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About 92 papers for “Nested loop join”

Index Nested Loop Join

Ling Liu, M. TAMER ÖZSU · Encyclopedia of Database Systems · 2009

Cited by 0

Cluster Nested Loop k-Farthest Neighbor Join Algorithm for Spatial Networks

Hyung-Ju Cho · ISPRS International Journal of Geo-Information · 2022

This paper considers k-farthest neighbor (kFN) join queries in spatial networks where the distance between two points is the length of the shortest path connecting them. Given a positive integer k, a set of query points Q, and a set of data points P, the kFN join query retrieves the k data points farthest from each query point in Q. There are many real-life applications using kFN join queries, including artificial intelligence, computational geometry, information retrieval, and pattern recognition. However, the solutions based on the Euclidean distance or nearest neighbor search are not suitable for our purpose due to the difference in the problem definition. Therefore, this paper proposes a cluster nested loop join (CNLJ) algorithm, which clusters query points (data points) into query clusters (data clusters) and reduces the number of kFN queries required to perform the kFN join. An empirical study was performed using real-life roadmaps to confirm the superiority and scalability of the CNLJ algorithm compared to the conventional solutions in various conditions.

Cited by 1Open access

Performance evaluations on inner vs. outer decomposition first parallel join algorithms for two nested loop joins

S. Y. Noh, Heejun Yoon, Il-Yeon Yeo et al. · Journal of Central South University · 2014

Cited by 0

Optimization of joins using random record generation method

G. Hemalatha, K. Thanuskodi · 2010

Joins are statements that retrieve data from more than one table. A Join is characterized by multiple tables in the FROM clause, and the relationship between the tables is defined through the existence of a Join condition in the WHERE clause. In the case of Very large databases and highly normalized databases frequency of Join queries are high. To perform the Join Query much efficiently, the Join Method the optimizer selects are very vital. Nested Loop Join, Hash Join and Merge Sort Join are primary Join methods available to join tables. When the size of the result set is less than 10,000 the optimizer will perform Nested Loop join. But Nested loop Join steals must of the system resources. In Nested loop Join each record from the outer table will be compared with the inner table to find out a match. This paper proposes a Random Record Join method in which a random record will be picked from the inner table to find a match with the outer table. This method reduces the number of iteration required to Join the table. To perform the Random Record join the optimizer requires statistics of the table. The no. of records containing the distinct Join key attributes value should be maintained by the data dictionary.

Cited by 1

Gujarat Technological University, Gujarat

Raksha Chauhan, Pratik A Patel · 2015

This paper introduced a method for producing immediate and result in multi-join query, in homogeneous and heterogeneous environment. In recent years Adaptive or Non Blocking join algorithms have attracted a lot of attention in streaming applications, where data is provided from autonomous data sources in heterogeneous network environments. This algorithms are better as compared to traditional algorithms is that they can generates join results as early as the first input tuples are on hand hence it improves pipelining, smooth out join result production and also masking source or network delays. As response time of the queries places a very important role in adaptive join, the join algorithm like Hash Join, Sort Merge Join are become unacceptable for this environment because they require preprocessing before generating the join result. Hence, in adaptive join technique only possible algorithm is Nested loop join. In Nested Loop Join, every single record of the outer relation is compared with every single record of the inner relation. The no. of comparisons done by the nested loop join can be reduced by making improvement in Block Nested loop Join. In proposed End-Around Block Nested loop join outer and inner table’s comparison is done in parallel and whenever a row in first location didn’t find a match then row from first location removed and placed at rear end as like in a queue, the matched row removed from inner relation and added to result set. Whenever, New tuple arrive is then pushed into rear end and process is continuing with new incoming tuples in streaming environments.

Cited by 0

On the Complexity of Database Query Optimization

S. S. K. Evani, Sai Surya, Kiran Evani et al. · 2001

In this thesis, we consider the complexity of computing the optimal join order sequences for star queries and general queries. We consider the following join methods in our thesis - indexed nested loop joins, sort-merge joins, hash joins and block nested loop joins. The use of cartesian products is avoided and only linear trees are considered for query execution.

Cited by 0

Optimization of object query languages

Hendrika Janna Steenhagen · University of Twente Research Information · 1995

Transformation of nested SQL queries into join queries is advantageous because a nested SQL query can be looked upon as a nested-loop join, which is just one of the several join implementations that may be available in a relational DBMS.In join queries, dangling (unmatched) operand tuples are lost, which causes a problem in transforming nested queries having the aggregate function COUNT between query blocks-a problem that has become well-known as the COUNT bug.In the relational context, the outerjoin has been employed to solve the COUNT bug.In complex object models supporting an SQLlike query language, transformation of nested queries into join queries is an important optimization issue as well.The COUNT bug turns out to be a special case of a general problem being revealed in a complex object model.To solve the more general problem, we introduce the nestjoin operator, which is a generalization of the outerjoin for complex objects.

