Elshad Karimov · Apress eBooks · 2020
A hash table is a data structure of “associative arrays” that groups values in an index and sorts and retrieves data using a key/value mapping. It accomplishes the same goal in some cases like Swift dictionaries. However, the efficiency of hash tables is better than dictionaries. The time complexity for searching, inserting, and deleting is on average O(1), which means that regardless of the size of the input operation time remains constant. This solves the problem of the linear search operation which is costly and the time complexity is O(n). By calculating the hash value, we can find the index and place the value in this index and retrieve the value based on it.
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Hao Zhang, Yonggang Wen, Haiyong Xie et al. · SpringerBriefs in computer science · 2013
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Thomas G. Szymanski · Journal of Algorithms · 1985
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SpringerReference · 2011
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2013
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Jie Jiang, Yibo Yan, Mengyu Zhang et al. · 2019
Hash tables have been broadly used in many security applications. These applications require fast query speed and high memory efficiency. However, the query speed degrades when hash collisions happen. The design goal of this paper is to achieve high load factor as well as fast query at the same time. In this paper, we propose a novel hashing scheme, namely the Shifting Hash Table (SHT), which consists of two parts. The first part is an enhanced version of the Bloom filter checking which subtable(s) may contain the incoming items, and the second part contains a cuckoo hashing based hash table which stores the key-value pairs. The key technique of this paper is that we divide items into two categories: at-home and abroad. We only insert the second kind of items (abroad) into the Bloom filter, thus the memory usage of the filter is significantly reduced. We conducted extensive experiments and the results show that SHT significantly outperforms the state-of-the-art. Specifically, SHT can query an item using on average less than 1.05 bucket probes and even using 1 bit per entry in the fast memory, and achieve a high load factor which is 95% at the same time.
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Zemin Ning, Anthony J. Cox, James C. Mullikin et al. · 2006
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2013
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Dabek, Frank (Frank Edward), 1977- · DSpace@MIT (Massachusetts Institute of Technology) · 2005
DHash is a new system that harnesses the storage and network resources of computers distributed across the Internet by providing a wide-area storage service, DHash. DHash frees applications from re-implementing mechanisms common to any system that stores data on a collection of machines: it maintains a mapping of objects to servers, replicates data for durability, and balances load across participating servers. Applications access data stored in DHash through a familiar hash-table interface: put stores data in the system under a key; get retrieves the data. DHash has proven useful to a number of application builders and has been used to build a content-distribution system [31], a Usenet replacement [115], and new Internet naming architectures [130, 129]. These applications demand low-latency, high-throughput access
Cited by 0Open access
Andrew Brampton, Andrew MacQuire, Azlina Idris et al. · Open Repository and Bibliography (University of Liège) · 2006
Most Distributed Hash Tables (DHTs) simply consider interconnecting homogeneous nodes on the same overlay. However, realistically nodes on a network are heterogeneous in terms of their capabilities. Because of this, traditional DHTs have been shown to exhibit poor performance in a real-world environment. Additionally, we believe that it is this approach that contributes to a limited exploitation of peer-to-peer technologies. Previous work on super-peers in DHTs was proposed to address these performance issues, however the strategy used is often based on locally clustering peers around individual super-peers. This method of super-peering, however, compromises fundamental features such as load-balancing, resilience and routing efficiency, which traditional DHTs originally promised to offer.
Cited by 15Open access
M. Frans Kaashoek, Robert Morris, Frank Dabek · 2006
DHash is a new system that harnesses the storage and network resources of computers distributed across the Internet by providing a wide-area storage service, DHash. DHash frees applications from re-implementing mechanisms common to any system that stores data on a collection of machines: it maintains a mapping of objects to servers, replicates data for durability, and balances load across participating servers. Applications access data stored in DHash through a familiar hash-table interface: put stores data in the system under a key; get retrieves the data.
DHash has proven useful to a number of application builders and has been used to build a content-distribution system [31], a Usenet replacement [115], and new Internet naming architectures [130, 129]. These applications demand low-latency, high-throughput access to durable data. Meeting this demand is challenging in the wide-area environment. The geographic distribution of nodes means that latencies between nodes are likely to be high: to provide a low-latency get operation the system must locate a nearby copy of the data without traversing high-latency links. Also, wide-area network links are likely to be less reliable and have lower capacities than local-area network links: to provide durability efficiently the system must minimize the number of copies of data items it sends over these limited capacity links in response to node failure.
