1. Document-oriented Storage: MongoDB stores data in JSON-like documents with dynamic schemas, making the integration of data in applications easier and faster. For example, a product document in MongoDB may look like this:
{
name: “Laptop”,
description: “Lenovo Thinkpad T480”,
price: 800
}
2. Indexing: MongoDB supports indexing on any field in a document which makes data retrieval faster. For example, if you want to find all the products with a price greater than $500, you can create an index on the price field and MongoDB will use it to quickly locate the documents you need.
3. Replication: MongoDB provides high availability with replica sets. A replica set consists of two or more copies of the data. All replica set members are synchronised, and one member is designated as the primary node, which receives all write operations. The other members, known as secondaries, replicate the primary’s data set.
4. Load balancing: MongoDB uses a technique called “sharding” to support deployments with very large data sets and high throughput operations. Sharding splits the data across multiple machines, so that the data can be spread out and accessed in parallel.
5. Aggregation: MongoDB has powerful aggregation capabilities that allow you to process large amounts of data and return computed results. For example, you can use the aggregation framework to calculate the average price of all the products in the collection.