Cited by 27Open access

GORDER: An Efficient Method for KNN Join Processing

Chenyi Xia, Hongjun Lu, Beng Chin et al. · 2004

An important but very expensive primitive operation of high-dimensional databases is the K-Nearest Neighbor (KNN) similarity join. The operation combines each point of one dataset with its KNNs in the other dataset and it provides more meaningful query results than the range similarity join. Such an operation is useful for data mining and similarity search. In this paper, we propose a novel KNN-join algorithm, called the Gorder (or the G-ordering KNN) join method. Gorder is a block nested loop join method that exploits sorting, join scheduling and distance computation filtering and reduction to reduce both I/O and CPU costs. It sorts input datasets into the G-order and applied the scheduled block nested loop join on the G-ordered data. The distance computation reduction is employed to further reduce CPU cost. It is simple and yet efficient, and handles high-dimensional data efficiently. Extensive experiments on both synthetic cluster and real life datasets were conducted, and the results illustrate that Gorder is an efficient KNN-join method and outperforms existing methods by a wide margin. 1

Cited by 0

Indexing for Table Joins

Tapio Lahdenmäki, Michael Leach · 2005

An introduction to, and the terms used by, the most common join techniques. Simple table join examples to illustrate the processing that takes place in a nested loop join - the most common join method; a comparison of a two table join SELECT with a program implemented two table access. The terms involved in the join process together with their significance: local and join predicates; the table access order; inner and outer tables. A case study to consider the importance of the table access order on the index design process, using the QUBE to illustrate the relative performance of three different program implementations. Merge scan joins and hash joins; comparisons with nested loop joins; why these join techniques have become more widely used with current hardware. Adapting the Basic Question, BQ, to table joins to formulate the basic join question, BJQ. Index design considerations when joining more than two tables together, and when using subqueries and unions. Why joins often perform poorly. Table design issues that should be considered with respect to joins; downward and upward denormalization; the cost of denormalization; NLJ and MS/HJ compared to denormalization; unconscious table design.

Cited by 0

A Comparative Performance Evaluation of Join Strategies: Sensitivity to Modern Hardware Properties

NIMISHA A. MODI · Zenodo (CERN European Organization for Nuclear Research) · 2026

Join processing is one of the most performance-critical components of query execution in relational database systems. While classical join algorithms have been extensively studied, their evaluation has traditionally been guided by asymptotic complexity and disk-oriented cost models. Recent advances in processor, memory, and storage architectures—characterized by multi-core processors and deep cache hierarchies—have introduced new dimensions that reshape the performance behavior of join strategies. A comparative, hardware-aware analysis is presented to examine how nested loop, index nested loop, hash join, sort-merge join, and accelerator-based joins interact with underlying hardware properties. The study synthesizes insights from foundational and recent research to analyze join sensitivity to cache locality, memory bandwidth, NUMA effects, parallel scalability, accelerator utilization, and storage characteristics. A structured comparison and hardware-layer mapping illustrate why theoretically efficient join strategies may underperform on contemporary platforms. The analysis indicates that join performance is now dominated more by architectural constraints than by asymptotic algorithmic complexity, and that no single join strategy is universally optimal across heterogeneous hardware environments. The paper concludes by identifying key research challenges as hardware-aware cost modeling, adaptive join execution, and cross-layer optimization - that are critical for the design of efficient and scalable database systems.

Cited by 0Open access

Design and Implementation of Algorithms for Star Query Optimization

P. Chakrabarti, Pinaki Chakrabarti, To The et al. · 2000

In this thesis, we consider the problem of computing optimal join order sequence for star queries, a subclass of tree queries. A join is allowed to be computed using both nested-loop and sort-merge join methods and the use of Cartesian products is avoided. This thesis extends the scope of the problem studied by Ibaraki and Kameda [IK84] and by Krishnamurthy, Boral and Zaniolo [KBZ86].

Cited by 0

A Comparative Performance Evaluation of Join Strategies: Sensitivity to Modern Hardware Properties

NIMISHA A. MODI · Zenodo (CERN European Organization for Nuclear Research) · 2026

Cited by 0Open access

A Cost Model for a Fingered Join Operator in Relational Query Plans

Vishnu Prathish · UWSpace (University of Waterloo) · 2015

We introduce the finger aware cursor operator for relational join queries. It scans a list of\ntuples in a finger enabled manner when a nested loop join operation is performed. Using\nthis scan operation, we improve the performance of nested loop join when compared to\nwhen compared to conventional scan. To quantify the improvement in performance using\nfingered scan, a statistic named runs that quantifies the degree of randomness in a list\nof records is introduced. This statistic is vital in assessing the performance improvement\nachieved using fingered scan. Using runs statistic as a key ingredient, we develop a cost\nmodel that can assign a cost value to the join operation based on underlying fingered\nscan. We then develop a cost formula and evaluate the cost model against a simulated\ndata set. We show that conventional System R cost model is not sufficient to capture the\nperformance improvement. We then evaluate using the new cost formula and show that it\npredicts the cost of join operation correctly.

