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MongoDB vs RabbitMQ: What are the differences?
Introduction
This Markdown code provides a comparison between MongoDB and RabbitMQ, highlighting the key differences between the two technologies.
Data Structure and Storage: MongoDB is a document-oriented database that stores data in a flexible JSON-like format called BSON. It allows for dynamic schema definition, making it suitable for unstructured or semi-structured data. On the other hand, RabbitMQ is a message broker that follows a queue-based messaging pattern. It stores and delivers messages using a message queue, which makes it more suitable for handling real-time communication between distributed systems.
Data Persistence and Scalability: MongoDB provides built-in horizontal scalability by allowing data to be spread across multiple servers in a cluster, providing high availability and automatic failover. It also supports sharding, which enables distributing data across multiple machines for better performance. RabbitMQ, as a message broker, does not provide a built-in persistence mechanism for messages. It relies on external data stores or message acknowledgement mechanisms to achieve persistence.
Data Query and Indexing: MongoDB provides a powerful query language that allows for complex queries using a JSON-like syntax. It supports indexing for faster query performance and can use multiple indexes per collection. RabbitMQ does not provide querying capabilities for the messages it handles. It focuses on routing and delivering messages based on defined routing rules rather than querying data.
Message Exchange Patterns: RabbitMQ supports various message exchange patterns like point-to-point, publish/subscribe, request/reply, etc. It allows for flexible message routing based on message headers, routing keys, and binding patterns. MongoDB, being a database, does not have built-in support for these message exchange patterns. It primarily focuses on CRUD operations and does not provide direct support for message queuing or routing.
Ecosystem and Community Support: MongoDB has a well-established ecosystem with a large community and third-party libraries and tools. It provides official drivers for multiple programming languages and has extensive documentation and support. RabbitMQ also has a vibrant ecosystem with various client libraries and tools available. However, its community and ecosystem are relatively smaller compared to MongoDB.
Data Consistency and ACID Transactions: MongoDB supports ACID transactions that ensure data consistency and integrity across multiple document operations within a single transaction. It allows for atomicity, consistency, isolation, and durability. RabbitMQ, being a message broker, does not provide built-in support for ACID transactions. It focuses on providing reliable delivery of messages rather than ensuring data consistency across multiple operations.
In summary, MongoDB is a document-oriented database that provides flexible data storage and querying capabilities, while RabbitMQ is a message broker that focuses on reliable message delivery and routing. They differ in terms of data structure, storage, persistence, scalability, query capabilities, message exchange patterns, ecosystem support, and data consistency features.
For learning purposes, I am trying to design a dashboard that displays the total revenue from all connected webshops/marketplaces, displaying incoming orders, total orders, etc.
So I will need to get the data (using Node backend) from the Shopify and marketplace APIs, storing this in the database, and get the data from the back end.
My question is:
What kind of database should I use? Is MongoDB fine for storing this kind of data? Or should I go with a SQL database?
Postgres is a solid database with a promising background. In the relational side of database design, I see Postgres as an absolute; Now the arguments and conflicts come in when talking about NoSQL data types. The truth is jsonb in Postgres is efficient and gives a good performance and storage. In a comparison with MongoDB with the same resources (such as RAM and CPU) with better tools and community, I think you should go for Postgres and use jsonb for some of the data. All in all, don't use a NoSQL database just cause you have the data type matching this tech, have both SQL and NoSQL at the same time.
I have found MongoDB easier to work with. Postgres and SQL in general, in my experience, is harder to work with. While Postgres does provide data consistency, MongoDB provides flexibility. I've found the MongoDB ecosystem to be really great with a good community. I've worked with MongoDB in production and it's been great. I really like the aggregation system and using query operators such as $in, $pull, $push.
While my opinion may be unpopular, I have found MongoDB really great for relational data, using aggregations from a code perspective. In general, data types are also more flexible with MongoDB.
I will use PostgreSQL because you have more powerfull feature for data agregation and views (the raw data from shopify and others could be stored as is) and then use views to produce diff. kind of reports unless you wanna create those aggregations/views in nodejs code. HTH
I want to store the data retrieved from multiple APIs and perform some analytics on it. The data stored in DB will never/hardly change. First, I thought it would be better to retrieve the data and create table columns for them, but some data might have different columns than others. So I thought about storing the JSON response from API directly to the table and use it. So which database will be the better choice, PostgreSQL or MongoDB.
