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Blog post from Polar Signals
The blog post discusses the innovative use of Delta Lake tables and the integration of DataFusion for querying data within a Rust-based database system. The system uniquely distributes tenant data across two Delta Lake tables, requiring a process to read from both to get a comprehensive view. Initially, the custom delta-rs library fork was used for data operations, but the team explored DataFusion's capabilities to streamline this process. By building a DataFusion TableManager and the GreatLakes TableProvider, they enabled efficient parallel processing and SQL querying of the Delta Lake actions, enhancing performance. However, while DataFusion proved faster for large datasets, it incurred overhead costs that affected performance for smaller queries, such as those involving 10 commits, due to the physical planning and optimization steps. The team plans to refine this process to optimize performance further, balancing the use of DataFusion and bespoke code for varying query sizes.
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