Session details: Data structures and range queries

2014 ◽  
Author(s):  
Martin Grohe
Keyword(s):  
Data Structures ◽  
Range Queries

scholarly journals I/O-efficient spatial data structures for range queries

2012 ◽  
Vol 4 (2) ◽  
pp. 2-7 ◽  
Author(s):  
Lars Arge ◽  
Kasper Green Larsen
Keyword(s):  
Data Structures ◽  
Spatial Data ◽  
Range Queries ◽  
Spatial Data Structures

Lock-free Contention Adapting Search Trees

2021 ◽  
Vol 8 (2) ◽  
pp. 1-38
Author(s):  
Kjell Winblad ◽  
Konstantinos Sagonas ◽  
Bengt Jonsson
Keyword(s):  
Data Structures ◽  
Range Query ◽  
Search Tree ◽  
Range Queries ◽  
Local Adaptations ◽  
Number Of Factors ◽  
Free Data ◽  
Wide Range ◽  
And Performance ◽  
Better Than

Concurrent key-value stores with range query support are crucial for the scalability and performance of many applications. Existing lock-free data structures of this kind use a fixed synchronization granularity. Using a fixed synchronization granularity in a concurrent key-value store with range query support is problematic as the best performing synchronization granularity depends on a number of factors that are difficult to predict, such as the level of contention and the number of items that are accessed by range queries. We present the first linearizable lock-free key-value store with range query support that dynamically adapts its synchronization granularity. This data structure is called the lock-free contention adapting search tree (LFCA tree). An LFCA tree automatically performs local adaptations of its synchronization granularity based on heuristics that take contention and the performance of range queries into account. We show that the operations of LFCA trees are linearizable, that the lookup operation is wait-free, and that the remaining operations (insert, remove and range query) are lock-free. Our experimental evaluation shows that LFCA trees achieve more than twice the throughput of related lock-free data structures in many scenarios. Furthermore, LFCA trees are able to perform substantially better than data structures with a fixed synchronization granularity over a wide range of scenarios due to their ability to adapt to the scenario at hand.

scholarly journals Query co-planning for shared execution in key-value stores

2021 ◽  
Vol 12 (5) ◽  
Author(s):  
Josué Ttito ◽  
Renato Marroquín ◽  
Sergio Lifschitz ◽  
Lewis McGibbney ◽  
José Talavera
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Data Model ◽  
Range Queries ◽  
Model Data ◽  
Data Movement ◽  
Segment Tree ◽  
Interval Tree ◽  
Simple Interface ◽  
Arbitrary Objects

Key-value stores propose a straightforward yet powerful data model. Data is modeled using key-value pairs where values can be arbitrary objects and written/read using the key associated with it. In addition to their simple interface, such data stores also provide read operations such as full and range scans. However, due to the simplicity of its interface, trying to optimize data accesses becomes challenging. This work aims to enable the shared execution of concurrent range and point queries on key-value stores. Thus, reducing the overall data movement when executing a complete workload. To accomplish this, we analyze different possible data structures and propose our variation of a segment tree, Updatable Interval Tree. Our data structure helps us co-planning and co-executing multiple range queries together and reduces redundant work. This results in executing workloads more efficiently and overall increased throughput, as we show in our evaluation.

Opportunistic data structures for range queries

2006 ◽  
Vol 11 (2) ◽  
pp. 145-154 ◽  
Author(s):  
Chung Keung Poon ◽  
Wai Keung Yiu
Keyword(s):  
Data Structures ◽  
Range Queries

scholarly journals Query co-planning for shared execution in Key-Value Stores

2020 ◽  
Author(s):  
Josue Joel Ttito ◽  
Renato Marroquin ◽  
Sergio Lifschitz
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Data Model ◽  
Range Queries ◽  
Model Data ◽  
Data Movement ◽  
Segment Tree ◽  
Interval Tree ◽  
Simple Interface ◽  
Arbitrary Objects

Key-value stores propose a very simple yet powerful data model. Data is modeled using key-value pairs where values can be arbitrary objects and can be written/read using the key associated with it. In addition to their simple interface, such data stores also provide read operations such as full and range scans. However, due to the simplicity of its interface, trying to optimize data accesses becomes challenging. This work aims to enable the shared execution of concurrent range and point queries on key-value stores. Thus, reducing the overall data movement when executing a complete workload. To accomplish this, we analyze different possible data structures and propose our variation of a segment tree, Updatable Interval Tree. This data structure helps us co-planning and co-executing multiple range queries together, as we show in our evaluation.

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