Hey Krunal, your requirement sounds pretty clear and specific to what you want to do with that data. My recommendation to you, would be to use MongoDB. Since schema-less IO is faster in MongoDB, your general speed of reading / writing from and to the database would be quick. Additionally, the aggregate framework is very powerful with large data so that is also something that you can use in computing your analytics.
I suggest you to go with MongoDB
, because it is schema-less, i.e., it permits you to easily manipulate the schema of a table. If you want to add a column, it can be done without much effort. Moreover, MongoDB
can deal with more types of data, since the latest is stored as key-value pair. I do not what kind of analysis you are going to do, but NoSQL
is not the best choice if you are going to use complex queries. In addition, if you are working with huge amount of data and you are interested in optimising the performance, I suggest you PostgreSQL
.
Since you are speaking about API and JSON, I guess that you may using Node JS
for fetching API. I suggest you to try Mongoose
, which facilitate the use of MongoDB
with Node JS
.
Looks like the use case is to store JSON data. mongoDB and Postgres differ in so many aspects like scaling and consistency. Postgres has excellent JSON support now with the power of SQL. MongoDB is good in handling schema less data. However in this case it seems these differences don’t matter that much. I’d recommend you go with what you are most comfortable with.
I don't have an unquestionable opinion regarding your use case. I only trend to pick the MongoDB since it is schemaless avoiding null columns that you not always know when it is used (it depends on the source of the data). The only drawback that I could consider is the query's complexity in MongoDB, sometimes it is a bit tricky, when compared to the traditional SQL queries.
This is largely a matter of opinion. I see that someone else responded and recommended MongoDB but since you are doing data analytics, I highly recommend you go with SQL. You're going to have a really hard time normalizing the data when you can't manipulate relationships and bulk edit with a nice update query.
I'm much more experienced with MySQL than any other database and I am having a hard time getting on board with noSQL entirely because it's really hard to query complex data with relationships using noSQL. I'm using Firestore with one of my apps and MongoDB with another app but they both use MySQL for the heavy lifting and then a document database for things like permissions, caching, etc.
It sounds like the type of problem you need to reverse engineer. I'm sure you can imagine what the data sets would look like if you use MongoDB or Postgres. I suspect that putting in a little bit more work up front will pay high dividends and productivity once the data is normalized.
Again - it's largely a matter of preference but I prefer SQL almost every time.
I need urgent advice from you all! I am making a web-based food ordering platform which includes 3 different ordering methods (Dine-in using QR code scanning + Take away + Home Delivery) and a table reservation system. We are using React for the front-end, and I need your advice if I should use NestJS or ExpressJS for the backend. And regarding the database, which database should I use, MongoDB or PostgreSQL? Which combination will be better? PS. We want to follow the microservice architecture as scalability, reliability, and usability are the most important Non Functional requirements. Expert advice is needed, please. A load of thanks in advance. Kind Regards, Miqdad
I can't speak for the NestJS vs ExpressJS discussion, but I can given a viewpoint on databases.
The main thing to consider around database choice, is what "shape" the data will be in, and the kind of read/write patterns you expect of that data. The blog example shows up so much for DBMS like MongoDB, because it showcases what NoSQL / document storage is very scalable and performant in: mostly isolated documents with a few views / ways to order them and filter them. In your case, I can imagine a number of "relations" already, which suggest a more traditional SQL solution would work well: You have restaurants, they have maybe a few menus (regular, gluten-free etc), with menu items in, which have different prices over time (25% discount on christmas food just after christmas, 50% off pizzas on wednesdays). Then there's a whole different set of "relations" for people ordering, like showing them past orders, which need to refer to the restaurant etc, and credit card transaction information for refunds etc. That to me suggests PostgreSQL, which will scale quite well if you database design is okay.
PostgreSQL also offers you some extensions, which are just amazing for your use-case. https://postgis.net/ for example will let you query for restaurants based on location, without the big cost that comes from constantly using something like Google Maps API to work out which restaurants are near to someone ordering. Partitioning and window functions will be great for your own use internally too, like answering questions of "What types of takeways perform the best for us, Italian, Mexican?" or in combination with PostGIS, answering questions like "What kind of takeways do we need to market to, to improve our selection?".
