Designing Your Data Lakehouse Tables for Fast Queries

Efficient query performance in a data lakehouse is significantly influenced by the organization and maintenance of data, as detailed in a guide on Onehouse optimization strategies. The guide emphasizes the importance of storing data with optimal file sizing, sorting, and indexing, particularly using Apache Parquet™, to balance read and write performance, and suggests a file size of 120 MB to minimize I/O operations. Onehouse's Clustering service automatically optimizes file sizes and sorts data to enhance query speed, advocating sorting data by frequently filtered columns and using advanced techniques like Z-Order for multi-dimensional data. Partitioning is recommended for improving file pruning, with a focus on avoiding small files and partition skew, monitored through the Onehouse console. Indexes, such as those in Apache Hudi™, are highlighted for accelerating lookups, while ingestion performance profiles in OneFlow provide options for balancing read and write speeds. The guide advises filtering queries on partition and sort columns, using appropriate data types, and optimizing joins through strategies like broadcasting small tables. It also discusses choosing the right query engines, with Onehouse offering managed engines for various use cases, ensuring that optimized data layout translates to consistently fast query performance.