The good, the bad and the ugly – the story of MongoDB

MongoDB has about 2000 customers in the country, growing at 60 per cent year-on-year.
The good, the bad and the ugly – the story of MongoDB
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Any developer, data scientist or analyst worth their salt will swear by MongoDB’s superiority. MongoDB – besides NoSQL, Apache Cassandra, DynamoDB, RethinkDB, Redis, Firebase, and others – is one of the most sought-after NoSQL databases where data is stored in JSON-like documents with flexible schemas

So, what makes MongoDB so special? 

About 15 years ago – when two developers (Dwight Merriman and Eliot Horowitz), working on a project, struggled to insert the data, make changes to the database, and scale them quickly due to rigid schema, along with the generation of large quantities of data by systems/applications in real-time. Unfortunately, there weren’t many platforms to solve these things at the time. 

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That is when the duo, along with Kevin Ryan – the team behind DoubleClick (now owned by Google) – decided to solve the problem by creating a new data store, where the schema is flexible and the database is scalable. That was the start of MongoDB (2007) – made ‘by developers for developers.’ 

With time, a lot of developers started using MongoDB as a database. So in 2016, MongoDB launched Atlas, a database-as-a-service platform, and made it available on various cloud platforms. “Within the last few years, we have seen tremendous growth and adoption for Atlas as well,” shared Himanshumali, Solutions Architect Corp – APAC at MongoDB. MongoDB Atlas is a cloud-hosted MongoDB service on AWS, Azure, and Google Cloud. 

India sees massive adoption

MongoDB has about 1.5 million global University registrations. Out of which, 3,60,000 registrations are from India. The total number of downloads for MongoDB – the community version – stands at around 260 million downloads worldwide, and a major chunk comes from India. “Currently, in India, we have around 2000+ customers, and we are growing at a rate of 60 per cent YoY,” said Himanshumali. 

He said their platform had seen massive adoption by the Indian startup ecosystem across sectors, including fintech, healthcare, edtech, and others. Lately, the company has also seen many crypto and NFT companies using its developer data platform. Some of its customers in India include cure.fit, Vedantu, myBillBook, and others. 

Besides new-age startups, MongoDB also caters to traditional and legacy companies, including IT/ITES, banking and the financial ecosystem, where multiple use cases work on MongoDB. “One of the use cases that we try to highlight is mainframe offloading,” said Himanshumali, pointing at the document data model. 

Further explaining, he said that the way data in MongoDB is stored in the form of a document (NoSQL) rather than rows and columns (SQL), which the traditional and legacy databases are still using. This provides developers with a flexible schema, where as soon as they make changes to the application, it gets changed in the database. It also makes it easier for developers to write the code. “Because when you talk about MongoDB, scalability certainly comes into the picture,” added Himanshumali. 

The good, the bad and the ugly – the story of MongoDB

A special treat for Indian developers

Last year, MongoDB launched a serverless database which was made generally available (GA) only recently. On the analytics front, the company has launched a columnar index, which addresses in-app analytics in real-time. There is more in store. 

The latest columnar index store feature is extremely useful for tracking real-time analytics within the application, like fraud detection, real-time credit scoring, customer relationship management, etc. “The analytics node can be added on Atlas, we are enabling resizing that analytics node different from the rest of the cluster so that customers can have more flexible and sized analytics,” said Himanshumali. 

Besides these, MongoDB has also launched MongoDB Atlas Search, a full-text search solution that offers a seamless and scalable experience for building relevance-based features. This comes with faceted search, also known as faceted browsing or faceted navigation, a technique commonly used by eCommerce companies to help users analyse, organise, and filter large sets of product categories/catalogues based on size, clour, price, and brand. 

This new feature is very similar to Elasticsearch. It also happens to be one of the popular tools used in MongoDB. “We know that Elasticsearch has been addressing this market. We completely acknowledge the technology. It is a very mature product,” said Himanshumali. 

But the question is, how is MongoDB Atlas Search different from Elasticsearch? 

To this, MongoDB said that usage of Elasticsearch brings two distinct challenges. Firstly, it causes delays when moving data from their permanent storage to the search environment. Secondly, the added component to the architecture requires its own overhead, upgrade and maintenance. However, with MongoDB Atlas Search, users can not only write data and insert it into the database, but they can also create an index and search the status directly on the platform. 

