MongoDB vs RabbitMQ

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MongoDB
MongoDB

16.6K
13.1K
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RabbitMQ
RabbitMQ

4.7K
3.3K
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453
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MongoDB vs RabbitMQ: What are the differences?

MongoDB: The database for giant ideas. MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding; RabbitMQ: A messaging broker - an intermediary for messaging. RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

MongoDB belongs to "Databases" category of the tech stack, while RabbitMQ can be primarily classified under "Message Queue".

"Document-oriented storage" is the top reason why over 788 developers like MongoDB, while over 202 developers mention "It's fast and it works with good metrics/monitoring" as the leading cause for choosing RabbitMQ.

MongoDB and RabbitMQ are both open source tools. It seems that MongoDB with 16.2K GitHub stars and 4.08K forks on GitHub has more adoption than RabbitMQ with 5.88K GitHub stars and 1.73K GitHub forks.

According to the StackShare community, MongoDB has a broader approval, being mentioned in 2175 company stacks & 2143 developers stacks; compared to RabbitMQ, which is listed in 921 company stacks and 532 developer stacks.

What is MongoDB?

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

What is RabbitMQ?

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
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What are some alternatives to MongoDB and RabbitMQ?
Amazon DynamoDB
With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
Couchbase
Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
MySQL
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
PostgreSQL
PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
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Decisions about MongoDB and RabbitMQ
MongoDB
MongoDB

I starting using MongoDB because it was much easier to implement in production then hosted SQL, and found that a lot of the limitation you think of from a document store vs a relational database were overcome by connecting the application to a graphql API, making retrieval seamless. Mongos latest upgrades as well as Stitch and Mongo mobile make it a perfect fit especially if your application will be cross platform web and mobile.

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Jeyabalaji Subramanian
Jeyabalaji Subramanian
CTO at FundsCorner · | 24 upvotes · 278.9K views
atFundsCornerFundsCorner
Zappa
Zappa
AWS Lambda
AWS Lambda
SQLAlchemy
SQLAlchemy
Python
Python
Amazon SQS
Amazon SQS
Node.js
Node.js
MongoDB Stitch
MongoDB Stitch
PostgreSQL
PostgreSQL
MongoDB
MongoDB

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

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Khauth György
Khauth György
CTO at SalesAutopilot Kft. · | 11 upvotes · 96.6K views
atSalesAutopilot Kft.SalesAutopilot Kft.
AWS CodePipeline
AWS CodePipeline
Jenkins
Jenkins
Docker
Docker
vuex
vuex
Vuetify
Vuetify
Vue.js
Vue.js
jQuery UI
jQuery UI
Redis
Redis
MongoDB
MongoDB
MySQL
MySQL
Amazon Route 53
Amazon Route 53
Amazon CloudFront
Amazon CloudFront
Amazon SNS
Amazon SNS
Amazon CloudWatch
Amazon CloudWatch
GitHub
GitHub

I'm the CTO of a marketing automation SaaS. Because of the continuously increasing load we moved to the AWSCloud. We are using more and more features of AWS: Amazon CloudWatch, Amazon SNS, Amazon CloudFront, Amazon Route 53 and so on.

Our main Database is MySQL but for the hundreds of GB document data we use MongoDB more and more. We started to use Redis for cache and other time sensitive operations.

On the front-end we use jQuery UI + Smarty but now we refactor our app to use Vue.js with Vuetify. Because our app is relatively complex we need to use vuex as well.

On the development side we use GitHub as our main repo, Docker for local and server environment and Jenkins and AWS CodePipeline for Continuous Integration.

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Jeyabalaji Subramanian
Jeyabalaji Subramanian
CTO at FundsCorner · | 12 upvotes · 21.4K views
atFundsCornerFundsCorner
MongoDB Atlas
MongoDB Atlas
MongoDB
MongoDB
PostgreSQL
PostgreSQL

Database is at the heart of any technology stack. It is no wonder we spend a lot of time choosing the right database before we dive deep into product building.

When we were faced with the question of what database to choose, we set the following criteria: The database must (1) Have a very high transaction throughput. We wanted to err on the side of "reads" but not on the "writes". (2) be flexible. I.e. be adaptive enough to take - in data variations. Since we are an early-stage start-up, not everything is set in stone. (3) Fast & easy to work with (4) Cloud Native. We did not want to spend our time in "ANY" infrastructure management.

