Is horizontal scalability possible for a service that handles data processing and storage while maintaining state?

Horizontal scalability of a service that processes and stores data with state preservation is possible, but requires proper architecture and state management approaches.

Key points:

• State separation: To scale horizontally, the state must be either shareable or replicable among instances. For example, using external storage (databases, cache systems) to store state.

• Data consistency: It is necessary to ensure data consistency and integrity when multiple instances operate in parallel. This is achieved through transactions, distributed locks, or consensus algorithms.

• Sessions and user state: If the service stores sessions, it is better to move them to distributed storage (Redis, Memcached) or use tokens (JWT) to avoid binding to a specific instance.

• Idempotency and retry handling: When scaling horizontally, it is important that operations are idempotent to prevent errors during reprocessing.

Thus, horizontal scaling with state preservation is possible but requires architectural solutions aimed at separating and managing state outside the specific service instance.