Table Optimizer: The Optimal Way to Execute Table Services
Blog post from Onehouse
A data lakehouse architecture, which merges the benefits of data lakes and data warehouses, requires effective optimization techniques to handle data efficiently and avoid performance pitfalls. Key to this optimization is the use of metadata layers like Apache Hudi, Apache Iceberg, or Delta Lake, which abstract file management and enable various enhancements. Onehouse's Table Optimizer is a novel solution designed to streamline Apache Hudi table management, allowing data engineers to prioritize business logic while maintaining optimal table performance and reliability. The Table Optimizer, integrated with existing data pipelines, runs within a Spark application deployed on cloud services like AWS EKS or GCP GKE, and it efficiently schedules and executes table service tasks such as compaction, clustering, and metadata sync. By managing these tasks asynchronously and independently from data ingestion processes, the Table Optimizer reduces latency, enhances resource management, and minimizes complexity for users, ultimately improving the cost-effectiveness and reliability of data operations. Challenges in its development included ensuring no data corruption by effectively resolving conflicts between concurrent writers, which was addressed using distributed locking mechanisms and rigorous testing. The solution, now available for Hudi users, offers interoperability with Apache Iceberg and Delta Lake through Apache XTable, promising improved automation and user experience without significant changes to existing workflows.
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