Alphasort: A cache-sensitive parallel external sort

Nyberg, Chris; Barclay, Thomas; Cvetanovic, Zarka; Gray, Jim; Lomet, Dave

doi:10.1007/bf01354877

Cited by 49 publications

(27 citation statements)

References 12 publications

(14 reference statements)

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“…AlphaSort [29] explores several ideas to minimize cache misses by storing partial keys. Masstree uses a trie [20] like data structure to achieve the same goal.…”

Section: Related Workmentioning

confidence: 99%

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Cache craftiness for fast multicore key-value storage

Mao

Kohler

Morris

2012

Proceedings of the 7th ACM European Conference on Computer Systems

350

179

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We present Masstree, a fast key-value database designed for SMP machines. Masstree keeps all data in memory. Its main data structure is a trie-like concatenation of B + -trees, each of which handles a fixed-length slice of a variable-length key. This structure effectively handles arbitrary-length possiblybinary keys, including keys with long shared prefixes. B + -tree fanout was chosen to minimize total DRAM delay when descending the tree and prefetching each tree node. Lookups use optimistic concurrency control, a read-copy-update-like technique, and do not write shared data structures; updates lock only affected nodes. Logging and checkpointing provide consistency and durability. Though some of these ideas appear elsewhere, Masstree is the first to combine them. We discuss design variants and their consequences.On a 16-core machine, with logging enabled and queries arriving over a network, Masstree executes more than six million simple queries per second. This performance is comparable to that of memcached, a non-persistent hash table server, and higher (often much higher) than that of VoltDB, MongoDB, and Redis.

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“…AlphaSort [29] explores several ideas to minimize cache misses by storing partial keys. Masstree uses a trie [20] like data structure to achieve the same goal.…”

Section: Related Workmentioning

confidence: 99%

“…Masstree uses a combination of old and new techniques to achieve high performance [8,11,13,20,[27][28][29]. It achieves fast concurrent operation using a scheme inspired by OLFIT [11], Bronson et al [9], and read-copy update [28].…”

Section: Introductionmentioning

confidence: 99%

Cache craftiness for fast multicore key-value storage

Mao

Kohler

Morris

2012

Proceedings of the 7th ACM European Conference on Computer Systems

350

179

View full text Add to dashboard Cite

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“…As the data is on the disk, any efficient external sorting algorithm can be used for this purpose [69] [78]. A snap shot of the database after sorting is shown in Figure 3.1(B).…”

Section: A Detailed Intuition Of Our Algorithmmentioning

confidence: 99%

Exact Primitives for Time Series Data Mining

Mueen

2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…We compute the distance of each data point from r and sort all such distances in ascending order. As the data is on the disk, any efficient external sorting algorithm can be used for this purpose (Motzkin and Hansen 1982;Nyberg et al 1995). A snap shot of the database after sorting is shown in Fig.…”

Section: A Detailed Intuition Of Our Algorithmmentioning

confidence: 99%

A disk-aware algorithm for time series motif discovery

Mueen

Keogh

Zhu

et al. 2010

Data Min Knowl Disc

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Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding time series motifs exactly in massive databases is an open problem. Previous efforts either found approximate motifs or considered relatively small datasets residing in main memory. In this work, we leverage off previous work on pivot-based indexing to introduce a disk-aware algorithm to find time series motifs exactly in multi-gigabyte databases which contain on the order of tens of millions of time series. We have evaluated our algorithm on datasets from diverse areas including medicine, anthropology, computer networking and image processing and show that we can find interesting and meaningful motifs in datasets that are many orders of magnitude larger than anything considered before.

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Alphasort: A cache-sensitive parallel external sort

Cited by 49 publications

References 12 publications

Cache craftiness for fast multicore key-value storage

Cache craftiness for fast multicore key-value storage

Exact Primitives for Time Series Data Mining

A disk-aware algorithm for time series motif discovery

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