Introduction
MongoDB is a document-oriented database. It doesn't have any schema and stores documents in JSON format. It's intuitive for those familiar with JavaScript and easy to work with for storing complex, nested data—current version 7.0.
As a technology, MySQL and MongoDB are very different, but I will use MySQL references wherever possible to make it easier to understand.
MongoDB historically leans towards Consistency and Partition Tolerance (CP).
| MySQL | MongoDB |
|---|---|
Database | Database |
Table | Collection |
Row | Document |
Column | Field |
Index | Index |
Pros
Scalability: MongoDB is designed to scale horizontally by distributing data across multiple servers, making it suitable for handling large amounts of data and high traffic loads.
Flexibility: MongoDB's document data model allows for flexible and dynamic schema design, making it easier to handle evolving data structures.
High Performance: MongoDB's embedded data model and indexing capabilities can provide high read and write performance for specific workloads.
Rich Query Language: MongoDB's query language supports various operations, including ad-hoc queries, text searches, and geospatial queries.
Ease of Use: MongoDB's syntax and query language is relatively straightforward, making it easier for developers to learn and use than other NoSQL databases.
Replication and High Availability: MongoDB supports built-in replication and automatic failover, ensuring high availability and data redundancy.
Sharding: MongoDB's sharding feature allows for horizontal scaling by distributing data across multiple shards (partitions), enabling support for larger datasets.
Cons
Lack of Strict Schema: While flexibility is a strength, the lack of a strict schema can lead to data inconsistencies and make it more challenging to maintain data integrity.
Limited Transactions: MongoDB's transaction support was limited until version 4.0 (released in 2018), which introduced multi-document ACID transactions.
Limited Join Support: MongoDB's document data model does not natively support joins, which can make it more challenging to handle complex relational data structures.
Memory Usage: MongoDB's data model can lead to higher memory usage than traditional relational databases, especially for workloads with high write throughput or large documents.
Potential Data Duplication: Denormalization, often used in MongoDB to improve read performance, can lead to data duplication and potential inconsistencies.
Lack of Mature Tools: While the MongoDB ecosystem is growing, some developers may find the tooling and ecosystem less mature than long-established relational databases.
Single Writer Per Shard: In sharded environments, MongoDB only allows a single writer per shard at a time, which can limit write scalability for specific workloads.
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