MongoDB Reviews

MongoDB has the best support for database clients. And the Support for GeoJSON API and database transactions.

MongoDB database is perfect. It's the MongoDB compass needs some touches

Very fast installation and use. It has very complete tools to manage you, such as the Mongo Compass or the possibility of having everything in your cloud with Mongo Atlas. The installation is very simple and the power of the tool is incredible and integrates perfectly with our system built in NodeJS. It has a very detailed documentation which facilitates its integration.

The main cons is that it does not have the possibility of making queries with JOINS but this is a limitation of NO-SQL databases

I love how MongoDB integrates with code in such a way that queries look just like any other code. I also love how the documents from MongoDB are returned natively to Node.JS as objects without any mapping at all. MongoDB is an intuitive and very performant database, I recommend trying it if you haven't.

The big thing with MongoDB you have to get your head around is the whole redundant data thing - it is usually recommended that you store a lot of redundant data in your documents instead of having data normalised with relationships as you would in a relational DB. Have redundant data can make your query code really simple, but it can make your update code very complex / cumbersome. However, you can opt out of this approach and use MongoDB with relationships - I would recommend using Mongoose (an npm package) that will handle a lot of the work with relationships for you.

-Easy Setup
-Getting simple data in/out is painless & straightforward
-Basic analytics are easy, as is managing multi-server clusters

-Working with complex data is a difficult.
-Many original features or design choices were incorrect and slow to be corrected. For example, timezone support is minimal and days/dates/months use a custom numbering rather than ISO. Mongo is only now adding ISO versions of aggregation commands

I enjoy using this data store because of how quick I can create models and begin using them within my code. The speed of development of applications is dramatically reduced because data can be consumed as soon as it is retrieved without writing much translation code.

I don't like the complexity of the setup. If you need to do advanced clustering it is challenging to setup and configure.

Super easy to get started, perfect documentation, great developer user experience, great performances, easy query language.

Actually can't find anything I don't like about it.

I enjoyed this high-level SQL interpretation of MongoDB. We address numerous business problems with MongoDB. It stores a large amount of information used by our association to store customer data sets on the server.

Most instruments are shut source and will more often than not have a few bugs.

Useful for large volumes of data and for recording all the critical transactions of the operation.

I don't use it for transactions that require data integrity, this for me is a disadvantage.

Performance of mongoDB is pretty good. It is highly available.

MongoDB usually has high memory usage, so its need infra to support it.

lightweight and fast powerful database tool

performance slows down with larger data and need a User Interface for administration

MongoDB has integrations with almost all computer languages used today, with even having an integration with Erlang (a very rarely used language). The drivers are easily available from open source contributors and works amazingly well. For a No-SQL database, it is amazingly well managed and easier to use in almost all other languages.

Its sort of confusing to manage the access control levels of the users, as it is all stored in json file. If there was a way to sort of make this process graphical or interactive, it would have reduced many access threats in a database.

I like the Atlas Cloud solution. It enables various tricks and cross-integrations of single database between multiple apps (containers) with simple user management - for a reasonable price.

I cannot get over this Atlas Compass UX issue - I want to open at least two "tabs" with collections at the same time. This is not possible and even worse, when you switch between collections, thee state is completely reset.

So I'm compiling the search query carefully with all the commas, brackets and identifiers, I often need to search for ID from another collection or maybe from the same collection. When i change collection scope -> BAM! The whole query is gone :( The input should retain its actual value...

Second "issue": When it comes to schema changes in living DB, sometimes it behaves unexpectedly (no error, no data). Maybe I'm doing something wrong, but I would like to know it...

I don't usually rave about products, but I do about MongoDB.
* MongoDB's schemaless document-centric approach to database makes it easy to store any kind of data I need to, even subdocuments and array fields with minimal fuss within code or tools. This allows for a lot of flexibility and makes it easy to upgrade or refactor existing software.
* MongoDB's indexes provide amazing performance even in a schemaless world. Simple and compound indexes, as found in the relational database world, are just the start. Add to that multikey indexes (indexes over array fields), string and geolocation indexes, and indexes where entries expire documents with time. Mongo has it all.
* MongoDB's approach to scalability - using replica sets for high availability and fault-tolerant failover and sharding databases over a potentially large number of servers - makes it easy to scale huge amounts of data without overly expensive hardware and failover complexity.
* MongoDB driver support is available for a variety of languages.
* Mongo University, free graded courses covering various aspects of MongoDB from development to administrative activities to security, etc., offers a solid path to learning.

I'm a huge proponent of MongoDB but because of the nature of schemaless document-oriented databases, there are still some problems for which a relational database is still the answer. There are some applications and technical domains where relational databases still have a huge lead over Mongo for performance.

One of the most difficult parts of software development, especially with complex projects, is keeping the software maintained. As business requirement change, the effort required to make those changes grows over time. MongoDB opened up a whole new world for me. I can make structural changes to my underlying data with ease without too much modification the data access layer. It reduces initial development significantly, and allows my team to pivot to new requirements with unprecedented ease. Because related data is encapsulated, queries are much faster, and our software is extremely performant. Highly recommended.

The tooling isn't great. Compass is a decent tool for accessing and lookup, but it lacks polish. It's slow to start up and sometimes difficult to pull up the data I'm seeking

MongoDB integrates very well with JS-based server code. It's fast and its dynamic, NoSQL nature allows for a lot of flexibility and creative use.

