MongoDB is schema-less, meaning documents in the same collection can have different structures. This provides flexibility for applications where the data model may evolve over time.
In contrast, RDBMS requires a fixed schema, and altering the schema (e.g., adding columns or changing data types) often requires migrations.
Scalability:
MongoDB is designed to scale horizontally by sharding data across multiple servers. This makes it ideal for handling large amounts of data and high throughput.
RDBMS traditionally scales vertically (by adding more CPU, RAM, or storage to a single machine), which can become more expensive and less efficient at large scales.
High Availability:
MongoDB supports replication natively via Replica Sets, ensuring high availability and fault tolerance. If a primary node fails, a secondary node can take over as the new primary.
RDBMS requires additional complex setups like master-slave replication for similar redundancy.
Performance and Speed:
MongoDB is optimized for fast writes and reads, and it can handle large datasets with complex queries without significant performance degradation.
RDBMS can sometimes suffer from performance bottlenecks when handling large amounts of unstructured or semi-structured data.
Document-Oriented Storage:
MongoDB stores data in JSON-like BSON format, which is naturally hierarchical and allows nested data structures (arrays, sub-documents, etc.), making it more suited to modern applications dealing with complex, nested data.
RDBMS uses tables and rows, which are not naturally hierarchical and require joining multiple tables for complex queries, leading to more complexity in handling nested data.
Dynamic Queries and Aggregation:
MongoDB provides a powerful aggregation framework, which allows complex querying and data transformation operations to be performed efficiently.
While RDBMS supports queries using SQL, handling complex aggregations and joins can be more challenging and performance-intensive.
Cost-Effectiveness:
Since MongoDB scales horizontally, adding more machines can be more cost-effective than vertically scaling a single server in RDBMS.
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