Another interesting offering announced by MongoDB at its flagship conference MongoDB World 2022, held in New York, includes MongoDB Relational Migrator. This platform simplifies the process of moving workloads from relational databases to MongoDB. “Relational Migrator is a very important and useful tool for the market. We expect users to use it very effectively,” said Himanshumali. 

Currently, MongoDB allows users to reference (relations) while creating a data model; however, migrating from NoSQL (MongoDB) to SQL (Postgres DB, etc.) is always a challenge. Thanks to MongoDB Relational Migrator. It lets users not only integrate data, visualise and analyse schema, but it will also help them migrate. This is good news because it will make developers’ lives easier as they can migrate from a relational to a MongoDB environment. 

For India, MongoDB has planned various engagements for the developers’ ecosystem in the near future. One such engagement includes MongoDB User Groups (MUGs) – a platform that brings people together to learn and connect over their shared interest in MongoDB technologies. 

“There are various channels that we have for collecting developer’s feedback,” said Himanshumali, citing MongoDB Charts – a data visualisation tool developed based on the developers’ feedback. He said that this product is evolving on a daily basis, and they have been adding lots of capabilities and features on top of it. 

How is MongoDB Charts different from visualisation tools like PowerBI, Tableau, Snowflakes, etc.? “I wouldn’t say that we are a direct competitor to something like Tableau or PowerBI, which are much more mature tools in the market. But, it (MongoDB Charts) certainly does address the basic needs that we see in the market,” said Himanshumali. He said when you have data in MongoDB, you can quickly build visualisations in real-time, and many users have been benefiting from this new feature. 

Plans for expansion in India 

Currently, MongoDB has about 400+ employees in India, the largest compared to any other MongoDB region in the world in terms of headcount. “India is certainly a prominent region for our growth,” said Himanshumali. 

He said going forward, the company will continue to stick to their product philosophy and work towards enhancing productivity and making things easier and simpler for developers. “We are seeing the market grow, and we have multiple partners working here in India,” he added. MongoDB has about 2000 customers in the country, growing at 60 per cent year-on-year. 

In the coming months, MongoDB looks to host multiple localised in-person events and meet-ups to unleash the power of its platform across businesses. 

Why is MongoDB synonymous with NoSQL? 

As per the Stack Overflow developer survey, MongoDB is the database most wanted by developers. Besides MongoDB, Redis has been one of the most loved databases for five consecutive years. On the other hand, IBM DB2 remains the most dreaded database. 

The good, the bad and the ugly – the story of MongoDB

MongoDB’s document data model (NoSQL) is the key to everything they do. Himanshumali said that with the document data model and Atlas on it as a managed service on the cloud, they could simplify the integrity of the database for the users. So everything related to monitoring, backup, security, and encryption is taken care of by Atlas. 

“Developers can focus on their core development, rather than spending their time thinking about these things,” he added, pointing at the Innovation Tax report that MongoDB published last month, which highlights the challenges developers face, and explains some of the reasons behind slower innovation in organisations. 

Slowing innovation 

The report published by MongoDB surveyed 2,000 developers and IT decision-makers across Asia, including over 400 in India. It stated that in India, nearly 86 per cent found that working with data was the hardest part of building and scaling applications, and their single biggest technical challenge in application development was working with high volumes of data in different formats. 

Almost 94 per cent of developers said that building new, innovative applications and features is crucial for their long-term success. However, many organisations report being unable to spend their time on innovation. 27 per cent of them spend just as much time maintaining existing data, apps and infrastructure instead of building new value-added features or applications. 

In addition, 63 per cent of respondents said their organisation’s data architecture is complex, and 86 per cent found this complexity to be a limiting factor in innovation leading to businesses’ inability to enter new markets and meet new regulations. 

Mark Porter, CTO at MongoDB, said organisations are still leveraging complex and legacy technologies at the cost of the productivity of their development teams. He said leaders heading up digital transformation initiatives need to focus on deploying applications faster, iterating quickly, and predicting application deployment. 

Bridging the gap between cloud and digital transformation

MongoDB said 82 per cent of respondents agreed that digital transformation has made their data architecture more complex. When it came to the cloud, it was clear that some companies have had different experiences – where 71 per cent of respondents said moving to the cloud had helped simplify their architecture. In contrast, 21 per cent of them said that the cloud has made their data architecture more complex. 

In addition, MongoDB said that legacy data infrastructure was another identified setback, with 85 per cent calling it out as a hurdle to innovation. 

Sachin Chawla, vice president of India and APC at MongoDB, said that it is clear from this data that not every business is benefiting from the cloud. He said many organisations are taking existing infrastructure and lifting and shifting to the cloud and adding services as they go. 