While these things can all be implemented in MongoDB, you tend to lose some of the convenience of ACID or have to deal with things like eventual consistency, which requires more thinking on the part of your engineers. PostgreSQL offers decent (if more complex) scalablity and redundancy solutions, and is honestly very well proven and plenty of documentation exists on optimising queries.
Hello, i build microservice systems using Angular And Spring (Java) so i can't help with with ur back end choice, BUT, i definitely advice you to use a Nosql database, thus MongoDB of course or even Cassandra if your looking for extreme scalability with zero point of failure. Anyway, Nosql if much more faster then Sql (in your case Postresql DB). All you wanna do with sql can also be done by nosql (not the opposite of course).I also advice you to use docker containers + kubernetes to orchestrate them, if you need scalability and replication, that way your app can support auto scalability (in case ur users number goes high). Best of luck
About PostgreSQL vs MongoDB: short answer. Both are great. Choose what you like the most. Only if you expect millions of users, I‘ll incline with MongoDB.
Hi! I am creating a scraping system in Django, which involves long running tasks between 1 minute & 1 Day. As I am new to Message Brokers and Task Queues, I need advice on which architecture to use for my system. ( Amazon SQS, RabbitMQ, or Celery). The system should be autoscalable using Kubernetes(K8) based on the number of pending tasks in the queue.
Hello, i highly recommend Apache Kafka, to me it's the best. You can deploy it in cluster mode inside K8S, thus you can have a Highly available system (also auto scalable).
Good luck
I am just a beginner at these two technologies.
Problem statement: I am getting lakh of users from the sequel server for whom I need to create caches in MongoDB by making different REST API requests.
Here these users can be treated as messages. Each REST API request is a task.
I am confused about whether I should go for RabbitMQ alone or Celery.
If I have to go with RabbitMQ, I prefer to use python with Pika module. But the challenge with Pika is, it is not thread-safe. So I am not finding a way to execute a lakh of API requests in parallel using multiple threads using Pika.
If I have to go with Celery, I don't know how I can achieve better scalability in executing these API requests in parallel.
For large amounts of small tasks and caches I have had good luck with Redis and RQ. I have not personally used celery but I am fairly sure it would scale well, and I have not used RabbitMQ for anything besides communication between services. If you prefer python my suggestions should feel comfortable.
Sorry I do not have a more information
Hi, we are in a ZMQ set up in a push/pull pattern, and we currently start to have more traffic and cases that the service is unavailable or stuck. We want to: * Not loose messages in services outages * Safely restart service without losing messages (ZeroMQ seems to need to close the socket in the receiver before restart manually)
Do you have experience with this setup with ZeroMQ? Would you suggest RabbitMQ or Amazon SQS (we are in AWS setup) instead? Something else?
Thank you for your time
ZeroMQ is fast but you need to build build reliability yourself. There are a number of patterns described in the zeromq guide. I have used RabbitMQ before which gives lot of functionality out of the box, you can probably use the worker queues
example from the tutorial, it can also persists messages in the queue.
I haven't used Amazon SQS before. Another tool you could use is Kafka.
Both would do the trick, but there are some nuances. We work with both.
From the sound of it, your main focus is "not losing messages". In that case, I would go with RabbitMQ with a high availability policy (ha-mode=all) and a main/retry/error queue pattern.
Push messages to an exchange, which sends them to the main queue. If an error occurs, push the errored out message to the retry exchange, which forwards it to the retry queue. Give the retry queue a x-message-ttl and set the main exchange as a dead-letter-exchange. If your message has been retried several times, push it to the error exchange, where the message can remain until someone has time to look at it.
This is a very useful and resilient pattern that allows you to never lose messages. With the high availability policy, you make sure that if one of your rabbitmq nodes dies, another can take over and messages are already mirrored to it.
