may want to use different backends for performance reasons.

The founders of FerretDB are always keen to emphasize that it is not another NoSQL database. Rather, it helps you leverage the power of the MongoDB ecosystem. That way, you can use all tools, drivers, and UIs like you would with MongoDB. Additionally, you can use the same query language and run it all on PostgreSQL or SQLite.

How FerretDB works

FerretDB is built on top of PostgreSQL. It stores documents in binary JSON format using the corresponding PostgreSQL column type. Since it does not store data by itself, it leverages the tables in PostgreSQL to store database structure information like database names, collections, and user access management. It translates MongoDB wire protocol queries to SQL for compatibility with existing software and tools built on MongoDB. The FerretDB community is addressing any incompatibility issues that emerge.

Benefits of using FerretDB

FerretDB and Mingo Setup

Scaleway and Civo MarketPlace are two cloud providers that have availed FerretDB for installation. However, for this setup, we will use Docker for installation. FerretDB Docker installation offers a complete guide on installing the software on Docker. If you do not have Docker installed on your machine, you should start by setting up Docker.

With that, you have FerretDB installed and ready to use with Mingo.

Mingo has first-class support for FerretDB, but FerretDB is not 100% compatible with MongoDB API, so some of the features are not usable. To get Mingo, we need to visit the Mingo website to get the appropriate version for our machines. The second step is downloading the desktop application in macOS, windows, and Linux.

Connect Mingo with the FerretDB instance

Open Mingo and click “New Connection” on the welcome screen to set up a connection. That way, you should be able to set up your connection. Proceed to connect FerretDB Mingo using MongoDB URI.

Basic operations

Like when using MongoDB, existing databases in the FerretDB instance will be displayed in the list of available databases on the Mingo screen. Alternatively, you can create a database and add collections by clicking “Add Collection.” Going forward, you can execute any operation as you would when using MongoDB on Mingo.

Advanced operations

FerretDB can perform complex operations using aggregation pipelines to transform data. This cements the fact that it is an open-source alternative to MongoDB. Other than aggregatecount, and distinct aggregation pipeline commands, FerretDB supports the following aggregation pipeline stages:

A list of the supported FerretDB commands can be found here.

Finally, FerretDB also supports unique index creation. It is another advanced operation you can perform in these fields.

Today we demystify how Mingo can ease your pain as a MongoDB user. Mingo has had positive reviews from all companies that have tried it out. Some users have loved it for its wide range of features that are not in other GUI tools, while others have remained loyal to us due to our favorable pricing plans. While Mingo enables you to visualize, analyze and interact with MongoDB data, it is essential to understand how you can leverage MongoDB charts; an in-built feature in Mingo.

Setting the stage: An introduction to MongoDB Charts in Mingo

MongoDB Charts is a feature on Mingo, a GUI tool for MongoDB. With MongoDB Charts, you can create visualizations from your MongoDB data. That way, valuable insights can be generated and open up new possibilities. Mingo allows you to unlock the full functionality of charts off the fly. You can connect Mingo to your Localhost and create interactive charts and visualizations directly from Mingo. The integration allows you to explore and visualize your data using a unified interface seamlessly.

Here are a few ways in which using MongoDB Charts (widgets) in Mingo can propel your company forward, enabling you to make informed decisions:

Simplified Data Visualization

With MongoDB charts in Mingo, you get a user-friendly and intuitive interface that allows you to easily create compelling visualizations of your data. MongoDB charts first simplifies data visualizations through predefined chart types. Some pre-defined chart types are bar, bar timeline line, line timeline and doughnut charts. You can choose the appropriate charts depending on your needs without building charts from scratch. The feature also enables automatic aggregation.

MongoDB Charts aggregates data based on the selected fields and the chart type during chart creation. The need for a manual pipeline is eliminated as it aggregates data intelligently. Calculations are also done hence saving time for users. It facilitates collaboration within your team and easy decision-making.

Real-time insights

Today’s business environment is fast-paced, and it is in your company's best interests to have tools that increase efficiency. Mingo gets things done 65% faster, not to mention the blazingly enjoyable user experience. It will therefore offer a leg up for your company when data-driven decisions need to be made in real-time.

MongoDB Charts offers you real-time visualization capabilities. That way, you can visualize your data on the fly as it updates. This will enable your organization to respond swiftly to the changing market conditions, customer preferences and address all operational challenges. Once instant insights are obtained, data-driven decisions can propel your company to greater heights.

