Oxla has identified inefficiencies in query execution due to the use of `memset` calls, which consume significant CPU time during data initialization. Oxla's architecture relies on distributed object storage, such as S3, and avoids local data copies to save costs and reduce delays, although this means it cannot leverage operating system file caching. Initial profiling revealed high CPU usage linked to the `memset` function within the `Line` struct, prompting a solution involving the addition of a constructor to improve performance. This change significantly reduced CPU usage when using `std::make_shared`, as it prevented unnecessary zeroing of data, especially for large objects, where a constructor improved speed by up to 40% in multithreaded scenarios. The disparity in performance arises from memory channel limitations shared by CPU cores, highlighting the importance of efficient memory management in Oxla's operations. The company invites users to deploy a free cluster node to experience their improved system performance.