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About 3,150 papers for “Query plan”

Query Plan

Evaggelia Pitoura · Encyclopedia of Database Systems · 2009

Cited by 0

Query Evaluation Plan

LING LIU, M. TAMER ÖZSU · Encyclopedia of Database Systems · 2009

Cited by 0

Query Execution Plan

Encyclopedia of Database Systems · 2009

Cited by 0

In this phase, you will develop an additional component called Query Rewriter and shown in the architecture below to optimize logical query plans and decide the order of joins using cost estimates. You will also enrich some of the existing components to support joins.

Cited by 0

Hybrid query plan generation

Carlos-Manuel López-Enríquez, Genoveva Vargas‐Solar, José-Luis Zechinelli-Martini et al. · HAL (Le Centre pour la Communication Scientifique Directe) · 2012

http://ceur-ws.org/Vol-911 - Regular Paper

Cited by 0Open access

Optimized Query Plan Algorithm for the Nested Query

Chittaranjan Pradhan, Sushree Sangita Jena · 2013

Abstract- The SQL language allows users to express the queries that have nested sub-queries in them. Optimization of nested queries has received considerable attention over the last few years. Most of the previous optimization work has assumed that at most one block is nested within any given block. The two main contributions of this report are: 1. Optimization strategies for queries that have an arbitrary number of blocks nested within any given block, and 2. a new algorithm for the execution of nested queries, involving one or more other joins, in a multi-processor environment. The new algorithm cuts down the processing costs over conventional algorithms.

Cited by 0

Query Compilation and Plan Synthesis

David Toman, Grant Weddell · Synthesis lectures on data management · 2011

Cited by 0

Learned Query Optimization by Constraint-Based Query Plan Augmentation

Chen Ye, Haoyang Duan, Hua Zhang et al. · Mathematics · 2024

Over the last decades, various cost-based optimizers have been proposed to generate optimal plans for SQL queries. These optimizers are key to achieving good performance in database systems and can speed up query execution. Still, they may need enormous expert efforts and perform poorly on complicated queries. Learning-based optimizers have been shown to achieve high-quality plans by learning from past experiences. However, these solutions treat each query separately and neglect the semantic equivalence among different queries. Intuitively, a high-quality plan may be obtained for a complicated query by discovering a simple equivalent query. Motivated by this, in this paper, we present Celo, a novel constraint-enhanced learned optimizer to directly integrate the equivalent information of queries into the learning-based model. We apply denial constraints to identify equivalent queries by replacing equivalent predicates. Given a query, we augment the query plans generated by the learning-based model with the high-quality plans of its equivalent queries. Then, a more potentially well-performed plan will be predicted among the augmented query plans. Extensive experiments using real-world datasets demonstrated that Celo outperforms the previous state-of-the-art (SOTA) results even with few constraints.

Cited by 1Open access

Plan Selection based on Query Clustering

Antara Ghosh Jignashu, Antara Ghosh, Jignashu Parikh et al. · 2002

Query optimization is a computationally intensive process, especially for complex queries. We present here a tool, called PLASTIC, that can be used by query optimizers to amortize the optimization cost. Our scheme groups similar queries into clusters and uses the optimizer-generated plan for the cluster representative to execute all future queries assigned to the cluster. Query similarity is evaluated based on a comparison of query structures and the associated table schemas and statistics, and a classifier is employed for efficient cluster assignments. Experiments with a variety of queries on a commercial optimizer show that PLASTIC predicts the correct plan choice in most cases, thereby providing significantly improved query optimization times. Further, when errors are made, the additional execution cost incurred due to the sub-optimal plan choices is marginal.

Cited by 0

Towards a Unified Query Plan Representation

Jinsheng Ba, Manuel Rigger · 2025

In database systems, a query plan is a series of concrete internal steps to execute a query. Multiple testing approaches utilize query plans for finding bugs. However, query plans are represented in a database-specific manner, so implementing these testing approaches requires a non-trivial effort, hindering their adoption. We envision that a unified query plan representation can facilitate the implementation of these approaches. In this paper, we present an exploratory case study to investigate query plan representations in nine widely-used database systems. Our study shows that query plan representations consist of three conceptual components: operations, properties, and formats, which enable us to design a unified query plan representation. Based on it, existing testing methods can be efficiently adopted, finding 17 previously unknown and unique bugs. Additionally, the unified query plan representation can facilitate other applications. Existing visualization tools can support multiple database systems based on the unified query plan representation with moderate implementation effort, and comparing unified query plans across database systems provides actionable insights to improve their performance. We expect that the unified query plan representation will enable the exploration of additional application scenarios.