Customizability and flexibility

We are aware that every business has unique data requirements and visualization needs. This is among the many reasons we incorporated MongoDB Charts with Mingo. MongoDB Charts offers various customizable options for your visualizations that fit your business needs. It does not use a “shoe fits all approach.” You can choose from line charts, bar charts, bar timeline charts, line timeline charts and doughnut charts. Additionally, you can customize these charts to your liking. Features like appearance, color schemes, and labeling style may be set up to meet your branding style.

This flexibility is significant as it allows you to present data in a way that resonates with stakeholders. Thus, their understanding is enhanced and will play a part in propelling your company to new heights.

Parting thoughts

We cannot overemphasize MongoDB Charts' role in propelling your business to greater heights. Efficiency trumps everything in the modern business environment, as you can get much done in less time. This saves up on resources and the time used to build crucial business aspects. The advanced analytic insights, such as built-in aggregation pipelines, will aid in performing complex visualizations. Deeper insights can thus be uncovered and data correlations explored.

At Mingo, we offer you all these features under one roof. If you have not joined the movement, consider checking out our application.

MongoDB charts are used to create a visual representation of MongoDB data. With MongoDB charts, you can communicate insights clearly and concisely. The feature will be useful when analyzing trends, presenting information, or making data-driven decisions. Complex information is conveyed in a more accessible format. That way, you can focus on developing actionable plans.

Mingo is a flexible and user-friendly tool for generating eye-catching charts in data analytics. Mingo is a powerful Graphical User Interface (GUI) tool with a wide range of features, among which it helps you set up professional charts. This article guides you in setting up MongoDB charts in Mingo, enabling you to create compelling visualizations for your data.

Charts you can set up on Mingo

  1. Bar charts: A bar chart is also known as a bar graph. It displays information in rectangular bars. Bar graphs have a horizontal and vertical axis. You can use the horizontal axis to represent categories or groups being compared. These categories could be anything you want to analyze, such as products, days, or houses. The vertical axis, on the other hand, is representative of the values or magnitudes associated with each category. Thus, the height corresponds with the value or quantity being represented. In Mingo, you are required to include the title of your bar graphs. You can use these charts to visualize comparisons between categories, identify trends or patterns, and render information in a way that makes it easier to understand.
  2. Bar timeline charts: You can also visualize data using a bar timeline chart when using Mingo. The charts combine a bar chart's concepts but incorporate a timeline in the horizontal axis. This chart will come in handy when you need to illustrate the duration, progress, or scheduling of events over time. We recommend Mingo to project managers as it offers visual representations of the timeline, durations, and overlaps, making tracking a project’s progress easy.
  3. Line chart: Mingo also lets you visualize your data in a line chart. A line chart, also known as a line graph, displays a series of data points connected by a straight line. You can use the line chart to represent visual trends, changes, or relationships between variables over time or other continuous variables. In a line chart, the horizontal axis represents the independent variable, while the horizontal variable represents the dependent variable. Mingo allows you to visualize how the dependent variable changes with respect to the independent variable. Like any other chart, Mingo will prompt you to offer a title for the chart.
  4. Line timeline chart: This visualization tool combines the features of a line chart and a timeline. As such, it has data points along a chronological axis. In this type of chart, the horizontal axis represents the timeline on a chronological scale. This progression could be a range of hours, days, weeks, months, or years. The vertical axis, on the other hand, represents specific data at a particular time. These data points are represented using markers or dots and are connected using a line.
  5. Doughnut charts: A doughnut chart is circular with a hole at the center. It is a variation of a pie chart and displays data as proportional segments of a circle. The circle’s outer circumference is partitioned to represent different categories or data values. You can use these charts to compare the relative sizes of the categories relative to the entire portion. That way, data distribution can be highlighted, making it a great visualization tool.

Getting started with Mingo

Before we delve into chart creation, let us familiarize ourselves with Mingo’s features and have Mingo set up in your working environment. Here’s a step-by-step guide to get started:

Creating charts in Mingo

Mingo charts are popularly referred to as widgets. Now that your data is ready, we will explore creating charts in Mingo. Follow the following steps to create charts in Mingo:

  1. Navigate to the dashboard and open the chart widget editor.
  2. Once the chart editor is open, select the title of your chart, database, collection and the chart type. Here, you can also tweak the color of your chart to your preference. Several alternatives are available in Mingo, such as bar charts, bar timeline charts, line charts, line timeline charts, and doughnut charts. To choose the best chart, consider your data type and the narrative you want to tell.
  3. Click the “Next step” button. This opens up the next page of the chart widget editor. Depending on the structure and use of your data, determine the X-axis and Y-axis values, data labels, and other crucial characteristics.

