What is Clustering in an Open Data Lakehouse?
Blog post from Onehouse
Clustering is a storage optimization technique utilized in open table formats like Apache Hudi, Apache Iceberg, and Delta Lake to enhance query efficiency by reducing unnecessary file scans. It addresses the disparity between data ingestion order and query-access patterns by reorganizing data based on frequently queried fields, thus improving data locality and minimizing read latency. Linear sorting, hierarchical sorting, and multi-dimensional clustering, including advanced techniques like Z-order and Hilbert curves, are employed to optimize data layout and query performance, particularly in complex, multi-dimensional datasets. Apache Hudi offers flexible clustering capabilities through a dedicated service that supports asynchronous operations, balancing data ingestion with clustering. In contrast, Apache Iceberg and Delta Lake rely on manual clustering processes, focusing on strategies like linear sorting and Z-order clustering to enhance query performance. Delta Lake's introduction of Liquid Clustering and Hilbert curves further improves spatial locality and reduces write amplification. Overall, clustering is essential in data lakehouse architectures for aligning data layout with query patterns, ensuring efficient storage and fast query responses.
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