MongoDB Atlas for Government

MongoDB  does well in being able to access our network devices and keep logs and reporting—that's about it.

I would recommend MongoDB  as part of a template if anyone is considering free and open-source templating services such as LibreNMS , but as a standalone, I couldn't advise.

MongoDB has definitely helped us improve our network monitoring and reporting dashboard, so I would say it has impacted our operations positively overall.

I'm not sure about the documentation or the knowledge bases available for MongoDB because I don't interact with it at that level, but I would say it's minimal and could be improved.

I am not experienced with MongoDB enough to know any pain points or areas they could improve.

Nothing else comes to mind at this time that could be improved.

We deployed MongoDB about five years ago and it has been in operation since then.

I was not a part of the initial setup or deployment of MongoDB.

One person was involved with the setup team, and it took just a few days to deploy it.

Overall, on a scale of one to ten, I would rate MongoDB an eight; it's mostly because we're still running a monolithic environment on old hardware, so there are some limitations with read-write access.

At this time, I'm only looking into Cisco or Linux or other solutions out of curiosity about possibly switching to it, but currently all that we use are LibreNMS  and Splynx.

From what I know, I would say the initial setup of MongoDB was pretty straightforward.

On LibreNMS, they have a template for setting up the environment that includes all the services, so MongoDB is just part of that template, meaning they weren't really too hands-on with setting up MongoDB itself.

One person was involved with the setup team, and their job title was Network Operations Engineer.

I'm familiar with open-source databases such as MongoDB, and I don't think it's Grafana , but it's similar to Grafana , though I'm trying to think of what it's called.

I'm not entirely sure about the main differences between MongoDB and other open-source databases that I've used.

We haven't really delved too much into looking at comparisons for databases.

MongoDB is not currently supporting our AI-driven projects nor do we use it along with AI at all.

I don't know how MongoDB's document-oriented model has benefited our management processes; that's beyond my expertise.

I don't have experience with QRadar or Auvik or similar products.

I'm familiar with some Linux tools, just things such as smokeping, which we use implemented in our LibreNMS environment.

I'm only an operator, so I don't actually spend a lot of time developing MongoDB, thus I'm not sure what the best features are.

I would rate MongoDB an eight out of ten.

On-premises

I am not a partner of MongoDB; I am just a customer.

I do not use MongoDB in AI projects; only CosmoDB is used for AI projects, as MongoDB is an old pattern for us, and the new workload in AI is for a new pattern, which is CosmoDB for AI apps.

I would recommend MongoDB because it is a good pattern and a good product for legacy; for us, MongoDB is for legacy databases and legacy apps, and in this scope, it is a good pattern and a stable database engine; however, for new deployments and new applications, CosmoDB is a better engine.

My experience as a partner with Microsoft is very good because we have been a partner for three or four years, and it has been a very good experience.

MongoDB may have advantages over Cosmos DB perhaps in metrics because you can make some dashboards with database metrics, and there are many tools in MongoDB for dashboarding that are perhaps better than CosmoDB.

The dashboards in MongoDB have more functionalities; for example, you can create a dashboard with MongoDB database data, and it is simple to create, such as some sales dashboards, while I do not see this functionality to rapidly create such dashboards in CosmoDB.

While MongoDB is a good product, it is also an expensive product for support, and its scalability is acceptable, but the big problem with MongoDB is the cost.

For security in MongoDB, we work with encrypted databases by default, but we have not contracted the security options in our contract because it is too expensive, so we only implement encrypted databases without the security pack, which is very expensive for us; in security, we are at the first steps, just using encrypted databases.

I think additional features needed in MongoDB include perhaps vector databases, as I think they are not supported right now.

I have been working with MongoDB for five years.

The scalability in MongoDB is limited because we only work with ReplicaSet with two servers, and in comparison, the scalability in CosmoDB is much better than the MongoDB ReplicaSet models; although you can set the auto-provisioning of a node in ReplicaSet, it is very expensive, and we have to work with manual scalability in MongoDB.

The performance of MongoDB is good, especially in a ReplicaSet model, but if you want to pass on to another model, for example, Sharding models, it is very complicated; in ReplicaSet, it is acceptable, but if your workload needs more performance, and you must pass to a Sharding model, it is complicated in MongoDB, whereas in CosmoDB, it is simple.

We have seen a little ROI, and we want to target CosmoDB for this return on investment because it is the better model for this feature; however, with MongoDB, it is difficult to calculate the return on investment, as it is too expensive for our use.

We pay approximately 2,000 euros per month for MongoDB.

This solution receives a rating of 7 out of 10.

What do you like best about the product?

The mongodb GUI is very good to view the collection and manage to the databases

What do you dislike about the product?

The GUI of we cannot see the user details

What problems is the product solving and how is that benefiting you?

