Benchmarking the Full Pipeline of Materialized-View-Based Query Rewriting

DAGSmith: Dependency-Aware Rewriting for dbt-Style SQL Pipelines

DAGSmith is introduced, to the best of the authors' knowledge the first holistic dependency-aware source-to-source rewriting system for SQL pipeline DAGs and enables dependency-edge simplification, non-local semantic reuse, downstream-aware pruning, pipeline-aware work placement, rewrite-materialization co-optimization, and frequency-aware optimization.

Enzyme Demo: Incremental View Maintenance for Data Engineering

Reducing the Cross-Model Tax: Query Optimization over Multi-Model Data

Querying across heterogeneous data models incurs substantial overhead from query decomposition, data transfer, and processing outside the underlying database systems. We show that, in the evaluated decomposition-based architecture, a substantial part of this cross-model tax is not inherent to heterogeneity itself, but results from avoidable decisions made by the unifying query processor. We present a mapping- and capability-aware optimization approach that systematically moves processing closer to the data. It combines model-aware predicate pushdown, cross-model dependent joins, and non-redundant query-part construction within a unified optimization pipeline applicable across relational, document, and graph databases. The approach is implemented in MM-quecat and evaluated over PostgreSQL, MongoDB, Neo4j, and their heterogeneous combination. It reduces query latency by up to two orders of magnitude, eliminates all out-of-memory failures observed in the original single-DBMS experiments, provides further order-of-magnitude improvements through dependent execution, and reduces planning time for complex graph plans from hundreds of milliseconds to several milliseconds. The results demonstrate that established optimization principles can be generalized across data-model and system boundaries and can substantially improve the efficiency and robustness of decomposition-based multi-model query processing.

BatchDAG: LLM-Planned Execution Graphs for Scalable Ad-Hoc Analysis Over Enterprise Data

BatchDAG is a general-purpose orchestration layer that replaces multiple hand-engineered workflows with a single system that generates the appropriate execution strategy from natural language, and is not primarily an accuracy improvement over hand-optimized pipelines.

Maintainable, Low-Latency and High-Quality Query Compilation with MLIR and TPDE

LLM-augmented query optimization: a hybrid framework for intelligent SQL performance tuning

LLM-QOpt++ is presented, a novel hybrid, confidence-aware query optimization framework that unifies traditional CBO estimation, machine learning–based cost prediction, and large language model (LLM) reasoning within a single adaptive pipeline.