 Advanced charting techniques in Mingo

In the face of data analysis, Mingo offers an all-encompassing solution for setting up MongoDB charts that are informative and visually captivating. Following the outlined steps enables you to harness the power of Mingo’s charting capabilities. That way, you can transform raw data into visually compelling stories.

Create charts that resonate with your audience using Mingo’s features.

Data modeling is the process of defining how data is stored and the relationships that exist among different entities in your data. The organization of data inside a database is referred to as a schema. When developing your schema, put more thoughts on your application than your database. A good data model makes data management easier, makes queries more efficient, uses less memory and CPU, and reduces costs. As such, data that is accessed together is stored together.

With data modeling, all data components of a system are identified, and their connections are determined, making it possible to come up with the best ways to represent the relationships between them.

Components of data models

There are four components of data modeling, namely:

MongoDB’s flexible schema should not be confused with its being schemeless. For this reason, you need data modeling or design schema. A solid data model will boost the performance of your application and make it more future-proof.

Ways to model relationships between entities

Flexible data modeling with Mingo

MongoDB’s flexible schema enables you to change your data's structure to suit your application's needs. However, it is best practice to envision data modeling before constructing the database. This is possible by evaluating your application's business requirements and structure.

Mingo allows you to model your data through the data relations feature. The data relations functionality can be unlocked by right-clicking the database and selecting it or clicking on it on the left-hand side of the Mingo application. Once opened, you can view all the relations between your collections. You also have the option to refresh your relations and remove all relations. The field path for the relations is also indicated, and you get to glimpse your data relations in one place.

There is also an “add a relation” functionality that allows you to edit the existing relations. This can be done by referencing collections, deleting, or managing all relations. That way, you can dictate the structure of your database with the Mingo GUI functionality. Through referencing, you can have separate collections for each entity and establish relationships between them using unique identifiers.

Mingo makes MongoDB easy to use, whether you are a software developer, database administrator, system administrator, DevOps engineer, or data scientist.  Try it with Mingo, the fastest MongoDB GUI that gets things done 65% faster.

Indexes are unique data structures that store a small portion of the collection’s data in an ordered form that is easy to traverse and search efficiently. They point to the document identity and allow you to look up, access, and update data faster. In MongoDB, indexes are used to improve query performance. Indexes improve query performance by speeding up queries, reducing disc I/O and reducing the required resources. Additionally, they support queries such as equality matches, range-based operations, and return sorted results.

MongoDB's most common index types are single-type indexes and compound indexes. There are also multi-key indexes that operate on an array field.

Follow ESR (Equality, Sort, Range) Rule

When using compound indexes, you should apply the ESR rule to determine the order of fields in the index. Compound indexes refer to indexes with different fields and can improve query response time when used appropriately. Always start by adding the fields against which “Equality” queries are run. In this context, “Equality” refers to an exact match of a single value. As such, the index keys for exact matches must come before any other index fields.

The following fields to be indexed should reflect the Sort order of the query. Since “Sort” determines the order of the results, it’s only logical to have it after equality matches to reduce the number of sorted documents. The last fields represent the “Range” of the data to be accessed. It comes after the equality matches to reduce the number of documents scanned. Therefore, you should narrow the range to the smallest possible interval with the desired documents to improve efficiency.

Leverage covered queries whenever possible

Covered queries can be satisfied fully using an index without needing to access the documents. For an index to cover a query, all fields in the query are part of an index. Also, all the fields returned in the results are in the same index, and no fields in the query are equal to null.

The efficiency of covered queries is because they return results from an index without accessing the source documents. This reduces disk I/O, consequently minimizing the data that needs to be transferred over the network. That way, you can improve your overall MongoDB performance.

Deleting unused or redundant indexes

It goes with no saying that deleting unused or redundant indexes can improve MongoDB query performance. Mingo, the leading GUI for MongoDB, is exceptional at identifying unused or redundant indexes. Indexes come with a write performance cost. During query execution, MongoDB considers all relevant indexes and chooses the most optimal index. There is increased overhead in the selection process when there are unused and redundant indexes. Therefore, deleting unused and redundant narrows down the choice for MongoDB, making it focus on the most efficient indexes. This, in turn, increases the query performance.

