Oxla's query execution engine optimizes performance and memory utilization by forming a pipeline of processors, where each processor computes output batches from input ones based on a subset of columns. The article introduces a problem called "Minimum Expression and Column Subset" (MECS) to minimize the number of expressions and columns processed, enhancing efficiency. The solution involves using graph theory and Hall's theorem to compute an optimal valid solution by determining a max matching in a bipartite graph representing dependencies between expressions and columns. The authors present a polynomial-time algorithm that provides a solution that is at most twice the size of the optimal, while further refinements can yield the optimal solution. This approach, while effective for nested SELECT queries, shows limitations with joins, requiring different optimization techniques. The article invites readers to explore Oxla's features firsthand through its BETA deployment.