This is not really possible with SQS, because SQS is a lot more focused on throughput and scaling. Combined with SNS it can do interesting things like deduplication of messages and such. That said, one thing core to its design is that messages have a maximum retention time. The idea is that a message that has stayed in an SQS queue for a while serves no more purpose after a while, so it gets removed - so as to not block up any listener resources for a long time. You can also set up a DLQ here, but these similarly do not hold onto messages forever. Since you seem to depend on messages surviving at all cost, I would suggest that the scaling/throughput benefit of SQS does not outweigh the difference in approach to messages there.
Hello dear developers, our company is starting a new project for a new Web App, and we are currently designing the Architecture (we will be using .NET Core). We want to embark on something new, so we are thinking about migrating from a monolithic perspective to a microservices perspective. We wish to containerize those microservices and make them independent from each other. Is it the best way for microservices to communicate with each other via ESB, or is there a new way of doing this? Maybe complementing with an API Gateway? Can you recommend something else different than the two tools I provided?
We want something good for Cost/Benefit; performance should be high too (but not the primary constraint).
Thank you very much in advance :)
There are many different messaging frameworks available for IPC use. It's not really a question of how "new" the technology is, but what you need it to do. Azure Service Bus can be a great service to use, but it can also take a lot of effort to administrate and maintain that can make it costly to use unless you need the more advanced features it offers for routing, sequencing, delivery, etc. I would recommend checking out this link to get a basic idea of different messaging architectures. These only cover Azure services, but there are many other solutions that use similar architectural models.
https://docs.microsoft.com/en-us/azure/event-grid/compare-messaging-services
Hello, I am developing a new project with an internal chat between users. Also, there are complex relationships between the other project entities but I wolud like to build something scalable and fast and right now I am designing the data model. What kind of database would you recommend me to manage all application data? relational like MySQL, no relational like MongoDB or a mixed one? Thank you
In MongoDB, a write operation is atomic on the level of a single document, so it's harder to deal with consistency without transactions.
MongoDB supports horizontal scaling through Sharding , distributing data across several machines and facilitating high throughput operations with large sets of data. ... Sharding allows you to add additional instances to increase capacity when required
If you are trying with "complex relationships", give a chance to learn ArangoDB and Graph databases. Its database structures allow doing this with faster and simpler queries. The database is not as strict as others and allows arbitrary data. The data model is really like a neural network and you will never need foreign keys tables anymore. In Udemy there is a free course about it to get started.
The most important question is where are you planning to host? On-premise, or in the cloud.
Particularly if you are planning to host in either AWS or Azure, then your first point of call should be the PaaS (Platform as a Service) databases supplied by these vendors, as you will find yourself requiring a lot less effort to support them, much easier Disaster Recovery options, and also, depending on how PAYG the database is that you use, potentially also much cheaper costs than having a dedicated database server.
Your question regards 'Relational or not' is obviously key, and you need to consider both your required data structure, as well as the ACID requirements of your application model, as well as the non-functional requirements in terms of scalability, resilience, whether you want security authorisation at the highest application tier, or right down to 'row' level in the database, etc. - however please don't fall into the trap of considering 'NoSQL' as being single category. MongoDB, with its document-store type solution is a very different model to key-value-pair stores (like AWS DynamoDB), or column stores (like AWS RedShift) or for more complex data relationships, Entity Graph Stores (like AWS Neptune), to stores designed for tokenisation and text search (ElasticSearch) etc.
Also critical in all this is how many items you believe you need to index by. RDBMS/SQL stores are great for having as many indexes as you want, other than the slow-down in write speed, whereas databases like Amazon DynamoDB provide blisteringly fast read/write performance, but are very limited on key indexing capabilities.
It feels like you have most experience with SQL/RDBMS technologies, so for the simplest learning curve, and if your application fits it, then I'd personally start by looking at AWS Aurora https://aws.amazon.com/rds/aurora/ .
FIrstly, it may help if you explain what you mean by "complex relationships between project entities". Secondly, you can build a fast and scalable solution using either. With that said however, the data sounds relational so I would recommend MySQL.
I think, Its depend of your project type and your skills. MySQL is good and simple for maintenance but MongoDB need more skills and knowledge. If you work on little project, use MySQL. For your project type, MySQL is enough after you can migrate with PostgreSQL
I am going to work on a real estate project and have to decide on a database. Now, SQL databases can be very efficient if appropriately designed. More relations between the data and less redundancy. But with a #NoSQL database, the development time is reduced, and it is easy to query. Since this is my first time working on the real estate domain, I would like to pick a database that would be efficient in the long run.
