MongoDB vs NuoDB: What are the differences?
What is 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.
What is NuoDB? A scale-out SQL database for global operations. NuoDB’s continuously available, ACID-compliant, SQL database delivers on-demand capacity on commodity hardware across multiple data centers.
MongoDB and NuoDB belong to "Databases" category of the tech stack.
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.
What is MongoDB?
What is NuoDB?
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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.
In my opinion PostgreSQL is totally over MongoDB - not only works with structured data & SQL & strict types, but also has excellent support for unstructured data as separate data type (you can store arbitrary JSONs - and they may be also queryable, depending on one of format's you may choose). Both writes & reads are much faster, then in Mongo. So you can get best on Document NoSQL & SQL in single database..
Formal downside of PostgreSQL is clustering scalability. There's not simple way to build distributed a cluster. However, two points:
1) You will need much more time before you need to actually scale due to PG's efficiency. And if you follow database-per-service pattern, maybe you won't need ever, cause dealing few billion records on single machine is an option for PG.
2) When you need to - you do it in a way you need, including as a part of app's logic (e.g. sharding by key, or PG-based clustering solution with strict model), scalability will be very transparent, much more obvious than Mongo's "cluster just works (but then fails)" replication.
I started using PostgreSQL because I started a job at a company that was already using it as well as MongoDB. The main difference between the two from my perspective is that postgres columns are a chore to add/remove/modify whereas you can throw whatever you want into a mongo collection. And personally I prefer the query language for postgres over that of mongo, but they both have their merits. Maybe someday I'll be a DBA and have more insight to share but for now there's my 2 cents.
Back at the start of 2017, we decided to create a web-based tool for the SEO OnPage analysis of our clients' websites. We had over 2.000 websites to analyze, so we had to perform thousands of requests to get every single page from those websites, process the information and save the big amounts of data somewhere.
Very soon we realized that the initial chosen script language and database, PHP, Laravel and MySQL, was not going to be able to cope efficiently with such a task.
By that time, we were doing some experiments for other projects with a language we had recently get to know, Go , so we decided to get a try and code the crawler using it. It was fantastic, we could process much more data with way less CPU power and in less time. By using the concurrency abilites that the language has to offers, we could also do more Http requests in less time.
Unfortunately, I have no comparison numbers to show about the performance differences between Go and PHP since the difference was so clear from the beginning and that we didn't feel the need to do further comparison tests nor document it. We just switched fully to Go.
There was still a problem: despite the big amount of Data we were generating, MySQL was performing very well, but as we were adding more and more features to the software and with those features more and more different type of data to save, it was a nightmare for the database architects to structure everything correctly on the database, so it was clear what we had to do next: switch to a NoSQL database. So we switched to MongoDB, and it was also fantastic: we were expending almost zero time in thinking how to structure the Database and the performance also seemed to be better, but again, I have no comparison numbers to show due to the lack of time.
As of now, we don't only use the tool intern but we also opened it for everyone to use for free: https://tool-seo.com
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!
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.
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.
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.
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