Mingo offers a simplified way of finding unused indexes in MongoDB. It also offers a simple way to drop unused indexes with one click. Additionally, the chances of dropping the wrong index are reduced since the indexes are highlighted in the “No. of uses” section.  The “No. of uses since” section offers additional information on the frequency with which queries in the database utilize indexes. As such, this Mingo section helps you understand an index's significance in your database. This is because regularly used indexes show that they play a vital role in query execution. Mingo also provides you with zero or low usage indexes that end up slowing down the operations. These are the indexes you need to operate to improve query performance.

Substitute case-insensitive regular expressions for case-insensitive index

Case-insensitive indexes are faster than case-insensitive regex and hence should be used. This applies if the sole reason for using regex is case insensitivity. Case-insensitive indexes allow the searching and sorting of text data without regard for their cases. That way, matching documents can be identified based on the identified values. The cost of data computation when sorting text data can also be expensive when the index is case-sensitive. A case-insensitive index, however, will enable you to perform sorted queries without additional operations. As such performance of the queries is enhanced that, involves sorting by test fields. Overall, it is best practice to use case-insensitive indexes as it impacts other factors that are not performance related.

Visualize Index coverage with Mingo

Are you a developer, database admin, data analyst, DevOps engineer, data scientist or system administrator? Mingo is a powerful tool that you can leverage and will not just solve your indexing issues. It has features beyond index coverage visualization that you can use to optimize your MongoDB deployment.

Once you connect to your MongoDB deployment, you should pick out the database you want to analyze on your left-hand side. The databases are below the “New Connection” section. Then select the specific collection for analysis. Under the indexes tab, multiple selections can help you optimize your MongoDB performance. You can visualize the data distribution and have clear instructions on how the indexes cover your data. The effectiveness of the indexes can be determined with ease. That way, you can identify the areas that need improvements.

Are you satisfied with our recommendations? I bet you are. On that note, we strongly urge you to try out Mingo. We assure you that there is no harm in doing so. We are here to simplify your work and increase efficiency while at it. Mingo allows you to visualize your database and make database relations easy. Not to mention the great user experience without having to worry about the security of your data.

Don’t hesitate to take the first step and see the positive impact of Mingo.

Those who come bearing advice are rarely popular. Daedalus did not do his son Icarus any favors by warning him not to fly too close to the sun as his wax wings would melt, causing him to fall. Had Icarus not ignored his father’s advice, he would not have plummeted to his death. By all means, you are better than Icarus.

The technology landscape is ever-evolving, and companies seek robust and flexible database solutions. MongoDB is among the solutions that have gained traction over the years. It has more than 40800 customers in more than 100 countries. The software has been downloaded over 365 million times, and there have been over 1.8 million registrations for MongoDB University courses.

In a landscape filled with various databases, I will elucidate why MongoDB should be your option for database management needs. From start-ups to multinational corporations, organizations over the years have leveraged the power of MongoDB to optimize their database management needs. It has become an indispensable asset for modern businesses.

MongoDB Achievements in Fourth Quarter Fiscal 2023

This article delves into some of the top companies using MongoDB. Let’s explore some companies that have settled for MongoDB and how it’s propelling their business needs.

  1. eBay

eBay is an e-commerce platform founded in 1995 on the premise that humans are good. Currently, it connects millions of buyers to sellers. It offers buyers the products they seek while generating revenue through transaction fees, listing, and advertising fees.

The e-commerce platform, as of 2023, has 187 million active users within 190 serviceable locations globally. The large number of users necessitates an efficient way to store and manage data. MongoDB was instrumental in streamlining this task at eBay. eBay uses MongoDB as a search suggestion feature, enabling customers to find products they are searching for easily.

Other products the company runs via MongoDB are merchandizing categorization, cloud management, and metadata storage. In eBay’s shopping cart, a MongoDB store stores the entire cart contents for a user’s cart.

  1. Forbes

Forbes is a global media company that has evolved into a digital-first platform for publishing numerous articles and interviews on various topics. It publishes an average of 250 articles daily and drives traffic of about 70 million unique users monthly. In 2019, the company shifted to MongoDB Atlas and Google Cloud. Because of MongoDB's document model, developers can now create new features more quickly, incorporate changes more simply, and manage a wider variety of data types.

