Oxla is focused on being the fastest distributed analytical database by implementing a range of optimizations that enhance performance and enable cost-effective use cases. The optimizations encompass single operation enhancements, data organization, planner improvements, and query engine architecture modifications. Examples include the use of columnar storage, vectorized query execution, and morsel-driven parallelism. To address challenges like those posed by ClickBench, which benchmarks database performance on tasks such as aggregations and high cardinality GROUP BY operations, Oxla has made numerous improvements. These include optimizing memory usage, using SIMD for length functions, balancing CPU workloads, and implementing faster operations for ORDER BY and LIKE. The cumulative effect of these optimizations has significantly boosted performance, with improvements in query execution times and efficiency. Some of these enhancements are still pending release, but Oxla aims to provide comprehensive documentation to facilitate quick user adoption.