Amazon RDS vs MongoDB: What are the differences?
Developers describe Amazon RDS as "Set up, operate, and scale a relational database in the cloud". Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call. On the other hand, MongoDB is detailed as "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.
Amazon RDS and MongoDB are primarily classified as "SQL Database as a Service" and "Databases" tools respectively.
"Reliable failovers", "Automated backups" and "Backed by amazon" are the key factors why developers consider Amazon RDS; whereas "Document-oriented storage", "No sql" and "Ease of use" are the primary reasons why MongoDB is favored.
MongoDB is an open source tool with 16.3K GitHub stars and 4.1K GitHub forks. Here's a link to MongoDB's open source repository on GitHub.
Uber Technologies, Lyft, and Codecademy are some of the popular companies that use MongoDB, whereas Amazon RDS is used by Airbnb, Netflix, and Coursera. MongoDB has a broader approval, being mentioned in 2189 company stacks & 2218 developers stacks; compared to Amazon RDS, which is listed in 1435 company stacks and 526 developer stacks.
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
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
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.
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 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.
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
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.
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.
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)
Sign up to add or upvote prosMake informed product decisions
Sign up to add or upvote consMake informed product decisions
What is Amazon RDS?
What is MongoDB?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
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.
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.
While we initially started off running our own Postgres cluster, we evaluated RDS and found it to be an excellent fit for us.
The failovers, manual scaling, replication, Postgres upgrades, and pretty much everything else has been super smooth and reliable.
We'll probably need something a little more complex in the future, but RDS performs admirably for now.
We are using RDS for managing PostgreSQL and legacy MSSQL databases.
Unfortunately while RDS works great for managing the PostgreSQL systems, MSSQL is very much a second class citizen and they don't offer very much capability. Infact, in order to upgrade instance storage for MSSQL we actually have to spin up a new cluster and migrate the data over.
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.
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.
Because you don't know what data you will be querying for. Perfectly suited for rapid prototyping. Altering tables in traditional, relational DBMS is painfully expensive and slow.
Performance is not great though. Ah who cares!
Our PostgreSQL servers, where we keep the bulk of Wirkn data, are hosted on the fantastically easy and reliable AWS RDS platform.
We use Aurora for our OLTP database, it provides significant speed increases on top of MySQL without the need to manage it