Based on the above, we picked PostgreSQL and MongoDB for evaluation. We tried a few iterations on hardening the data model with PostgreSQL, but realised that we can move much faster by loosely defining the schema (with just a few fundamental principles intact).

Thus we switched to MongoDB. Before diving in, we validated a few core principles such as: (1) Transaction guarantee. Until 3.6, MongoDB supports Transaction guarantee at Document level. From 4.0 onwards, you can achieve transaction guarantee across multiple documents.

(2) Primary Keys & Indexing: Like any RDBMS, MongoDB supports unique keys & indexes to ensure data integrity & search ability

(3) Ability to join data across data sets: MongoDB offers a super-rich aggregate framework that enables one to filter and group data

(4) Concurrency handling: MongoDB offers specific operations (such as findOneAndUpdate), which when coupled with Optimistic Locking, can be used to achieve concurrency.

Above all, MongoDB offers a complete no-frills Cloud Database as a service - MongoDB Atlas. This kind of sealed the deal for us.

Looking back, choosing MongoDB with MongoDB Atlas was one of the best decisions we took and it is serving us well. My only gripe is that there must be a way to scale-up or scale-down the Atlas configuration at different parts of the day with minimal downtime.

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RabbitMQ
RabbitMQ
Kafka
Kafka

The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring". I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka's strengths (replayability, based on an even sourcing architecture). You could pick one of the AMQP options.

I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best.

For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries (though it is missing Python's - which should be Puka, I assume).

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Frédéric MARAND
Frédéric MARAND
Core Developer at OSInet · | 2 upvotes · 92.2K views
atOSInetOSInet
RabbitMQ
RabbitMQ
Beanstalkd
Beanstalkd
Kafka
Kafka

I used Kafka originally because it was mandated as part of the top-level IT requirements at a Fortune 500 client. What I found was that it was orders of magnitude more complex ...and powerful than my daily Beanstalkd , and far more flexible, resilient, and manageable than RabbitMQ.

So for any case where utmost flexibility and resilience are part of the deal, I would use Kafka again. But due to the complexities involved, for any time where this level of scalability is not required, I would probably just use Beanstalkd for its simplicity.

I tend to find RabbitMQ to be in an uncomfortable middle place between these two extremities.

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Ajit Parthan
Ajit Parthan
CTO at Shaw Academy · | 1 upvotes · 5K views
atShaw AcademyShaw Academy
MongoDB
MongoDB
MySQL
MySQL
#NosqlDatabaseAsAService

Initial storage was traditional MySQL. The pace of changes during a startup mode made it very difficult to have a clean and consistent schema. Large portions ended up as unstructured data stuffed into CLOBs and BLOBs.

Moving to MongoDB definitely made this part much easier.

Accessing data for analysis is a little bit of a challenge - especially for people coming from the world of SQL Workbench. But with tools like Exploratory this is becoming less of a problem.

#NosqlDatabaseAsAService

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Tim Nolet
Tim Nolet
Founder, Engineer & Dishwasher at Checkly · | 8 upvotes · 61.2K views
atChecklyHQChecklyHQ
Amazon DynamoDB
Amazon DynamoDB
MongoDB
MongoDB
Node.js
Node.js
Heroku
Heroku
PostgreSQL
PostgreSQL

PostgreSQL Heroku Node.js MongoDB Amazon DynamoDB

When I started building Checkly, one of the first things on the agenda was how to actually structure our SaaS database model: think accounts, users, subscriptions etc. Weirdly, there is not a lot of information on this on the "blogopshere" (cringe...). After research and some false starts with MongoDB and Amazon DynamoDB we ended up with PostgreSQL and a schema consisting of just four tables that form the backbone of all generic "Saasy" stuff almost any B2B SaaS bumps into.

In a nutshell:cPostgreSQL Heroku Node.js MongoDB Amazon DynamoDB

When I started building Checkly, one of the first things on the agenda was how to actually structure our SaaS database model: think accounts, users, subscriptions etc. Weirdly, there is not a lot of information on this on the "blogopshere" (cringe...). After research and some false starts with MongoDB and Amazon DynamoDB we ended up with PostgreSQL and a schema consisting of just four tables that form the backbone of all generic "Saasy" stuff almost any B2B SaaS bumps into.

