---
_id: '65178'
abstract:
- lang: eng
  text: "Large intermediate results can cause join queries to run unexpectedly long.
    This problem is particularly common for analytical queries, which aggregate data
    over many tables to produce a comparatively small final output, and queries on
    graph data, where intermediate results blow up quickly. Recent work inspired by
    Yannakakis’ algorithm approaches this by modifying the query engine to avoid materializing
    unnecessary tuples. However, this requires significant changes to the core of
    the system, which is not feasible in many situations such as cloud environments
    or proprietary systems.\r\nIn this work, we propose a flexible approach for optimizing
    long-running join queries from the outside of the DBMS. Rewriting-based realizations
    of Yannakakis’ algorithm suffer from inherent overhead due to the creation of
    intermediate tables. Thus, we present an approach for detecting and targeting
    queries which would benefit from a Yannakakis-style optimization. We introduce
    a new benchmark combining 5 standard benchmarks and augmenting them with additional
    instances, which provides a sufficient size and diversity for a machine learning
    based solution. On PostgreSQL, DuckDB and SparkSQL, slowdowns on queries where
    the rewriting is counterproductive are mostly avoided, as opposed to a naïve application
    of the rewriting, and we observe significant improvements in end-to-end runtimes
    over standard query execution and unconditional rewriting."
author:
- first_name: Daniela
  full_name: Böhm, Daniela
  last_name: Böhm
- first_name: Georg
  full_name: Gottlob, Georg
  last_name: Gottlob
- first_name: Matthias
  full_name: Lanzinger, Matthias
  last_name: Lanzinger
- first_name: Davide Mario
  full_name: Longo, Davide Mario
  last_name: Longo
- first_name: Cem
  full_name: Okulmus, Cem
  id: '114410'
  last_name: Okulmus
  orcid: 0000-0002-7742-0439
- first_name: Reinhard
  full_name: Pichler, Reinhard
  last_name: Pichler
- first_name: Alexander
  full_name: Selzer, Alexander
  last_name: Selzer
citation:
  ama: 'Böhm D, Gottlob G, Lanzinger M, et al. Selective Use of Yannakakis’ Algorithm
    for Consistent Performance Gains. In: <i>Proceedings of the 28th International
    Workshop on Design, Optimization, Languages and Analytical Processing of Big Data
    (DOLAP 2026)</i>. ; 2026.'
  apa: Böhm, D., Gottlob, G., Lanzinger, M., Longo, D. M., Okulmus, C., Pichler, R.,
    &#38; Selzer, A. (2026). Selective Use of Yannakakis’ Algorithm for Consistent
    Performance Gains. <i>Proceedings of the 28th International Workshop on Design,
    Optimization, Languages and Analytical Processing of Big Data (DOLAP 2026)</i>.
  bibtex: '@inproceedings{Böhm_Gottlob_Lanzinger_Longo_Okulmus_Pichler_Selzer_2026,
    place={Tampere, Finland}, title={Selective Use of Yannakakis’ Algorithm for Consistent
    Performance Gains}, booktitle={Proceedings of the 28th International Workshop
    on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP
    2026)}, author={Böhm, Daniela and Gottlob, Georg and Lanzinger, Matthias and Longo,
    Davide Mario and Okulmus, Cem and Pichler, Reinhard and Selzer, Alexander}, year={2026}
    }'
  chicago: Böhm, Daniela, Georg Gottlob, Matthias Lanzinger, Davide Mario Longo, Cem
    Okulmus, Reinhard Pichler, and Alexander Selzer. “Selective Use of Yannakakis’
    Algorithm for Consistent Performance Gains.” In <i>Proceedings of the 28th International
    Workshop on Design, Optimization, Languages and Analytical Processing of Big Data
    (DOLAP 2026)</i>. Tampere, Finland, 2026.
  ieee: D. Böhm <i>et al.</i>, “Selective Use of Yannakakis’ Algorithm for Consistent
    Performance Gains,” 2026.
  mla: Böhm, Daniela, et al. “Selective Use of Yannakakis’ Algorithm for Consistent
    Performance Gains.” <i>Proceedings of the 28th International Workshop on Design,
    Optimization, Languages and Analytical Processing of Big Data (DOLAP 2026)</i>,
    2026.
  short: 'D. Böhm, G. Gottlob, M. Lanzinger, D.M. Longo, C. Okulmus, R. Pichler, A.
    Selzer, in: Proceedings of the 28th International Workshop on Design, Optimization,
    Languages and Analytical Processing of Big Data (DOLAP 2026), Tampere, Finland,
    2026.'
date_created: 2026-03-27T15:20:54Z
date_updated: 2026-03-27T15:22:01Z
department:
- _id: '888'
keyword:
- Join Queries
- Acyclic Queries
- Query Processing
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://ceur-ws.org/Vol-4186/paper2.pdf
oa: '1'
place: Tampere, Finland
publication: Proceedings of the 28th International Workshop on Design, Optimization,
  Languages and Analytical Processing of Big Data (DOLAP 2026)
status: public
title: Selective Use of Yannakakis’ Algorithm for Consistent Performance Gains
type: conference
user_id: '114410'
year: '2026'
...