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

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.

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

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.

Bedtk: finding interval overlap with implicit interval tree

Summary We present bedtk, a new toolkit for manipulating genomic intervals in the BED format. It supports sorting, merging, intersection, subtraction and the calculation of the breadth of coverage. Bedtk uses implicit interval tree, a data structure for fast interval overlap queries. It is several to tens of times faster than existing tools and tends to use less memory. Availability and implementation The source code is available at https://github.com/lh3/bedtk.

Bedtk: Finding Interval Overlap with Implicit Interval Tree

SummaryWe present bedtk, a new toolkit for manipulating genomic intervals in the BED format. It supports sorting, merging, intersection, subtraction and the calculation of the breadth of coverage. Bedtk employs implicit interval tree, a new data structure for fast interval overlap queries. It is several to tens of times faster than existing tools and tends to use less memory.Availabilityhttps://github.com/lh3/[email protected], [email protected]

Interval Tree and Its Application in Integer Factorization

The paper first puts forward a way to study odd integers by placing the odd integers in a given interval on a perfect full binary tree, then makes an investigation on the odd integers by means of combining the original properties of the integers with the properties of the binary trees and obtains several new results on how an odd integer's divisors distribute on a level of a binary tree. The newly discovered law of divisors' distribution that includes common divisors between two symmetric nodes, genetic divisors between an ancestor node and its descendant node can provide a new and simple approach to factorize odd composite integers. Based on the mathematical deductions, numerical experiments are designed and demonstrated in the Maple software. All the results of the experiments are conformance to expectation and validate the validity of the approach.