Distributed Databases Guide

Distributed databases are essential for handling large-scale data storage and processing. They spread data across multiple physical locations, enabling scalability, fault tolerance, and high availability. 🌍💾

Key Concepts

  • Data Sharding: Split data into smaller parts (shards) distributed across servers.
    Data_Sharding
  • Replication: Maintain copies of data across nodes to ensure redundancy.
    Replication_Techniques
  • Consistency Models: Define rules for data consistency (e.g., eventual consistency).
    Consistency_Models

Common Architectures

  • Horizontal Scaling: Add more nodes to distribute load.
    Horizontal_Scaling
  • Paxos/Raft: Protocols for consensus in distributed systems.
    Paxos_Raft
  • Hybrid Models: Combine sharding and replication for optimized performance.
    Hybrid_Models

Best Practices

  • Use data partitioning strategies aligned with access patterns.
  • Monitor network latency and node health with tools like Prometheus.
  • Implement database security measures to protect distributed data.
    Database_Security

For deeper insights into distributed database design, check our Knowledge Base for advanced topics. 📚🔗