I recommend PostgreSQL as it’s the most powerful out of the 3 databases you mentioned. It supports JSON objects so you can mimic the MongoDB functionality, but I would also argue that SQL is actually quite powerful and in many cases significantly easier to work with than with NoSQL databases.
Stay away from foreign keys, keep it fast and simple. Define your data structures well in advance. Try to model your data structures based on your system’s vision; based on where it’s going and not based solely on what you currently need it to do. This will help you avoid drastic changes to your database after your system is launched. Populate the database with fake data and run tests. PostgreSQL allows you to create Views from multiple tables. Try to create those views and make sure you can easily create useful views from multiple tables. Run an Explain on those view queries to make sure you created your indexes correctly. Make sure it’s fast!
Any of those three databases are going to be efficient, scalable, and reliable in the long term if you configure and use them correctly. They all also have solid hosting solutions.
All things being equal, I would agree with other posters that Postgres is my preference among the three, but there are caveats.
MongoDB and MySQL have better support for mutli-region replication in your big three cloud environments. Azure recently bought Citus Data, which was a best-in-class Postgres replication solution, so they might be the only one I trust to provide cross-region replication at the moment.
If you have a single region deployment and are on AWS, I can't recommend Aurora Postgres highly enough. It's a very good implementation and extremely performant.
I'll second another piece of advice. Postgresql's JSON columns are a dream when it comes to productivity and I use them frequently with our Rails application. In these cases, no migration is required to change schema. We store payloads with dozens or hundreds of keys and performance has not been an issue. We also have a lot of relational tables, so the joins we get with SQL are very important to us and hard to replicate with a NoQL solution.
That really depends of where do you see you application in the long run. On any application, any of those choices are excellent. You could argue about good support on JSON binaries, but even MySQL has an excellent support for that on the latest versions.
On the long run, when your application gets hundreds of thousands of requests per second, you might start thinking about how many inputs you will have in the database compared to the outputs. PostgresSQL it’s excellent at giving you outputs, but table corruption can happen when you start receiving this massive number of inputs (Which was the reason Uber switched from Postgres to MySQL)
On our OPS Platform at CTO.ai , we decided to use Postgres, because we need a reliable and agile way to send the output to our users, so that was out best choice in the long run for our product.
Hi, I am building an enhanced web-conferencing app that will have a voice/video call, live chats, live notifications, live discussions, screen sharing, etc features. Ref: Zoom.
I need advise finalizing the tech stack for this app. I am considering below tech stack:
- Frontend: React
- Backend: Node.js
- Database: MongoDB
- IAAS: #AWS
- Containers & Orchestration: Docker / Kubernetes
- DevOps: GitLab, Terraform
- Brokers: Redis / RabbitMQ
I need advice at the platform level as to what could be considered to support concurrent video streaming seamlessly.
Also, please suggest what could be a better tech stack for my app?
#SAAS #VideoConferencing #WebAndVideoConferencing #zoom #stack
You're going to want to look hard at WebRTC. It's what almost every realtime video service uses. The appeal is that it establishes a direct connection between peers so that the massive video bandwidth doesn't need to go through your backend. That aside, actor clusters will be the other technology that handle that sort of traffic well. It was popularized by erlang for telecom backbone, akka is another choice for actor systems.
Infrastructure wise, kubernetes would be a fine choice. Just make sure to look up some benchmarks for Container Network Interface (CNI) implementations that support high bandwidth traffic.
Kubernetes provides Auto-scaling whereas Docker Swarm doesn't support autoscaling. Kubernetes supports up to 5000 nodes whereas Docker Swarm supports more than 2000 nodes. Kubernetes is less extensive and customizable whereas Docker Swarm is more comprehensive and highly customizable. So if your main usecase is autoscaling go for kubernetes else Docker is always a good choice.
