PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!

Summary: PerfGuard provides a pre-production safeguard for ML-for-systems, reducing deployment regressions. It confines search to query-plan deltas, learns delta-cost signals with a DL pipeline, and highlights key plan components, showing offline promise for relational DBs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12547
Venue: VLDB
Year: 2021
Pagerank: 4.551524e-05
Overall Rank: 8,219 | 42.88%
DOI: 10.14778/3484224.3484233

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,687	Deploying a Steered Query Optimizer in Production at Microsoft	2022	SIGMOD	5.9915268e-05
5,941	Eraser: Eliminating Performance Regression on Learned Query Optimizer	2024	VLDB	5.2594013e-05
7,652	Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward	2021	VLDB	4.6831938e-05
8,003	The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions	2024	VLDB	4.6049527e-05
10,018	GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints	2026	SIGMOD	4.1905499e-05
10,217	This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch!	2026	SIGMOD	4.1905499e-05
10,271	OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning	2026	VLDB	4.1905499e-05
10,863	Graph Transformers for Query Plan Representation: Potentials and Challenges	2025	VLDB	4.1905499e-05

Outgoing Citations (Sorted by Pagerank)

Showing 25 of 25 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
22	SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets	2008	VLDB	0.00084679526
101	The Case for Learned Index Structures	2018	SIGMOD	0.00049778866
203	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00034868567
329	Neo: A Learned Query Optimizer	2019	VLDB	0.00027301488
606	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00019251186
752	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.00017138049
796	SageDB: A Learned Database System	2019	CIDR	0.00016541749
819	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00016237497
905	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00015423174
950	Runtime Measurements in the Cloud: Observing, Analyzing, and Reducing Variance	2010	VLDB	0.00015100872
1,017	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.00014627121
1,239	Selectivity Estimation for Range Predicates using Lightweight Models	2019	VLDB	0.00013091459
1,699	Are We Ready For Learned Cardinality Estimation?	2021	VLDB	0.00010848882
1,856	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00010319105
2,080	Towards a Learning Optimizer for Shared Clouds	2019	VLDB	9.5954034e-05
2,364	Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries	2020	SIGMOD	8.955077e-05
2,769	FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation	2021	VLDB	8.1512848e-05
2,781	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	8.1282042e-05
3,623	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	6.9017341e-05
3,924	A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation	2021	SIGMOD	6.6227223e-05
3,955	Efficiently Approximating Selectivity Functions using Low Overhead Regression Models	2020	VLDB	6.5895015e-05
4,069	CDFShop: Exploring and Optimizing Learned Index Structures	2020	SIGMOD	6.4744708e-05
5,477	Learned Cardinality Estimation for Similarity Queries	2021	SIGMOD	5.4856699e-05
5,994	Steering Query Optimizers: A Practical Take on Big Data Workloads	2021	SIGMOD	5.2367998e-05
7,685	AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft	2020	VLDB	4.6753414e-05

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