In a nutshell:

  • We use Postgres on Heroku.
  • We use a "one database, on schema" approach for partitioning customer data.
  • We use an accounts, memberships and users table to create a many-to-many relation between users and accounts.
  • We completely decouple prices, payments and the exact ingredients for a customer's plan.

All the details including a database schema diagram are in the linked blog post.

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Łukasz Korecki
Łukasz Korecki
CTO & Co-founder at EnjoyHQ · | 12 upvotes · 38.5K views
atEnjoyHQEnjoyHQ
PostgreSQL
PostgreSQL
MongoDB
MongoDB
RethinkDB
RethinkDB

We initially chose RethinkDB because of the schema-less document store features, and better durability resilience/story than MongoDB In the end, it didn't work out quite as we expected: there's plenty of scalability issues, it's near impossible to run analytical workloads and small community makes working with Rethink a challenge. We're in process of migrating all our workloads to PostgreSQL and hopefully, we will be able to decommission our RethinkDB deployment soon.

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Mauro Bennici
Mauro Bennici
CTO at You Are My GUide · | 7 upvotes · 10.4K views
atYou Are My GUideYou Are My GUide
MongoDB
MongoDB
TimescaleDB
TimescaleDB
PostgreSQL
PostgreSQL

PostgreSQL plus TimescaleDB allow us to concentrate the business effort on how to analyze valuable data instead of manage them on IT side. We are now able to ingest thousand of social shares "managed" data without compromise the scalability of the system or the time query. TimescaleDB is transparent to PostgreSQL , so we continue to use the same SQL syntax without any changes. At the same time, because we need to manage few document objects we dismissed the MongoDB cluster.

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Robert Zuber
Robert Zuber
CTO at CircleCI · | 22 upvotes · 160.8K views
atCircleCICircleCI
Amazon S3
Amazon S3
GitHub
GitHub
Redis
Redis
PostgreSQL
PostgreSQL
MongoDB
MongoDB

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

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Martin Johannesson
Martin Johannesson
Senior Software Developer at IT Minds · | 10 upvotes · 15.1K views
atIT MindsIT Minds
AMP
AMP
PWA
PWA
React
React
MongoDB
MongoDB
Next.js
Next.js
GraphQL
GraphQL
Apollo
Apollo
PostgreSQL
PostgreSQL
TypeORM
TypeORM
Node.js
Node.js
TypeScript
TypeScript
#Serverless
#Backend
#B2B

At IT Minds we create customized internal or #B2B web and mobile apps. I have a go to stack that I pitch to our customers consisting of 3 core areas. 1) A data core #backend . 2) A micro #serverless #backend. 3) A user client #frontend.

For the Data Core I create a backend using TypeScript Node.js and with TypeORM connecting to a PostgreSQL Exposing an action based api with Apollo GraphQL

For the micro serverless backend, which purpose is verification for authentication, autorization, logins and the likes. It is created with Next.js api pages. Using MongoDB to store essential information, caching etc.

Finally the frontend is built with React using Next.js , TypeScript and @Apollo. We create the frontend as a PWA and have a AMP landing page by default.

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MongoDB
MongoDB
MySQL
MySQL
.NET Core
.NET Core
C#
C#

Hi! I needed to choose a full stack of tools for a web drop shipping site without the payment process for a family startup proyect. It will feed from several web services (JSON), I'm looking forward a 4,200 articles tops. For web use only and for a few clients at the beginning.

I'm considering C# with .NET Core 3.0 as is the one language I'm starting to learn. For the Database I haven´t made my mind yet, but could be MySQL or MongoDB any advice is welcome as I'm getting back to programming after year away from this awesome world. Thanks

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Nicolas Apx
Nicolas Apx
CEO - FullStack Javascript at Apx Development Limited · | 14 upvotes · 17.1K views
atAPX DevelopmentAPX Development
PostgreSQL
PostgreSQL
MongoDB
MongoDB
Node.js
Node.js
Python
Python

I am planning on building a micro-service eCommerce back-end to be easy to reuse in any project as we need. I would like to use both Python and Node.js and MongoDB & PostgreSQL , in your opinion which one would best suited for the following services:

  • Users-service
  • Products-service
  • Auth-service
  • Inventory-service
  • Order-service
  • Payment-service
  • Sku-service
  • And more not yet defined....

