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We study the dynamic membership (or dynamic dictionary) problem, which is one of the most fundamental problems in data structures. We study the problem in the external memory model with cell size b bits and cache size m bits. We prove that if the amortized cost of updates is at most 0.999 (or any other constant < 1), then the query cost must be Ω(logb log n (n/m)), where n is the number of elements in the dictionary. In contrast, when the update time is allowed to be 1 + o(1), then a bit vector or hash table give query time O(1). Thus, this is a threshold phenomenon for data structures. This lower bound answers a folklore conjecture of the external memory community. Since almost any data structure task can solve membership, our lower bound implies a dichotomy between two alternatives: (i) make the amortized update time at least 1 (so the data structure does not buffer, and we lose one of the main potential advantages of the cache), or (ii) make the query time at least roughly logarithmic in n. Our result holds even when the updates and queries are chosen uniformly at random and there are no deletions; it holds for randomized data structures, holds when the universe size is O(n), and does not make any restrictive assumptions such as indivisibility. All of the lower bounds we prove hold regardless of the space consumption of the data structure, while the upper bounds only need linear space. The lower bound has some striking implications for external memory data structures. It shows that the query complexities of many problems such as 1D-range counting, predecessor, rank-select, and many others, are all the sam

Year: 2010

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