Cited by 0Open access

Design and Implementation of Algorithms for Tree Query Optimization

Hegde Hrishikesh P., To The, Dr. Sumit Ganguly · 2000

In this thesis, we consider the problem of finding an optimal join order sequence for tree and star queries. A join between two relations is either performed using the nested loop method or the sort-merge method. The problem considered in our thesis is an extension of the work studied by Ibaraki and Kameda[IK84] and Ravi Krishnamurthy, Boral and Zaniolo[KBZ86] The algorithm for obtaining an optimal join order sequence for star query has exponential time complexity[Cha00]. In this thesis we present a greedy heuristic, GHGS, that outputs join sequence that is very close to the optimal solution. These results are based on the experimental analysis performed on GHGS with 10000 different Catalogs. The greedy algorithm, GHGS has O(m3) time complexity and O(m) space complexity where the number of relations considered are m. We have also extended the idea proposed in [IK84] and [KBZ86] to consider both, nested loop and sort merge joins to obtain an optimal join order sequence for tree queries. The time complexity of our algorithm ExIba is O(m2 log m 2m) and the space complexity is O(m). The time complexity of the ExKBZ algorithm is O(m2 2m) and the space complexity of the algorithm is O(m) where m is the number of relations in the tree query.

Cited by 0

Clustering-Variable-Width Histogram Based Window Semi-hash Multi-join over Streams

Zhenjie Zhang, Wanchang Jiang, Yadong Zhang et al. · 2007 International Conference on Convergence Information Technology (ICCIT 2007) · 2007

Join operator has become more and more important in the context of data stream. Most join algorithms over streams to date are based on nested loop joins or hash joins. However, these time expensive algorithms can not suit for CPU-limited case. In this paper, a clustering-based variable-width histogram is designed for obtaining value distribution of tuples in the sliding windows, and some important characteristics of tuples can be retained. Semi-hash tables can be constructed by using the histogram. Our sliding window semi-hash multi-join algorithm can minimize the processing time cost of join and produce an accurate join result much earlier. Experimental results show that our approach is more efficient than other approaches.

Cited by 0

On Finding Approximate Solutions to the Query Optimization Problem

To The, Sourav Chatterji, Sourav Chatterji et al. · 2001

In this project, we consider the problem of computing the optimal join order sequences for general queries. We considered nested loops join model. The use of cartesian products is avoided and only linear trees are considered for query execution. We show that the problem of computing the optimal join order sequence for genreral queries is NP-Complete when the join methods are restricted to nested loop joins only. We then show that the problem of finding a join order sequence whose cost is within a factor of 2 ( logK) of K, where K is the optimal cost, is NP-Hard. The result is valid when the number of edges in the query graph is restricted to a given function of the number of its vertices, except for very sparse or very dense graphs. The join method is restricted to nested loops joins only, but, the result remains true when other join methods are also considered. To the best of our knowledge, this is the first inapproximabilty result in Query Optimization We also prove some results which are not directly relevant to the problem in hand and are only of theoretical interest to the problem at present. However they also provide some insight to the complexity of the problem. We prove that nding an approximate solution to the lexicographic ordering is NP complete. We also provide a procedure to transform a graph to another graph with all vertices of some degree without changing the max clique size. Lastly, we suggest some heuristics for finding the join order.

Cited by 0

Tree structured multiple processor join methods

Roger K. Shultz, Ila Miller · 1987

This paper summarizes the execution cost of join operations performed by parallel executing processors. Four different parallel join algorithms are proposed for execution on multiple processing nodes interconnected on a tree structured communication network. Secondary storage is accessed in parallel by leaf nodes. An average execution cost analysis is presented for the multiple processor join methods. For a reasonable ratio of result to operand cardinality, joins which use hashing for semi-join of operands at the leaf node secondary storage interface are predicted to perform better than nested-loop and sort-merge joins. Both node memory capacity and join result cardinality have a large influence on the relative performance of the join methods. This analysis method forms a basis for selecting among alternative processing methods for statically linked, multiple processor computer architectures.

Cited by 8

Query processing algorithms for temporal intersection joins

Himawan Gunadhi, A. Segev · 2002

Intersection join processing in temporal relational databases is investigated. An analysis is presented of the characteristics and processing requirements of three types of intersection join operators: the time-join temporal equijoin on the surrogate, and temporal equijoin on the temporal attribute. Based on the physical organization of the database and on the semantics of the operators, several algorithms were developed to process these joins efficiently. The primary cost variables were identified for each algorithm and their performance is compared to that of a conventional nested-loop join procedure. It is shown that the algorithms developed can reduce processing costs significantly.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Cited by 67

An adaptive hash join algorithm on a network of workstations

Kenji Imasaki, Sivarama P. Dandamudi · 2002

Due to advances in computer technology, many organizations have a large number of workstation-class machines connected by LAN. Such a network of workstations (NOW) can be used for parallel processing, including database query processing. This paper proposes a new load sharing algorithm for hash join processing on NOWs. This new algorithm combines a chunking method with hash join to manage dynamic changes that occur in NOW environments. The algorithm is compared with two other algorithms: an adaptive nested-loop join and adaptive GRACE hash join. These three algorithms were evaluated on a Pentium-based heterogeneous NOW system with skewed data and various non-query background loads. The results show that the new algorithm is the best among the three in most of the cases and should be used for single join processing on NOWs.

Cited by 15