Delivery of the CMS was possible in under two months. The modernized architecture increased interaction with Forbes' content by enabling new initiatives like a vast network of new contributors and real-time social media analysis. Before the intervention, the website was slow, but now it is fast to serve up pages. As Forbes’s readership has experienced growth, the MongoDB-powered CMS has also been growing. This is because scaling is built into the database. It is automatic and transparent.

  1. Toyota

To be number one in Industry 4.0, Toyota had to incorporate MongoDB Atlas, a fully managed cloud service offered by MongoDB. Toyota created a smart factory by moving from Monolith to Microservices, Microsoft Azure, and MongoDB Atlas. Filip Dadgar, the Principal Solutions Architect and IT Manager at Toyota Material Handling has been at the forefront of championing MongoDB’s use to solve the company’s problems.

After its implementation, he asserted that MongoDB’s data model was the most beautiful part. The developers find it easy to work with MongoDB; hence tasks can be completed quickly. That way, the organization spends time building business value rather than data modeling. Toyota has benefited from the access to MongoDB University and claims that the people there have always been helpful. There have also been great relationships with the MongoDB teams.

Toyota Financial Services have also thrived due to the use of MongoDB Atlas. TFS operates in over 40 countries as a subsidiary of Toyota Motor Corporation. After incorporating MongoDB Atlas, the time spent on database management has been reduced by 40%. This has helped the company diversify from a traditional finance lending firm to a digital financial services provider.

  1. Vodafone

Vodafone is a multinational telecommunication company with over 600 million global users. Since 2020, the company has been working on its Tech 2025 plan to shift from telecommunications to Teleco-as-a-Service (TaaS). Its focus is mostly on software and IoT. For this to be successful, the company had to incorporate MongoDB Atlas. Felipe Canedo, Head of Engineering Transformation, Vodafone, claims that MongoDB Atlas has offered developer speed and agility. That way, they can reuse assets in different countries. Vodafone can scale easily with MongoDB Atlas and achieve maximum availability without worrying about technical difficulties. A million new connections are made monthly, making Vodafone's IoT ecosystem the largest globally, with over 140 million linked devices.

  1. Apna

Apna is India’s number 1 Jobs App. It is a networking platform that connects job seekers to employers. It operates in 74 cities and is used by big-name companies in recruitment. The company has over 30 million users, necessitating a high-performing and scalable database. Apna resorted to MongoDB Atlas as it could handle millions of user data without failure. The organization settled for MongoDB Atlas because it is user-friendly, supports auto-scaling, and has a flexible data model. It is also the primary data source for the CMS and facilitates every interaction on the application. These interactions include but are not limited to searching for job listings and inviting individuals to apply for roles. The company has praised MongoDB positively and recommends it to other start-ups. Apna was shocked they did not face teething problems when implementing the new technology.


The demand for NoSQL databases like MongoDB is set to skyrocket. MongoDB Atlas specifically opened unlimited possibilities for MongoDB. For this reason, whether you are running a start-up or a multinational, you should not shy off from using MongoDB. As if that is not enough, Mingo offers you simplified management for your MongoDB databases. Administrative tasks are streamlined, and you can focus on developing business value.

Here's a short list of bug fixes and improvements in our latest release. It is definitely not complete, there are many minor goodies we didn't even mention here. Enjoy!

Bug Fixes


We would like to thank all the contributors for their valuable feedback and bug reports. Your contributions help us make Mingo better. Please continue to report any issues or suggestions you may have for future releases.

It is plain as day that MongoDB has been a popular database solution for many applications in the recent past. One may argue this is due to its flexibility, scalability, and ease to use. However, Organizations are faced with opting for MongoDB Atlas, a fully managed cloud-based database service, or having MongoDB set up and hosted on their infrastructure.

MongoDB Atlas: The Managed Cloud Solution

MongoDB Atlas is a fully managed cloud database service offered by MongoDB Incorporation. It is a fully paid version of MongoDB and integrates natively with Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). As a developer, MongoDB Atlas’ Database-as-a-Service (DaaS) model enables you to use a robust document data model for high availability, scalability, and security with a unified query interface. In a nutshell, the main benefit of MongoDB Atlas is simplifying database management. The following are a few of the main benefits of using MongoDB Atlas:

Pros of using MongoDB Atlas:

Ease of use and deployment

MongoDB Atlas allows developers to focus on their applications rather than the infrastructure of their applications. Setting up, managing, and scalability of your MongoDB databases will not be part of your routine. It worsens if you have to be woken up at 3 am twice a month because of a database outage. Such problems can be avoided by having MongoDB Atlas get the job done. The ease of use also affects the platform’s ability to offer seamless scalability. That way, organizations can adjust their cluster size to accommodate the varying workloads. The platform provides a user-friendly interface and automation tools that streamline the deployment process and everyday operations.

