{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/162924"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/162924","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"MINCE: Dialect-Aware SQL Decomposition for Federated Query Execution","abstract":"The increasing adoption of specialized database systems has led to the rise of heterogeneous data environments. While having multiple engines in a data infrastructure enables opportunities for workload optimization, SQL dialect incompatibility makes workload migration difficult. To address this challenge, we develop MINCE (Multi-dialect INtegration and Crossengine Execution), a technique that decomposes SQL queries into parts to enable federated execution across engines with differing SQL dialects. MINCE uses a rule-based method to partition a query into executable components that are assigned to different database systems. To evaluate different execution strategies, MINCE further implements a cost model that incorporates both on-engine query execution time and inter-system data transfer overhead. We evaluate MINCE on a TPC-H-based workload augmented with PostgreSQL-specific functions unsupported in Amazon Redshift. Experimental results show that MINCE produces the fastest execution strategy among our baselines for 72.1% of queries using estimated cardinality, achieving a 2× speedup over single-engine baselines. With perfect cardinality information available to our cost model, this value increases to 88.4%, with an average 2.8× speedup. These results demonstrate that our system not only enables more flexible federated query execution, but also reliably identifies performant execution strategies.","abstract_html":"The increasing adoption of specialized database systems has led to the rise of heterogeneous data environments. While having multiple engines in a data infrastructure enables opportunities for workload optimization, SQL dialect incompatibility makes workload migration difficult. To address this challenge, we develop MINCE (Multi-dialect INtegration and Crossengine Execution), a technique that decomposes SQL queries into parts to enable federated execution across engines with differing SQL dialects. MINCE uses a rule-based method to partition a query into executable components that are assigned to different database systems. To evaluate different execution strategies, MINCE further implements a cost model that incorporates both on-engine query execution time and inter-system data transfer overhead. We evaluate MINCE on a TPC-H-based workload augmented with PostgreSQL-specific functions unsupported in Amazon Redshift. Experimental results show that MINCE produces the fastest execution strategy among our baselines for 72.1% of queries using estimated cardinality, achieving a 2× speedup over single-engine baselines. With perfect cardinality information available to our cost model, this value increases to 88.4%, with an average 2.8× speedup. These results demonstrate that our system not only enables more flexible federated query execution, but also reliably identifies performant execution strategies.","abstract_has_math":false,"creators":["Zhang, Sophie S."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Kraska, Tim"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-22T22:22:17Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/162924","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kraska, Tim"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Zhang, Sophie S."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-10-06T17:34:57Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-10-06T17:34:57Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Engineering in Electrical Engineering and Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/162924"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The increasing adoption of specialized database systems has led to the rise of heterogeneous data environments. While having multiple engines in a data infrastructure enables opportunities for workload optimization, SQL dialect incompatibility makes workload migration difficult. To address this challenge, we develop MINCE (Multi-dialect INtegration and Crossengine Execution), a technique that decomposes SQL queries into parts to enable federated execution across engines with differing SQL dialects. MINCE uses a rule-based method to partition a query into executable components that are assigned to different database systems. To evaluate different execution strategies, MINCE further implements a cost model that incorporates both on-engine query execution time and inter-system data transfer overhead. We evaluate MINCE on a TPC-H-based workload augmented with PostgreSQL-specific functions unsupported in Amazon Redshift. Experimental results show that MINCE produces the fastest execution strategy among our baselines for 72.1% of queries using estimated cardinality, achieving a 2× speedup over single-engine baselines. With perfect cardinality information available to our cost model, this value increases to 88.4%, with an average 2.8× speedup. These results demonstrate that our system not only enables more flexible federated query execution, but also reliably identifies performant execution strategies."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["MINCE: Dialect-Aware SQL Decomposition for Federated Query Execution"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kraska, Tim"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Zhang, Sophie S."],"dc:date.accessioned":["2025-10-06T17:34:57Z"],"dc:date.available":["2025-10-06T17:34:57Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["The increasing adoption of specialized database systems has led to the rise of heterogeneous data environments. While having multiple engines in a data infrastructure enables opportunities for workload optimization, SQL dialect incompatibility makes workload migration difficult. To address this challenge, we develop MINCE (Multi-dialect INtegration and Crossengine Execution), a technique that decomposes SQL queries into parts to enable federated execution across engines with differing SQL dialects. MINCE uses a rule-based method to partition a query into executable components that are assigned to different database systems. To evaluate different execution strategies, MINCE further implements a cost model that incorporates both on-engine query execution time and inter-system data transfer overhead. We evaluate MINCE on a TPC-H-based workload augmented with PostgreSQL-specific functions unsupported in Amazon Redshift. Experimental results show that MINCE produces the fastest execution strategy among our baselines for 72.1% of queries using estimated cardinality, achieving a 2× speedup over single-engine baselines. With perfect cardinality information available to our cost model, this value increases to 88.4%, with an average 2.8× speedup. These results demonstrate that our system not only enables more flexible federated query execution, but also reliably identifies performant execution strategies."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/162924"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["MINCE: Dialect-Aware SQL Decomposition for Federated Query Execution"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:22:17Z"}