Thanks

Nicolas

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Michael Mota
Michael Mota
CEO & Founder at AlterEstate · | 4 upvotes · 12K views
atAlterEstateAlterEstate
Django
Django
RabbitMQ
RabbitMQ
Celery
Celery

Automations are what makes a CRM powerful. With Celery and RabbitMQ we've been able to make powerful automations that truly works for our clients. Such as for example, automatic daily reports, reminders for their activities, important notifications regarding their client activities and actions on the website and more.

We use Celery basically for everything that needs to be scheduled for the future, and using RabbitMQ as our Queue-broker is amazing since it fully integrates with Django and Celery storing on our database results of the tasks done so we can see if anything fails immediately.

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Interest over time
Reviews of MongoDB and RabbitMQ
Review ofRabbitMQRabbitMQ

I developed one of the largest queue based medical results delivery systems in the world, 18,000+ queues and still growing over a decade later all using MQSeries, later called Websphere MQ. When I left that company I started using RabbitMQ after doing some research on free offerings.. it works brilliantly and is incredibly flexible from small scale single instance use to large scale multi-server - multi-site architectures.

If you can think in queues then RabbitMQ should be a viable solution for integrating disparate systems.

How developers use MongoDB and RabbitMQ
Avatar of Tarun Singh
Tarun Singh uses MongoDBMongoDB

Used MongoDB as primary database. It holds trip data of NYC taxis for the year 2013. It is a huge dataset and it's primary feature is geo coordinates with pickup and drop off locations. Also used MongoDB's map reduce to process this large dataset for aggregation. This aggregated result was then used to show visualizations.

Avatar of Trello
Trello uses MongoDBMongoDB

MongoDB fills our more traditional database needs. We knew we wanted Trello to be blisteringly fast. One of the coolest and most performance-obsessed teams we know is our next-door neighbor and sister company StackExchange. Talking to their dev lead David at lunch one day, I learned that even though they use SQL Server for data storage, they actually primarily store a lot of their data in a denormalized format for performance, and normalize only when they need to.

Avatar of Foursquare
Foursquare uses MongoDBMongoDB

Nearly all of our backend storage is on MongoDB. This has also worked out pretty well. It's enabled us to scale up faster/easier than if we had rolled our own solution on top of PostgreSQL (which we were using previously). There have been a few roadbumps along the way, but the team at 10gen has been a big help with thing.

Avatar of AngeloR
AngeloR uses MongoDBMongoDB

We are testing out MongoDB at the moment. Currently we are only using a small EC2 setup for a delayed job queue backed by agenda. If it works out well we might look to see where it could become a primary document storage engine for us.

Avatar of Matt Welke
Matt Welke uses MongoDBMongoDB

Used for proofs of concept and personal projects with a document data model, especially with need for strong geographic queries. Often not chosen in long term apps due to chance data model can end up relational as needs develop.

Avatar of Cloudify
Cloudify uses RabbitMQRabbitMQ

The poster child for scalable messaging systems, RabbitMQ has been used in countless large scale systems as the messaging backbone of any large cluster, and has proven itself time and again in many production settings.

Avatar of Chris Saylor
Chris Saylor uses RabbitMQRabbitMQ

Rabbit acts as our coordinator for all actions that happen during game time. All worker containers connect to rabbit in order to receive game events and emit their own events when applicable.

Avatar of Clarabridge Engage
Clarabridge Engage uses RabbitMQRabbitMQ

Used as central Message Broker; off-loading tasks to be executed asynchronous, used as communication tool between different microservices, used as tool to handle peaks in incoming data, etc.

Avatar of Analytical Informatics
Analytical Informatics uses RabbitMQRabbitMQ

RabbitMQ is the enterprise message bus for our platform, providing infrastructure for managing our ETL queues, real-time event notifications for applications, and audit logging.

Avatar of Packet
Packet uses RabbitMQRabbitMQ

RabbitMQ is an all purpose queuing service for our stack. We use it for user facing jobs as well as keeping track of behind the scenes jobs.

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