Distributed databases are systems that store data across multiple physical locations, enabling scalability, fault tolerance, and high availability. Here's a breakdown of key concepts and components:
Core Principles
- Decentralization: Data is partitioned and replicated across nodes (e.g.,
database_sharding), reducing reliance on a single server. - Consistency Models: Tools like Paxos or Raft ensure data consistency across distributed systems.
- Fault Tolerance: Redundancy and replication (e.g.,
distributed_systems_resilience) allow systems to recover from failures.
Architecture Overview
- Node Communication: Requires reliable networking (e.g.,
networking_architecture) for synchronization. - Data Distribution: Strategies include range-based, hash-based, or hybrid partitioning.
- Query Processing: Complex queries must traverse multiple nodes, often using consensus protocols like
Consensus_Protocols(link: /tech/consensus-protocols).
Use Cases
- Global Applications: Ideal for services needing low latency across regions.
- Big Data Analytics: Enables parallel processing of massive datasets.
- High-Volume Transactions: Scales horizontally to handle increased load.
For deeper insights, explore our guide on database replication strategies. 📚