Security and Compliance

Security is on top of the list of offerings by MongoDB Atlas. If you are worried about a third party digging into your data, MongoDB Atlas is your go-to solution. It complies with industry standards like SOC 2, HIPAA, and GDPR. That way, if your organization has strict regulatory requirements, the platform is the ideal fit for you. Some of the security features applied by MongoDB Atlas are encryption at rest, encryption at transit, and fine-grained access control. Also, it allows you to configure network access controls to restrict incoming connections to your database cluster.

Backup and Recovery

Backup and recovery are non-negotiable features when handling any form of data. Data loss could be devastating to an organization, so MongoDB Atlas offers automated backups with point-in-time recovery. The process is also user-controlled, as one can schedule backups. The recovery mechanism is also simplified hence making the platform data-loss-proof. Additionally, MongoDB Atlas includes backups in the price, so there's no need to pay extra to lease dedicated servers to store backup copies.

Cons of using MongoDB Atlas:


The major downside of using MongoDB Atlas is that the outstanding features discussed above come at a cost. It is upon your organization to determine whether the convenience offered by the platform supersedes the cost of the service. MongoDB Atlas charges per hour of cluster usage. The charges vary based on your cloud provider implying using AWS, Azure, or Google Cloud will attract varying costs. Also, cluster size, cluster region, data transfer costs, and types of services added play a role in determining your cost. Additional services include backups and restores, custom storage speed, and advanced training or support plans.

Limited control and dependency on the cloud provider

If the cost of the MongoDB Atlas plan is not a deal breaker for you, the limited control and dependency on the cloud provider should not be a deal breaker either. However, for what it's worth, you will have to work with the available infrastructure. There is restricted access to specific configurations. However, this is only a problem whereby businesses have specific requirements for their database environment. Additionally, if there are any disruptions on the provider’s end, you will be equally affected.

Self-Hosted MongoDB: Taking control of your database

Unlike a managed cloud solution, self-hosted MongoDB requires deploying and managing a MongoDB instance on your infrastructure or a chosen hosting provider. There is much control with this alternative, especially if you create your infrastructure. However, additional effort and numerous hours are spent on database management, which could have otherwise been used in other routine activities. While we are here, we might as well explore the pros and cons of this approach:

Pros of Self-Hosted MongoDB

Cost efficiency

Cost efficiency when using self-hosted MongoDB is a controversial factor. However, self-hosted MongoDB may be the best alternative for small-scale applications and organizations. For instance, if you have a limited budget or run operations by yourself, it is essential to avoid costs associated with MongoDB Atlas. Also, at this point, the data is not as bulky, making it easier to manage. You can find cost-efficient shared hosting for your business and then embrace the self-hosted MongoDB path.

Flexibility and data sovereignty

You will enjoy flexibility with self-hosted MongoDB because there are no pre-defined infrastructures. It is, therefore, an excellent alternative for a business specific to how it wants its data aligned. You can also control hardware requirements, network configuration, and security requirements. You can optimize the database environment to meet specific performance requirements. While at it, if your organization has strict data sovereignty requirements, self-hosted MongoDB is your go-to option. That way, you can have complete control over the location of your data.

Cons of Self-Hosted MongoDB

Administrative overhead

As stated above, with self-hosted MongoDB, a proportion of hours will be invested in database management. There is a need for skilled personnel to engage in tasks such as hardware provisioning, software installation, configuration, monitoring, and maintenance. Organizations must allocate resources and expertise for this responsibility to be executed effectively.

Backup and disaster recovery

Organizations need to ensure there are reliable backup and recovery systems in place when using self-hosted MongoDB. Once again, this is putting person-hours into business aspects that a third party can execute. Any system error may be detrimental to the organization. Schedules need to be maintained; replication needs to be managed, and testing recovery procedures in place.

Scalability and availability

This process is more complex when using a self-hosted MongoDB than MongoDB Atlas. Hardware needs must be considered, which could have been avoided by using a third party for the service. Other factors that must be factored in are sharding configuration and load balancing. All these interventions are in a bid to attain availability and fault tolerance.

