MongoDB

Frequently Asked Questions about MongoDB

• Who would be responsible for designing my database schema?

  We provide you with the solution where your database is hosted and take care of the server and database engine administration. Since you know your business best, you will be able to define and structure your database efficiently.

• How much does traffic cost in Data Center Designer?

  Incoming traffic is free and unlimited. We only charge you for outgoing traffic. This is because our solution is designed to work in a private environment where the web server makes requests to MongoDB.

• What is MongoDB?

  MongoDB is the most representative of the databases known as NoSQL, an acronym for Not only SQL. It can also be referred to as a document database, as what we store are pure JSON documents and not records, as happens in relational database tables. NoSQL databases occupy a specific niche among current database solutions. They address common needs, both for web applications, mobile apps, or the Internet of Things.

• When is it necessary to use a NoSQL database like MongoDB?

  • For an application to store or access a lot of information in a short time because document databases can be much faster than relational ones and can serve clients who need to perform many operations per second.
  • To store large volumes of information. Relational databases tend to slow down when a table contains very large amounts of records (on the order of a million and up). Such situations force administrators to seek solutions like dividing tables into various segments, incurring a cost in data access and operations. This is not a problem in NoSQL databases, which can manage gigantic volumes of data in their entities.
  • To address enormous needs for speed, volume, and variability. In relational databases, the information schema is meticulously defined in advance. For example, you cannot invent fields in records on the fly. In document databases like MongoDB, there is no problem with each document storing different fields, allowing flexibility in the information schema.

• What is the difference between MongoDB and relational databases?

  Unlike relational databases that organize data into tables with rows and columns, MongoDB stores data in JSON (or BSON) documents, allowing for a flexible structure. Relational databases require fixed schemas, while MongoDB allows adding or modifying fields without interrupting operations, making it ideal for dynamic data and modern applications.

• Is MongoDB suitable for large volumes of data?

  Yes. MongoDB is ideal for handling large volumes of data due to its scalable architecture. It uses a sharding approach, which allows data to be divided and distributed across different servers, facilitating efficient processing of large amounts of information. This allows you to grow without performance being affected, making it an excellent choice for big data and applications with millions of users.

• What is a document in MongoDB?

  In MongoDB, a document is the basic unit of data stored in a collection. A document is similar to a record in a relational database but has a more flexible format, based on JSON or BSON (Binary JSON). Documents can contain fields and values of different data types, including arrays and nested documents, offering great flexibility for storing unstructured information.

• What is a collection in MongoDB?

  A collection in MongoDB is a set of documents and is equivalent to a table in a relational database. Unlike relational tables, a collection in MongoDB does not require a fixed schema, meaning that documents within the same collection can have different structures, with different fields and data types.

• What is sharding in MongoDB and when is it used?

  Sharding is a partitioning technique in MongoDB that distributes data across multiple servers or nodes. It is used to handle large volumes of data and to ensure that the system remains scalable and efficient. By dividing data into different shards, MongoDB can distribute the workload across different servers, avoiding bottlenecks and allowing for horizontal growth of the system.