Also, for database I'm using mongo for my this huge personal project but I checked Aerospike (more like redis + mongo) you may use this instead of mongo for better performance. Or if you plan on going on with mongo try Tokumx by http://percona.com/. It literally says MongoDB on steriods (I tried but didn't go with my setup). Percona is really known for pumping databases like MySQL for better performance.
jitsi is end to end encryption and free alternative to zoom. It is more powerful than zoom. WebRTC is heavily used in any online-video conferencing platforms https://jitsi.org
If you use RabbitMQ you should be aware that it has a mechism where if it exceeds a certain amount of memory usage it will block all connections.
You can change this, https://www.rabbitmq.com/memory.html, but it's something to be aware of.
Generally speaking, RabbitMQ scales vertically not horizontally. Another tech to consider is NATS by the Cloud Computing Foundation: https://nats.io/ which is distributed so it scales horizontally. I not sure about how Redis scales.
I second the other poster's advice of considering something like WebRTC or other peer-to-peer communication. It would be by far the most scalable option and if it doesn't work you can fallback to client-server. Although it is generally more tricky to implement.
Go with RabbitMQ over redis. Not that I had any problem with redis but I used (without any problem) RabbitMQ with over 10 million records on a 1GB system and 3 cpu with 25+ (50 workers each node) nodes to handle scraping for our project. I'm sure redis can also handle that but it may need more ram.
Mediasoup offers an SFU implementation in C++ and exposes it through a node.js module which could get you up and running quickly.
Look into streaming tools such as Kafka https://kafka.apache.org. AWS Kafka service called MSK https://aws.amazon.com/msk/ for streaming.
At Pushnami we were looking at several alternative databases that would support following architectural requirements: - very quick prototyping for an unknown domain - ability to support large amounts of data - native ability to replicate and fail over - full stack approach for Node.js development After careful consideration MongoDB came on top, and 3 years later we are still very happy with that decision. Currently we keep almost 2TB of data in our cluster, and start thinking about sharding.
After using couchbase for over 4 years, we migrated to MongoDB and that was the best decision ever! I'm very disappointed with Couchbase's technical performance. Even though we received enterprise support and were a listed Couchbase Partner, the experience was horrible. With every contact, the sales team was trying to get me on a $7k+ license for access to features all other open source NoSQL databases get for free.
Here's why you should not use Couchbase
Full-text search Queries The full-text search often returns a different number of results if you run the same query multiple types
N1QL queries Configuring the indexes correctly is next to impossible. It's poorly documented and nobody seems to know what to do, even the Couchbase support engineers have no clue what they are doing.
Community support I posted several problems on the forum and I never once received a useful answer
Enterprise support It's very expensive. $7k+. The team constantly tried to get me to buy even though the community edition wasn't working great
Autonomous Operator It's actually just a poorly configured Kubernetes role that no matter what I did, I couldn't get it to work. The support team was useless. Same lack of documentation. If you do get it to work, you need 6 servers at least to meet their minimum requirements.
Couchbase cloud Typical for Couchbase, the user experience is awful and I could never get it to work.
Minimum requirements
The minimum requirements in production are 6 servers. On AWS the calculated monthly cost would be ~$600
. We achieved better performance using a $16
MongoDB instance on the Mongo Atlas Cloud
writing queries is a nightmare While N1QL is similar to SQL and it's easier to write because of the familiarity, that isn't entirely true. The "smart index" that Couchbase advertises is not smart at all. Creating an index with 5 fields, and only using 4 of them won't result in Couchbase using the same index, so you have to create a new one.
Couchbase UI
The UI that comes with every database deployment is full of bugs, barely functional and the developer experience is poor. When I asked Couchbase about it, they basically said they don't care because real developers use SQL directly from code
Consumes too much RAM
Couchbase is shipped with a smaller Memcached instance to handle the in-memory cache. Memcached ends up using 8 GB of RAM for 5000 documents
! I'm not kidding! We had less than 5000 docs on a Couchbase instance and less than 20 indexes and RAM consumption was always over 8 GB
Memory allocations are useless I asked the Couchbase team a question: If a bucket has 1 GB allocated, what happens when I have more than 1GB stored? Does it overflow? Does it cache somewhere? Do I get an error? I always received the same answer: If you buy the Couchbase enterprise then we can guide you.
