Explain the concept of data partitioning and its relationship to clustering.
- Data partitioning involves clustering related data together to optimize query performance. Clustering groups unrelated data together on the same node to improve fault tolerance. Data partitioning and clustering are independent concepts and are not related.
- Data partitioning involves dividing a database into smaller parts to improve scalability and performance. Clustering groups related data together on the same node to enhance data locality. Data partitioning is often used in conjunction with clustering to further optimize data distribution and access patterns.
- Data partitioning involves dividing a database into smaller parts to reduce storage requirements. Clustering groups unrelated data together on the same node to simplify data management. Data partitioning and clustering serve the same purpose and are often used interchangeably.
- Data partitioning involves replicating data across multiple nodes to improve fault tolerance. Clustering groups related data together on the same node to reduce network overhead. Data partitioning and clustering are complementary concepts that work together to optimize database performance.
Data partitioning involves dividing a database into smaller parts to improve scalability and performance, while clustering groups related data together on the same node to enhance data locality. These concepts are often used together in distributed database systems to optimize data distribution and access patterns, improving overall system performance.
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