MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.
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Percona Server for MongoDB
Score 8.4 out of 10
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Percona Server for MongoDB is a free and open-source drop-in replacement for MongoDB Community Edition. It combines all the features and benefits of MongoDB Community Edition with enterprise-class features from Percona. Built on the MongoDB Community Edition, Percona Server for MongoDB provides flexible data structure, native high availability, easy scalability, and developer-friendly syntax. It also includes an in-memory engine, hot backups, LDAP authentication, database auditing, and log…
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Percona Server for MongoDB
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Percona Server for MongoDB
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Fully managed, global cloud database on AWS, Azure, and GCP
At the performance level, it is similar to other solutions such as MongoDB and Percona Server for MySQL. and at the customization level, it offers better support for the development of specific solutions that seek good performance in transactions.
If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.
It offers good support for the implementation of solutions in the public and on-premises cloud and integration with other services such as Hashicorp Vault for data encryption. One of the main advantages is the ease of configuration, in addition to offering transaction support for the different operations and scalability of the servers.
Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.
An aggregate pipeline can be a bit overwhelming as a newcomer.
There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.
I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.
NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.
One aspect to improve is the user experience since sometimes the steps to take are not clear and the user may need to review some of the actions before continuing with the next ones. Another aspect to improve is the documentation and support for developers who want to know the tool.
Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.
It offers good support for the implementation of solutions in the public and on-premises cloud and integration with other services such as Hashicorp Vault for data encryption. Also, it offers support for different compatible programming languages such as C, C ++, Java, as well as offering good support for the persistence of schema-free data and the possibility of saving data in memory.
While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.
We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.
At the performance level, it is similar to other solutions such as MongoDB and Percona Server for MySQL. and at the customization level, it offers better support for the development of specific solutions that seek good performance in transactions.
Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB