Incrementally maintaining run-length encoded attributes in column stores

Abhijeet Mohapatra, M. Genesereth
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引用次数: 6

Abstract

Run-length encoding is a popular compression scheme which is used extensively to compress the attribute values in column stores. Out of order insertion of tuples potentially degrades the compression achieved using run-length encoding and consequently, the performance of reads. The in-place insertions, deletions and updates of tuples into a column store relation with n tuples take O(n) time. The linear cost is typically avoided by amortizing the cost of updates in batches. However, the relation is decompressed and subsequently re-compressed after applying a batch of updates. This leads to added time time complexity. We propose a novel indexing scheme called count indexes that supports O(log n) in-place insertions, deletions, updates and look ups on a run-length encoded sequence with n runs. We also show that count indexes efficiently update a batch of tuples requiring almost a constant time per updated tuple. Additionally, we show that count indexes are optimal. We extend count indexes to support O(log n) updates on bitmapped sequences with n values and adapt them to block-based stores.
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在列存储中增量地维护运行长度的编码属性
运行长度编码是一种流行的压缩方案,广泛用于压缩列存储中的属性值。乱序插入元组可能会降低使用运行长度编码实现的压缩,从而降低读取的性能。将元组插入、删除和更新到包含n个元组的列存储关系中需要O(n)时间。线性成本通常通过分摊批量更新的成本来避免。但是,关系会被解压缩,然后在应用一批更新后重新压缩。这导致了时间复杂度的增加。我们提出了一种新的索引方案,称为计数索引,它支持O(log n)个原地插入,删除,更新和查找在n次运行的运行长度编码序列上。我们还展示了计数索引有效地更新一批元组,每个更新元组所需的时间几乎是恒定的。此外,我们还证明计数索引是最优的。我们扩展计数索引,以支持0 (log n)次更新的位图序列与n个值,并使其适应基于块的存储。
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