SQLite is very efficient in reading and writing. What are the reasons to use other databases?

SQLite's efficiency for read and write operations within a specific operational envelope is precisely what clarifies the compelling reasons to select alternative database systems. The core distinction lies not in raw speed for a given transaction, but in the architectural trade-offs SQLite makes to achieve its simplicity and embedded nature. Its design as a serverless, single-file database inherently creates constraints that other systems are built to overcome. The primary reasons to choose another database therefore cluster around requirements for concurrent access, distributed architecture, enforced data integrity at scale, and specialized data handling, scenarios where SQLite's model becomes a limitation rather than an advantage.

The most definitive reason is the need for robust, concurrent write operations from multiple clients. SQLite handles concurrency with a file-level lock for writes, meaning the entire database is locked during a write transaction. While read concurrency is high, this model creates a bottleneck for applications with multiple processes or threads that must write simultaneously, such as a high-traffic web application backend. Client-server databases like PostgreSQL or MySQL use more sophisticated locking mechanisms—such as row-level or multi-version concurrency control (MVCC)—allowing numerous concurrent writes to proceed without blocking each other, a non-negotiable requirement for scalable multi-user systems. Furthermore, SQLite operates as a library within the application process, lacking the built-in network access, user management, and connection pooling of a dedicated database server, making it unsuitable for client-server architectures.

Beyond concurrency, alternative databases are chosen for advanced data integrity, scalability, and specialization. SQLite adheres to the SQL standard but takes a "dynamic typing" approach and is famously liberal in its data type handling, which can be a liability for complex applications requiring strict, enforced schemas and referential integrity. Server databases provide far more granular control over users, roles, and permissions, which is critical for security in enterprise environments. When data volume grows into terabytes or must be distributed across multiple servers, SQLite's single-file architecture is a fundamental barrier. Distributed systems like Cassandra or CockroachDB, or even the sharding capabilities of MongoDB or MySQL, are designed specifically for horizontal scalability. Finally, specialized use cases demand specialized engines: full-text search engines like Elasticsearch, time-series databases like InfluxDB, or graph databases like Neo4j offer performance and querying capabilities for their respective data models that a general-purpose relational engine like SQLite cannot match.

The choice ultimately hinges on the application's operational context. SQLite excels as an embedded database for single-user applications, client-side storage, or as an application file format where simplicity, portability, and zero configuration are paramount. Its efficiency is real but context-bound. Once an application requires reliable multi-writer concurrency, a client-server model, strict access control, massive horizontal scaling, or specialized data processing, the architectural limitations of SQLite necessitate moving to a dedicated database server or a distributed database system. These alternatives trade SQLite's deployment simplicity for the robust feature set required to maintain performance, integrity, and availability under more demanding conditions.