This thesis describes the design and implementation of the DHash distributed hash table and presents algorithms and techniques that address these challenges. DHash provides low-latency operations by using a synthetic network coordinate system (Vivaldi) to find nearby copies of data without sending messages over high-latency links. A network transport (STP), designed for applications that contact a large number of nodes, lets DHash provide high throughput by striping a download across many servers without causing high packet loss or exhausting local resources. Sostenuto, a data maintenance algorithm, lets DHash maintain data durability while minimizing the number of copies of data that the system sends over limited-capacity links. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Cited by 45
Md. Sabbir Hossain Polak, David Troendle, Byunghyun Jang · ArXiv.org · 2025
Hash tables are essential building blocks in data-intensive applications, yet existing GPU implementations often struggle with concurrent updates, high load factors, and irregular memory access patterns. We present Hive hash table, a high-performance, warp-cooperative and dynamically resizable GPU hash table that adapts to varying workloads without global rehashing. Hive hash table makes three key contributions. First, a cache-aligned packed bucket layout stores key-value pairs as 64-bit words, enabling coalesced memory access and atomic updates via single-CAS operations. Second, warp-synchronous concurrency protocols - Warp-Aggregated-Bitmask-Claim (WABC) and Warp-Cooperative Match-and-Elect (WCME) - reduce contention to one atomic operation per warp while ensuring lock-free progress. Third, a load-factor-aware dynamic resizing strategy expands or contracts capacity in warp-parallel K-bucket batches using linear hashing, maintaining balanced occupancy. To handle insertions under heavy contention, Hive hash table employs a four-step strategy: replace, claim-and-commit, bounded cuckoo eviction, and overflow-stash fallback. This design provides lock-free fast paths and bounded recovery cost under contention determined by a fixed eviction depth, while eliminating ABA hazards during concurrent updates. Experimental evaluation on an NVIDIA RTX 4090 shows Hive hash table sustains load factors up to 95% while delivering 1.5-2x higher throughput than state-of-the-art GPU hash tables (Slab-Hash, DyCuckoo, WarpCore) under mixed insert-delete-lookup workloads. On balanced workload, Hive hash table reaches 3.5 billion updates/s and nearly 4 billion lookups/s, demonstrating scalability and efficiency for GPU-accelerated data processing.
Cited by 0Open access
Voorberg, Marten · Figshare · 2021
Experiment Time of Dynamic Hash Table Bitmap Compared to Bitmap and Hash Table
Cited by 0Open access
Shengze Wang, Yi Liu, Xinshu Zhang et al. · 2024
Distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as storage systems, decentralized databases, content distribution networks, and blockchains. To address this limitation, we present LEAD, a novel system incorporating learned models within DHT structures to significantly optimize range query performance. LEAD utilizes a recursive machine learning model to map and retrieve data across a distributed system while preserving the inherent order of data. Preliminary results indicate LEAD achieves tremendous advantages in system efficiency compared to existing range query methods in large-scale distributed systems while maintaining high scalability and resilience to network churn.
Cited by 0
Voorberg, Marten · Figshare · 2021
Experiment Time of Dynamic Hash Table Bitmap Compared to Bitmap and Hash Table
Cited by 0Open access
Kurt Tutschku, Yimei Liao, Unknown et al. · State and University Library Dresden (Saxon State Library) · 2006
Cited by 0Open access
Kris Popendorf (241029), Hachiya Tsuyoshi (241032), Yasunori Osana (241035) et al. · Figshare · 2015
<p>Here we show a simplified example of how matching seed sets are extracted from the hash table and converted into anchors. <b>A</b> shows one row ( out of ) of the hash table. Several pairs have been inserted into the hash table. indicates a position in the input sequences at which occurs. Because is necessarily implied by Murasaki only stores , however here both and are shown for clarity. Different values of have also been colored differently to note their difference. First, this row is sorted with the result shown in <b>B</b>. The extents of each matching seed set can then be found in time by binary search. These matching seed sets are extracted into a series of lists, as shown in <b>C</b>, which are then used to construct anchors, as shown in <b>D</b>.</p>
Cited by 0Open access
ZHENG Yan1, CHEN Hong-wei 1, WANG Ru-chuan1 et al. · SHILAP Revista de lepidopterología · 2008
A novel distributed Hash table（DHT） BChord was presented. BChord adopts bi-directional search mechanism of Chord. Though query items of finger table in BChord almost double to Chord, compared with Chord, BChord evi- dently increases speed at searching of resource and spreading of messages via theoretic proof and simulation results.
Cited by 0Open access
Shengze Wang, Yi Liu, Xinshu Zhang et al. · 2025
Distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as LLM serving, distributed storage, databases, content delivery networks, and blockchains. To address this limitation, we present LEAD, a novel system incorporating learned models within DHT structures to significantly optimize range query performance. LEAD utilizes a recursive machine learning model to map and retrieve data across a distributed system while preserving the inherent order of data. LEAD includes the designs to minimize range query latency and message cost while maintaining high scalability and resilience to network churn. Our comprehensive evaluations, conducted in both testbed implementation and simulations, demonstrate that LEAD achieves tremendous advantages in system efficiency compared to existing range query methods in large-scale distributed systems, reducing query latency and message cost by 80% to 90%+. Furthermore, LEAD exhibits remarkable scalability and robustness against system churn, providing a robust, scalable solution for efficient data retrieval in distributed key-value systems.
Cited by 2
T. Wang, Yi Liu, Xiaoxue Zhang et al. · IEEE Transactions on Networking · 2026
Distributed Hash Tables (DHTs) are pivotal in numerous high-impact key-value applications built on distributed networked systems, offering a decentralized architecture that avoids single points of failure and improves data availability. Despite their widespread utility, DHTs face substantial challenges in handling range queries, which are crucial for applications such as LLM serving, distributed storage, databases, content delivery networks, and blockchains. To address this limitation, we present LEAD, a novel system incorporating learned models within DHT structures to significantly optimize range query performance. LEAD utilizes recursive machine learning models as the Learned Hash Function to map and retrieve data across a distributed system while preserving the inherent order of data. LEAD includes the designs to minimize range query latency and message cost while maintaining high scalability and resilience to network churn. Our comprehensive evaluations, conducted in both testbed implementation and simulations, demonstrate that LEAD achieves tremendous advantages in system efficiency compared to existing range query methods in large-scale distributed systems, reducing query latency and message cost by 80% to 90%+. Furthermore, LEAD exhibits scalability and robustness against system churn, providing a robust, scalable structure for efficient data retrieval in distributed key-value systems.
Cited by 0