Concurrency control in B-trees with batch updates

Recent studies have shown that cacheconscious indexes outperform conventional main memory indexes. Cache-conscious indexes focus on better utilization of each cache line for improving search performance of a single lookup. None has exploited cache spatial and temporal locality between consecutive lookups. We show that conventional indexes, even "cache-conscious" ones, suffer from significant cache thrashing between accesses. Such thrashing can impact the performance of applications such as stream processing and query operations such as indexnested-loops join.We propose techniques to buffer accesses to memory-resident tree-structured indexes to avoid cache thrashing. We study several alternative designs of the buffering technique, including whether to use fixed-size or variablesized buffers, whether to buffer at each tree level or only at some of the levels, how to support bulk access while there are concurrent updates happening to the index, and how to preserve the order of the incoming lookups in the output results. Our methods improve cache performance for both cache-conscious and conventional index structures. Our experiments show that buffering techniques enable a probe throughput that is two to three times higher than traditional methods.

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“…In situations with large numbers of updates per second, bulk update operations [8,14] may be appropriate.…”

Section: Related Workmentioning

confidence: 99%

Buffering Accesses to Memory-Resident Index Structures

Zhou

Ross

2003

Proceedings 2003 VLDB Conference

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“…This is crucial for achieving the I/O bound stated in Section 1.1 because processing each query immediately requires at least one I/O per query, even if it produces very little output. The structures discussed in the previous paragraph and even structures designed to efficiently support batched updates [18] answer queries immediately and, thus, cannot achieve an optimal I/O complexity for processing a sequence of operations.…”

Section: Previous Workmentioning

confidence: 97%

A parallel buffer tree

Sitchinava

Zeh

2012

Proceedings of the Twenty-Fourth Annual ACM Symposium on Parallelism in Algorithms and Architectures

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We present the parallel buffer tree, a parallel external memory (PEM) data structure for batched search problems. This data structure is a non-trivial extension of Arge's sequential buffer tree to a private-cache multiprocessor environment and reduces the number of I/O operations by the number of available processor cores compared to its sequential counterpart, thereby taking full advantage of multicore parallelism.The parallel buffer tree is a search tree data structure that supports the batched parallel processing of a sequence of N insertions, deletions, membership queries, and range queries in the optimal O(sortP (N ) + K/P B) parallel I/O complexity, where K is the size of the output reported in the process and sortP (N ) is the parallel I/O complexity of sorting N elements using P processors.

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“…The buffered keys are kept sorted. Once the buffer is full, a batch insert of the sorted buffered keys is performed [17]. A review of these buffering techniques can be found in [8].…”

Section: Related Workmentioning

confidence: 99%

Online update of b-trees

Barsky

Thomo

Tóth

et al. 2010

Proceedings of the 19th ACM International Conference on Information and Knowledge Management

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Many scenarios impose a heavy update load on B-tree indexes in modern databases. A typical case is when B-trees are used for indexing all the keywords of a text field. For example upon the insertion of a new text record (e.g. a new document arrives), a barrage of new keywords has to be inserted into the index causing many random disk I/Os and interrupting the normal operation of the database. The common approach has been to collect the updates in a separate structure and then perform a batch update of the index. This update "freezes" the database. Many applications, however, require the immediate availability of the new updates without any interruption of the normal database operation. In this paper we present a novel online B-tree update method based on a new buffering data structure we introduce -Dynamic Bucket Tree (DBT). The DBT-buffer serves as a differential index for new updates. The grouping of keys in DBT-buffer is based on the longest common prefixes (LCP ) of their binary representations. The LCP is used as a measure of the locality of keys to be transferred to the main B-tree. Our online update system does not slow down concurrent user transactions or lead to degradation of search performance. Experiments confirm that our DBT buffer can be efficiently used for online updates of text fields. As such it represents an effective solution to the notorious problem of handling updates to an Inverted Index.

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Concurrency control in B-trees with batch updates

Cited by 45 publications

References 19 publications

Buffering Accesses to Memory-Resident Index Structures

Buffering Accesses to Memory-Resident Index Structures

A parallel buffer tree

Online update of b-trees

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