The Decision Dilemma: Self-Hosted vs. MongoDB Atlas

The truth is that there is no definitive “right” or “wrong” answer when comparing MongoDB Atlas and self-hosted MongoDB. The choice of the approach taken solely depends on the needs of the organization. This is because both options have advantages and trade-offs; hence what may be ideal for one organization may not work for another. You can choose the choice that best meets your organization's needs by being aware of the significant differences. It would be best if you also took into account aspects like deployment ease, scalability, maintenance requirements, and cost-effectiveness.

MongoDB is among the most popular NOSQL databases currently. It’s no secret that developers working on high-performance applications ally with MongoDB. It is a document-based database that is highly scalable and hence handles large amounts of data. 

MongoDB can handle data ranging from small data sets to large and complex stacks of information. Whether working on a small database or dealing with vast data, MongoDB is for you. However, to achieve optimal performance and scalability, you need to optimize the performance of your database. In this article, we will discuss MongoDB performance optimization techniques.


By facilitating speedy data discovery and retrieval, indexes can considerably enhance the performance of MongoDB queries. In essence, an index is a data structure that MongoDB utilizes to optimize query execution. It functions by building a map of the values kept in a certain field, which makes it easier for MongoDB to find documents that fit a query criterion.

When an index is created, MongoDB creates a data structure that keeps references to the related documents and sorts the field's values. MongoDB can use the index to find the documents that match the query condition without scanning every document in the collection when a query is run against that field. 

To optimize MongoDB performance, you should create indexes on fields that are frequently used in queries. The performance of the queries will largely improve, and there will be reduced response time. MongoDB can use the index to locate the documents that match the query quickly.


Sharding is a MongoDB feature that enables you to partition data horizontally across multiple servers. The servers are also known as shards. As a MongoDB performance optimization technique, it leverages horizontal database scaling. Sharded data is split into small chunks and distributed across multiple instances by MongoDB. That way, each instance is responsible for specific data. However, sharding may not be necessary if the data set is relatively small.

When using sharding, you start by defining a shard key. A shard key determines the distribution of data across shards by MongoDB. Choosing the shard key should be well thought out to ensure an even data distribution. You can then add shards to your cluster and configure MongoDB for automatic data distribution. MongoDB will then balance the data across shards to ensure no shard is overloaded or under-utilized. This MongoDB performance optimization technique is effective for read-heavy workloads. It increases the throughput and reduces your queries’ response time.

Connection pooling

A connection pool refers to a cache of the driver's open, ready-to-use database connections. Connection pooling helps improve the performance of a MongoDB connection. Whenever a client connects to MongoDB, a new connection is established with the database. This can be time-consuming since a new connection is established for every query leading to poor performance.

By connection pooling, you can create a pool of connections that clients can reuse. That way, whenever a client sends a query, it uses an existing connection from the pool rather than creating a new connection. This technique will significantly improve your database's performance and response time. In other words, a connection pool reduces application latency and the frequency of creating new connections.

Replica sets

Replica sets are useful when improving MongoDB databases' availability and scalability. A replica set is a group of MongoDB instances that contain the same data. One instance is designated as the primary instance, and the others are secondary. The primary instance handles all write operations, while the secondary instances replicate the data from the primary instance.

Replica sets can greatly improve the availability of your database. If the primary instance fails, one of the secondary instances can be promoted to the primary instance, ensuring that the database remains available. Replica sets can also improve the scalability of your database. You can leverage this technique by adding more secondary instances. That way, the read capacity of your database can be increased.


You can use profiling as a technique to analyze the performance of MongoDB queries. As a developer, it allows you to identify queries that take a long time to execute and optimize them for better performance. MongoDB provides a built-in profiling feature that can be used to analyze the performance of queries.

To use profiling, you can enable the profiling feature and set the profiling level. MongoDB provides you with three levels of profiling: off, slow, and all. The slow-level profile queries take longer than a specified threshold, while the all-level profiles all queries. Once profiling is enabled, you can use the profiling data to identify slow queries and optimize them for better performance.

Bonus Tip

Mingo is a powerful GUI that can help you optimize MongoDB performance and scalability. Its features and functionalities include query optimization, index management, sharding, and security management. It is a valuable tool that will help you save time whether you are a developer, database administrator, or system administrator.