Easy to edit collections

One of our business units uses MongoDB , and we developed an ETL pipeline that extracts data from MongoDB  and transfers it into our data warehouse.

MongoDB is a NoSQL database that is similar to a document database. It offers flexibility in schema adaptation, allowing us to change the schema and add new data points. Additionally, it scales up easily with low memory requirements. This makes it suitable for our data management needs.

There is room for improvement in integrating MongoDB with agentive AI solutions. While solutions for other databases like SQL or PostgreSQL  already exist, MongoDB requires additional integrations for developing AI solutions.

I have about four years of experience working with MongoDB.

The deployment process was straightforward.

MongoDB is highly stable, and I would rate its stability at nine out of ten.

MongoDB is highly scalable. I would rate its scalability nine out of ten.

We use the open-source version of MongoDB and manage it ourselves, so we have not contacted their technical support.

Neutral

Before using MongoDB, we used IBM DB2 . We switched to MongoDB to develop a composite system that includes both SQL and NoSQL databases.

The initial setup of MongoDB was a straightforward process.

We use the free version of MongoDB, so there are no licensing costs.

Based on my experience, I would recommend MongoDB to others. Its usage depends on specific use cases. MongoDB is suitable for document database needs. I would rate MongoDB as eight or nine out of ten, and I would rate the overall solution the same.

Public Cloud

Other

What do you like best about the product?

I have nothing good to say about MongoDB Atlas.

What do you dislike about the product?

My story with Mongo began when I started a new software position, and they had a legacy version of their software product using Atlas.

Compared to our other infrastructure bills, Mongo was significantly higher for the amount of compute and storage we used ($3K per month). This is a managed service, so you would expect to pay a premium. Ok, sure, but then I expect great functionality, performance, and support.

The main problem began with Mongo when we needed to delete some data because they tie the CPU and memory tiers to storage size, so we were overpaying. Our application would run fine off an M10 dedicated cluster (the smallest tier), but it had automatically scaled to an M50 because of storage. This is already a bit disappointing because they are forcing customers to pay for more compute and memory than they need.

So we started deleting some data, but then we ran into problems. The data deletion process was really slow and also slowed our entire cluster down, causing lag and performance issues for our end users. But hang on, this makes no sense because we are paying for more CPU and RAM than we need, so why would we have this issue?

It took us three months to delete 500GB of data. In the meantime, our bill remained the same because you can't claim the space back without compacting the database. Ok, fine. So we ran compact(), but we only freed ~100GB on the secondary clusters.

Support gave us a script to run that can see how much storage can be freed.

In the end, we had to activate an expensive additional support plan costing us $500 USD per month to get support to run a re-sync command. This should have taken their support people 10 minutes, but instead, they mucked us around going back and forth on the ticket, taking three weeks to resolve.

A year later, we needed to delete some more data. We spent another five months deleting 800GB of data. Then we ran compact() and freed 300GB. Where is our other 500GB? We contacted some humans at Mongo, who really couldn't do much other than suggest we get funding to cover the $500 support for one month. Yes, we got the $500 credit, but when I went to reactivate support, it was going to charge us for three months for one month because Mongo retroactively bills you for three months when you reactivate. Wow, we started in a bad place, now I'm beyond frustrated; this is daylight robbery.

To this day, I am still fighting to reclaim some storage, but at this point, I'm going to recommend to our CEO that our dev team put some effort into moving away completely from Mongo.

I also need to mention that Mongo recommended we use their online archive features, but when we crunched the numbers, it was still quite expensive, and we would have to do significant work to make our application work between the regular clusters and online archive. So it was significantly more logical to just put the data in AWS S3, then delete it in Mongo.

If I can summarize my experience with Mongo, and I acknowledge mine is probably quite different to most, here it is:

Overpriced for the performance you get

Sneaky billing model where they tie CPU and memory to storage

Terrible and expensive support

Sneaky extra charges on reactivating support

Bad support escalation solutions - they couldn't just turn on free 'support'

Poor database performance

Slow delete operations

Ecosystem lock-in

Forced upgrades - no LTS releases

Let me sum it up this way: if your compact() command does not free up the space that is available on your cluster, then provide the customer with free support to do so.

I hate dealing with Mongo. Nothing is simple, everything is expensive, and the performance sucks.

If you are considering using Mongo, find something else. Even if you have to take a bit more time to learn AWS Dynamo, S3, or Aurora, you should do it; you will save time and money in the long run.

Mongo, you deserve this negative review. I have given you plenty of opportunities to resolve things and have escalated issues, but you just don't care.

We wanted to move away from Mongo before; now I can't get rid of it fast enough.

What problems is the product solving and how is that benefiting you?

A simple managed database to get up and moving quickly as a developer.