Cited by 0

Reduction of Query Optimizer Plan Diagrams

Harish D. Pooja, N. Darera, Jayant R. Haritsa · 2014

A “plan diagram ” is a pictorial enumeration of the execution plan choices of a database query optimizer over the relational selectivity space. We have shown recently that, for industrial-strength database engines, these diagrams are often remarkably complex and dense, with a large number of plans covering the space. However, they can often be reduced to much simpler pictures, featuring significantly fewer plans, without materially affecting the query processing quality. Plan reduc-tion has useful implications for the design and usage of query optimizers, including quantifying redundancy in the plan search space, enhancing useability of parametric query optimization, iden-tifying error-resistant and least-expected-cost plans, and minimizing the overheads of multi-plan approaches. We investigate here the plan reduction issue from theoretical, statistical and empirical perspec-tives. Our analysis shows that optimal plan reduction, w.r.t. minimizing the number of plans, is an NP-hard problem in general, and remains so even for a storage-constrained variant. We then present a greedy reduction algorithm with tight and optimal performance guarantees, whose complexity scales linearly with the number of plans in the diagram for a given resolution. Next, we devise fast estimators for locating the best tradeoff between the reduction in plan cardinality and the impact on query processing quality. Finally, extensive experimentation with a suite of multi-dimensional TPC-H and TPC-DS based query templates on industrial-strength optimizers demonstrates that complex plan diagrams easily reduce to “anorexic ” (small absolute number of plans) levels incurring only marginal increases in the estimated query processing costs. 1

Cited by 0

ReoptSMART: A Learning Query Plan Cache

Julia Stoyanovich, Kenneth A. Ross, Jun Rao et al. · Columbia Academic Commons (Columbia University) · 2008

The task of query optimization in modern relational database systems is important but can be computationally expensive. Parametric query optimization(PQO) has as its goal the prediction of optimal query execution plans based on historical results, without consulting the query optimizer. We develop machine learning techniques that can accurately model the output of a query optimizer. Our algorithms handle non-linear boundaries in plan space and achieve high prediction accuracy even when a limited amount of data is available for training. We use both predicted and actual query execution times for learning, and are the first to demonstrate a total net win of a PQO method over a state-of-the-art query optimizer for some workloads. ReoptSMART realizes savings not only in optimization time, but also in query execution time, for an over-all improvement by more than an order of magnitude in some cases.

Cited by 3Open access

Towards a Unified Query Plan Representation

Ba, Jinsheng, Rigger, Manuel · Zenodo (CERN European Organization for Nuclear Research) · 2025

The artifact for the ICDE'25 paper "Towards a Unified Query Plan Representation"

Cited by 0Open access

Towards a Unified Query Plan Representation

Jinsheng Ba, Manuel Rigger · arXiv (Cornell University) · 2024

Cited by 1Open access

Creating routing plan for keyword query

Pranali Nunes, Anuradha Srinivasaraghavan, Varsha Nagpurkar · 2017

Keyword search is an inherent example for searching linked data sources on the web. The proposed work tries to route keywords only to relevant sources. This would also reduce the high cost of processing keyword search queries over all sources. This proposed work also tries to compute top-k routing places based on their potentials to contain results for a given keyword query. The number of potential results may increase exponentially with the number of sources and links between them. Most of the results may be not necessary especially when they are not relevant to the user. The proposed work uses to solve the problem of keyword search over large number of linked and structured data source by using keyword expansion.

Cited by 0

Execution Plan Generation and the Query Optimizer

Grant Fritchey · Apress eBooks · 2022

All performance within SQL Server starts, and frequently ends, with the query optimizer. Because the optimizer is so important to query performance, we’re diving immediately into how the optimizer behaves in order to better understand why our queries behave the way they do. The optimization process results in an execution plan, a mechanism for showing the choices the optimizer made. The optimization process is expensive in terms of resources, especially CPU, and can be somewhat time-consuming, affecting the execution time of your queries. We’ll go over what the process is, how an execution plan is generated, and how that plan goes into memory in the plan cache in the hopes of reusing the plan.

Cited by 1

Dynamic Query Plan for Efficient Query Processing in Peer-to-Peer Environments

M. A. Masud, Hossain, M. A. · Americanae (AECID Library) · 2008

Abstract. In the past few years, Peer-to-Peer (P2P) applications have emerged as a popular way of autonomously sharing data and services in distributed environments. In such environments, peers dynamically join/leave a network and do not usually have any global knowledge about the data sources. This phenomenon demands an efficient query execution strategy in terms of data transmission and response time. In this paper, we propose a distributed query processing technique in P2P environments where each peer possesses partial knowledge of the domain information and collaborates with the participating peers to share data. The cornerstone of our approach is to dynamically determine the best query execution plan in order to execute the different parts of the user query, and propagate the results with minimized data transmission and response time. Keywords: Peer-to-peer, Information retrieval, Query processing.

Cited by 2

Plan Bouquets: Query Processing without Selectivity Estimation

Anshuman Dutt, Jayant R. Haritsa · 2016

Selectivity estimates for optimizing OLAP queries often differ sig-nificantly from those actually encountered during query execution, leading to poor plan choices and inflated response times. We propose here a conceptually new approach to address this prob-lem, wherein the compile-time estimation process is completely es-chewed for error-prone selectivities. Instead, a small “bouquet ” of plans is identified from the set of optimal plans in the query’s selec-tivity error space, such that at least one among this subset is near-optimal at each location in the space. Then, at run time, the actual selectivities of the query are incrementally “discovered ” through a sequence of partial executions of bouquet plans, eventually iden-tifying the appropriate bouquet plan to execute. The duration and switching of the partial executions is controlled by a graded pro-gression of isocost surfaces projected onto the optimal performance

Cited by 0