Data Management Strategies for Distributed Systems
The activity of handling data in the distributed system is mainly associated with a set of certain problems, that include consistency, replication, and partitioning. Some key considerations include:
- Replication: Replicated data over multiple nodes for fight redundancy.
- Partitioning/Sharding: Data Traversing Multiple Nodes to Overcome Scalability Issues.
- Consistency Models: When applied to the problem that arises due to the conflict between coefficient of consistency and scalability, consistency is the star (ranging from strong consistency (strict data consistency) to eventual consistency (relaxed constraints for the sake of scalability )).
- Distributed Transactions: The approaches 2PC and Paxos/Raft can be applied together for both consistency and consensus.
- Data Storage: Deciding which traditional relational databases or NoSQL database would be favorable based on your particular use case.
How to build a Distributed System?
A distributed system is a system where there are separate components (nodes, servers, etc.) that are integrally linked to each other to perform the operations. These systems will be created for the capability to scale, resilience, and fault tolerance. They communicate and also collaborate their operations through networks that enable the processing, storing, and sharing of resources in a decentralized manner.
Important Topics for how to build a Distributed System
- Key Concepts for Distributed Systems
- Design Principles for Distributed Systems
- Architectural Patterns for Distributed Systems
- Communication Protocols for Distributed Systems
- Data Management Strategies for Distributed Systems
- Concurrency and Consistency Control in Distributed Systems
- Scalability and Performance Optimization in Distributed Systems
- Security Considerations for Distributed Systems
- Deployment and Operations in Distributed Systems