As MongoDB isn't as ubiquitous as other solutions like MySQL, there's a bit less tooling available and some IaaS platforms treat it as a second class citizen.

MongoDB is incredibly easy to set up and use. The fact that non-relational DBs are better for more unstructured data, makes it so that you don't necessarily have to know exactly what the end state is going to be before building your schema. Its data throughput is also a key differentiator, so anything with Big Data is going to be a good fit for a non-relational DB and MongoDB, in particular. Finally, the team behind MongoDB is constantly improving the product and releasing updates, and there are several good data viewers in the market, including one from Mongo, for viewing the data and creating queries.

Creating metrics dashboards can be challenging due to the potential need for JOIN queries in your data. Anything that contains these will be harder to aggregate. Their aggregation framework can be hard to use and limiting for certain requirements.

We can see that MongoDB are on a growth-mindset, constantly adding new features like serverless functions, analytical text search, global write ops, and many other at-scale, enterprise level features. Some of these are already helping us reduce our infrastructure costs, for example, we've recently began switching off our third-party search service that was costing us £££ thanks to the introduction of MongoDB search.

We've had some recent headaches around the MongoDB connection string changes and issues with whitelisting some of our private VPNs on AWS. But these are minor issues. I personally think the Performance Advisor feature is poor - it would be far better to get a list of all unindexed queries listed by overall usage by time-period, rather than the adhoc suggestions that we see.

1) There is no fixed schema like RDBMS. We can alter the table structure insert any number of rows and columns.
2) Data retrieving from this No-SQL database is very much fast when compared to other No-SQL databases.
3) Very easy to install. Provides JSON data support.
4) Can be integrated into different languages like Java, PHP.
5) There are no complex joins of queries like RDBMS.
6) The technical support can be available from MongoDB clients in case of any complex issues that occur while working.
7) Having extra features like a backup of stored data, sharing the data to multiple systems across the network is an added advantage.
8) Handles unstructured data i.e the data which has no format, no proper structure.

I did not find any flaws with this software.

Mongo DB is non relational database (NoSQL). It is a document database. it has good query capacity. Very easy to install and setup Mongo DB. Also Mongo DB is schema-free, there fore our software (code) defines the schema. It support BSON data format, there fore no complex to write code with mongo db (most of programming languages support BSON data format. We can index the mongo collections and increase the query performance

When we need to query using two mongo collections (join two collection), we can use lookup feature. but the problem is Mongo DB lookup feature is slow. Also we have no option to run query among two or three collections like as Elastic Search. When we using Elastic Search we can run query among two or three indexes(collections). There for in Elastic Search we can move historical data to separate collections and improve query performance. if we need query with historical, Elastic Search support it. But using Mongo DB we have to handle it in programming side.

Really speedy and requires less memory.
We can create collections without a definite number of rows and columns to come.
We can add data without restrictions
It gives a JSON output which is really easy to work with
Easy to add , edit and remove data
If a certain section has more than one data, we can insert them as sub arrays, and it makes the developments so flexible.

The free version allows limited memory so that we have to upgrade it to insert unlimited data. Other than that this is the best.

First thing is there's no need of any Queries; it is the king of NoSQL. MongoDB is easy to user and learn too and also a flexible schema for changing data elements. Being a web developer, i work on JavaScript, so it's too good for Integrating with JavaScript backend like NodeJS. For the beginner and new comers, they have Mongo DB for free with certain amount of data storage.

Unlike SQL, it has no joins, which can sometime be an issue in some data retrieval cases. Sometimes it works very slow in the cloud environment. There are no functions available for Transaction. MongoDB's documentation is much confusing to the users. Even if it's easy to use and learn, learning MongoDB might take some time. I thinkg it is hard to work with complex queries.

MongoDB's support for setup and when we have run into problems was the best part about using Mongo and why I've come back to it many times.

MongoDB has good relationships with other providers but the fact that it's not part of a larger suite of cloud-based solutions is a shame.

MongoDB is a great and fresh database server that allows you to create and use non-relational databases, that means; you can store the data in JSON format object. Being an amazing and handy tool that will be compatible with almost every type of development.

Moreover, MongoDB features cloud services, allowing you to create and use cluster and triggers, also a tool for monitoring. Furthermore, Mongo uses hooks systems that offer you the option of use CRUD requests and customize the behavior of your database and the way that your models interact.

For novices or junior developers, the setup of mongo and its services will be hardy, being essential have some basic knowledge about how MongoDB and the non-relational databases work. For this scenario, MongoDB has a complete, and handy Documentation that explains every service and gives you examples about using the databases and its services.

It's ease of use and integration with Python is the best thing. Use for web development and other applications, this has been a great tool.

Don't know the cons of this great product. I can say it would be better if they start providing certifications for professionals.

Being a NoSQL, document-based database, MongoDb allows me to think about data NOT in terms of rows, columns, tables and keys, but in terms of complex hierarchical JSON-like documents that very closely resemble how the data is presented and used. This is incredibly valuable when quickly stubbing out a new app's feature set and the data requirements for it, with the added advantage that MongoDb works seemlessly within a MEAN/MERN development framework.

Also, the learning curve is greatly shortened by MongoDb University, which is a free set of training courses taught by MongoDb, Inc., Engineers. It's an amazing free resource for the neophyte Mongo developer.

Because MongoDb doesn't have a schema, deeply nested objects can become needlessly complex if the developer doesn't have a strong understanding of hit/her data. Queries requiring joins of complex object structures can be slow in non-optimized, under-powered environments.