Pasta: A Cost-Based Optimizer for Generating Pipelining Schedules for Dataflow DAGs

Summary: Introduces Pasta, a cost-based optimizer for scheduling pipelined execution of dataflow DAGs. General across cost models, it exploits cost-function structure to reduce materialization and improve plan quality on real-world workflows. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 7002
Venue: SIGMOD
Year: 2024
Pagerank: 4.1905499e-05
Overall Rank: 10,999 | 23.56%
DOI: 10.1145/3698832

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
977	Pipelining in Multi-Query Optimization	2001	PODS	0.000148774
1,826	Optimization Algorithms for Exploiting the Parallelism-Communication Tradeoff in Pipelined Parallelism	1994	VLDB	0.00010392071
2,416	Dynamic Load Balancing in Hierarchical Parallel Database Systems	1996	VLDB	8.8520203e-05
3,265	On Optimal Processor Allocation To Support Pipelined Hash Joins	1993	SIGMOD	7.3055541e-05
5,062	Optimizing Machine Learning Inference Queries with Correlative Proxy Models	2022	VLDB	5.7172262e-05
6,464	Materialization and Reuse Optimizations for Production Data Science Pipelines	2022	SIGMOD	5.0471003e-05
8,040	Amber: A Debuggable Dataflow System Based on the Actor Model	2020	VLDB	4.5957133e-05
8,742	Texera: A System for Collaborative and Interactive Data Analytics Using Workflows	2024	VLDB	4.4520434e-05
9,199	Demonstration of Collaborative and Interactive Workflow-Based Data Analytics in Texera	2022	VLDB	4.3706402e-05
11,274	Building a Collaborative Data Analytics System: Opportunities and Challenges	2023	VLDB	4.1905499e-05

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