In a distributed database system, what are some common techniques for achieving data consistency?
- Lambda architecture, Event sourcing, Data lake architectures, Data warehousing
- MapReduce algorithms, Bloom filters, Key-value stores, Data sharding
- RAID configurations, Disk mirroring, Clustering, Replication lag
- Two-phase commit protocol, Quorum-based replication, Vector clocks, Version vectors
Achieving data consistency in a distributed database system requires employing various techniques. Some common approaches include the two-phase commit protocol, which ensures all nodes commit or abort a transaction together, maintaining consistency across distributed transactions. Quorum-based replication involves requiring a certain number of replicas to agree on an update before committing, enhancing fault tolerance and consistency. Vector clocks and version vectors track causality and concurrent updates, enabling conflict resolution and consistency maintenance in distributed environments. These techniques play a vital role in ensuring data integrity and coherence across distributed systems.
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