We actually use both Mongo and SQL databases in production. Mongo excels in both speed and developer friendliness when it comes to geospatial data and queries on the geospatial data, but we also like ACID compliance hence most of our other data (except on-site logs) are stored in a SQL Database (MariaDB for now)
MySQL has a lot of strengths working for it. It's simple and easy to set up and use. It's JSON engine is also really good these days. Mongo is also simple to setup and use, and it's speed as a document-object storage engine is first class.
Where Postgres has both beat is in it's combining of all of the features that make both MySQL and Mongo great, while adding on enterprise grade level scalability and replication. It's Postgres' stability and robustness, while still fulfilling the roles of it's contemporaries extremely well that edge Postgre for me.
When I was new with web development, I was using PHP for backend and MySQL for database. But after improving my JS skills, I chosen Node.js. Because of too many reasons including npm, express, community, fast coding and etc. MongoDB is so good for using with Node.js. If your JS skills are enough good, I recommend to migrate to Node.js and MongoDB.
Easier scalability of MongoDB prompted this migration from MySQL.
As Runtastic grew, at some point it would have outgrown our MySQL installation. We looked for a couple of alternatives and found MongoDB as a great replacement for our use case. Read how a migration of live data from one database to another worked for us.
My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.
In addition to being a lot cheaper, Google Cloud Pub/Sub allowed us to not worry about maintaining any more infrastructure that needed.
We moved from a self-hosted RabbitMQ over to CloudAMQP and decided that since we use GCP anyway, why not try their managed PubSub?
It is one of the better decisions that we made, and we can just focus about building more important stuff!
We used Mongo for the first iterations of our app, but the relational nature of our data was an awkward fit for a database that is not relational. We sorely lacked relational database integrity features that needed to be done on the application side (poorly) and it was a huge relief when we managed to port our application over to Postgres, which performs great and never gives us trouble, while having very user friendly extensions like JSON and PubSub that made the transition easy.
We wanted a JSON datastore that could save the state of our bioinformatics visualizations without destructive normalization. As a leading NoSQL data storage technology, MongoDB has been a perfect fit for our needs. Plus it's open source, and has an enterprise SLA scale-out path, with support of hosted solutions like Atlas. Mongo has been an absolute champ. So much so that SQL and Oracle have begun shipping JSON column types as a new feature for their databases. And when Fast Healthcare Interoperability Resources (FHIR) announced support for JSON, we basically had our FHIR datalake technology.
Pros of MongoDB
- Document-oriented storage827
- No sql593
- Ease of use553
- Fast464
- High performance410
- Free257
- Open source218
- Flexible180
- Replication & high availability145
- Easy to maintain112
- Querying42
- Easy scalability39
- Auto-sharding38
- High availability37
- Map/reduce31
- Document database27
- Easy setup25
- Full index support25
- Reliable16
- Fast in-place updates15
- Agile programming, flexible, fast14
- No database migrations12
- Easy integration with Node.Js8
- Enterprise8
- Enterprise Support6
- Great NoSQL DB5
- Support for many languages through different drivers4
- Drivers support is good3
- Aggregation Framework3
- Schemaless3
- Fast2
- Managed service2
- Easy to Scale2
- Awesome2
- Consistent2
- Good GUI1
- Acid Compliant1
Pros of RabbitMQ
- It's fast and it works with good metrics/monitoring234
- Ease of configuration79
- I like the admin interface59
- Easy to set-up and start with50
- Durable21
- Intuitive work through python18
- Standard protocols18
- Written primarily in Erlang10
- Simply superb8
- Completeness of messaging patterns6
- Scales to 1 million messages per second3
- Reliable3
- Distributed2
- Supports MQTT2
- Better than most traditional queue based message broker2
- Supports AMQP2
- Clusterable1
- Clear documentation with different scripting language1
- Great ui1
- Inubit Integration1
- Better routing system1
- High performance1
- Runs on Open Telecom Platform1
- Delayed messages1
- Reliability1
- Open-source1
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Cons of MongoDB
- Very slowly for connected models that require joins6
- Not acid compliant3
- Proprietary query language1
Cons of RabbitMQ
- Too complicated cluster/HA config and management9
- Needs Erlang runtime. Need ops good with Erlang runtime6
- Configuration must be done first, not by your code5
- Slow4