Lock-Free Resizeable Concurrent Tries

Growing main memory capacities and an increasing number of hardware threads in modern server systems led to fundamental changes in database architectures. Most importantly, query processing is nowadays performed on data that is often completely stored in main memory. Despite of a high main memory scan performance, index structures are still important components, but they have to be designed from scratch to cope with the specific characteristics of main memory and to exploit the high degree of parallelism. Current research mainly focused on adapting block-optimized B+-Trees, but these data structures were designed for secondary memory and involve comprehensive structural maintenance for updates.In this paper, we present the KISS-Tree, a latch-free inmemory index that is optimized for a minimum number of memory accesses and a high number of concurrent updates. More specifically, we aim for the same performance as modern hash-based algorithms but keeping the order-preserving nature of trees. We achieve this by using a prefix tree that incorporates virtual memory management functionality and compression schemes. In our experiments, we evaluate the KISS-Tree on different workloads and hardware platforms and compare the results to existing in-memory indexes. The KISS-Tree offers the highest reported read performance on current architectures, a balanced read/write performance, and has a low memory footprint.

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“…turbo frequency, 4 cores, Hyper-Threading, 2 memory channels, 8MB LLC) equipped with 16GB RAM (DDR3-1333) running Ubuntu Linux 11. 10.…”

Section: Discussionmentioning

confidence: 99%

“…The CTrie [10] removes this overhead by compressing each node. This is achieved by adding a 32bit bitmap (limits the k to 5) to the beginning of each node, which indicates the occupied buckets in that node.…”

Section: Related Workmentioning

confidence: 99%

KISS-Tree

Kissinger

Schlegel

Habich

et al. 2012

Proceedings of the Eighth International Workshop on Data Management on New Hardware

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“…The hash trie data structure itself is structurally similar to hash array mapped tries [49] and the data structure used in extendible hashing schemes [20]. However, while these approaches allow for optimized point lookups of individual keys, our hash trie data structure supports optimized rangelookups of key prefixes as they are required by a hash-based multi-way join algorithm.…”

Section: Related Workmentioning

confidence: 99%

Adopting worst-case optimal joins in relational database systems

et al. 2020

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Worst-case optimal join algorithms are attractive from a theoretical point of view, as they offer asymptotically better runtime than binary joins on certain types of queries. In particular, they avoid enumerating large intermediate results by processing multiple input relations in a single multiway join. However, existing implementations incur a sizable overhead in practice, primarily since they rely on suitable ordered index structures on their input. Systems that support worst-case optimal joins often focus on a specific problem domain, such as read-only graph analytic queries, where extensive precomputation allows them to mask these costs.In this paper, we present a comprehensive implementation approach for worst-case optimal joins that is practical within general-purpose relational database management systems supporting both hybrid transactional and analytical workloads. The key component of our approach is a novel hash-based worst-case optimal join algorithm that relies only on data structures that can be built efficiently during query execution. Furthermore, we implement a hybrid query optimizer that intelligently and transparently combines both binary and multi-way joins within the same query plan. We demonstrate that our approach far outperforms existing systems when worst-case optimal joins are beneficial while sacrificing no performance when they are not.

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mentioning

confidence: 99%

“…In this technical report, we extend the standard concurrent hash-tries [3] with an auxiliary data structure called a cache 1 . The cache is essentially an array that stores pointers to a specific level of the hash trie.…”

mentioning

confidence: 99%

Analysis of Concurrent Lock-Free Hash Tries with Constant-Time Operations

Prokopec¹

2017

Preprint

Self Cite

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Ctrie is a scalable concurrent non-blocking dictionary data structure, with good cache locality, and non-blocking linearizable iterators [4]. However, operations on most existing concurrent hash tries run in O(log n) time.In this technical report, we extend the standard concurrent hash-tries [3] with an auxiliary data structure called a cache 1 . The cache is essentially an array that stores pointers to a specific level of the hash trie. We analyze the performance implications of adding a cache, and prove that the running time of the basic operations becomes O(1).Keywords concurrent data structures, lock-free hash tries, constant-time hash tries, expected running time analysis

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Lock-Free Resizeable Concurrent Tries

Cited by 11 publications

References 16 publications

KISS-Tree

KISS-Tree

Adopting worst-case optimal joins in relational database systems

Analysis of Concurrent Lock-Free Hash Tries with Constant-Time Operations

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