So, what’s the solution for this? Chawla suggested that businesses should take a different approach. He said the four areas that can help businesses use application platforms effectively include:

  • Developer productivity.
  • Prioritising elegant and repeatable architectures.
  • Security and data privacy in a few clicks.
  • An uncompromising approach to deployment flexibility with a focus on multi-cloud. 

This is where MongoDB Atlas comes into play. The platform has all the capabilities – search, data lake, analytics, etc. “A complete platform. That is where the strength lies for us compared to our competitors,” said Himanshumali, emphasising the ease it offers developers. 

Limitations 

MongoDB currently leads with a 48.42 per cent share in the NoSQL database market. It competes with NoSQL (24.09 per cent), Amazon DynamoDB (9.75 per cent), and Apache Cassandra (5.42 per cent). “We are far ahead from any other NoSQL database,” said Himanshumali. 

The good, the bad and the ugly – the story of MongoDB

Lately, multiple cloud providers, including Amazon Web Services, Microsoft Azure, and Google Cloud, are coming up with data services similar to MongoDB to cater to business needs. “We are in a love-hate relationship with our cloud partners,” said MongoDB. 

MongoDB Atlas is a managed database platform currently available across all three cloud providers – i.e. AWS, Microsoft Azure and Google Cloud, in over 100+ regions. “All the three cloud providers stand with us, and we work closely with them. So, we require the platform, and we get it from them. So, we are happy with it right now,” said Himanshumali. 

Further, he said that they understand the value MongoDB brings to the table. “Together, we can work for the benefit of each other. I do not see a requirement for having to set up our own data centres,” he said when asked if MongoDB has any plans of setting up its data centres in the near future. 

Supporting MLOps, DataOps, AutoML, and AIOps 

Of late, there is a lot of buzz around MLOps, DataOps, AutoML, and AIOps, where most platforms use MongoDB to develop these applications. So the question is, will MongoDB also venture into creating such applications in the near future? 

“We would not like to be particularly tied to these terms,” said Himanshumali, throwing light on MongoDB Atlas Data Lake. He said that at MongoDB World this year, we launched Atlas Data Lake Pipeline, which helps customers to push data seamlessly into object storage without using any third-party tools or ETL. This offers very low-cost storage with respect to the database. 

Further, he said they have added MongoDB Atlas Data Federation, which combines data from MongoDB Atlas clusters, Atlas Data Lake and cloud storage (AWS S3) into virtual databases and collections. The new tool allows users to make queries across clusters in a single query. 

With these new capabilities, alongside its latest in-app analytics feature, MongoDB aims to address analytics, AI and ML from a data standpoint, enabling the market to perform these kinds of things (like MLOps, DataOps, AIOps, AutoML) on their data developer platform. 

MongoDB makes developers cry 

While MongoDB is a go-to platform for everything data, there is scope for more improvement and features. Analytics India Magazine spoke to a few developers to understand some of the pressing challenges they face when using the MongoDB platform. 

“Why does MongoDB not support the ‘delete cascade’ feature?” said Karthik Devaraj, a full-stack developer working for a tech startup in Bengaluru. Because, without this feature, it makes developers write their own delete triggers, which becomes really cumbersome. In other words, if the parent data gets deleted, the children data becomes an orphan; there is no readily available facility to handle this situation in MongoDB.  

“Very often, we face these queries in the market – why don’t we support delete cascade? Why don’t we support joints, etc.,” recalled Himanshumali, saying that MongoDB offers a flexible schema, a document model, where all that data can be stored in a single collection. This removes the requirement to a great extent. That is the core of the difference where MongoDB architecture-like schema becomes useful.  

He said when you have this kind of model, we often do not require such kind of behaviour cascade capabilities, and even joints are very rare. “We believe data accessed together is stored together,” said Himanshumali. 

MongoDB believes that if you can store those multiple data tables in a single collection, querying becomes much faster because you do not have to join multiple tables to get that data. “MongoDB is much more scalable, and the number of requests it can address is much higher than traditional platforms,” he added. In other words, MongoDB believes that there is no requirement for such a feature as ‘delete cascade,’ as the framework has been designed to eliminate such redundancies.

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Amit Raja Naik
Amit Raja Naik is a seasoned technology journalist who covers everything from data science to machine learning and artificial intelligence for Analytics India Magazine, where he examines the trends, challenges, ideas, and transformations across the industry.

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