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Memory efficient perfect hashing for large records

机译:用于大型记录的记忆有效完美散列

摘要

Embodiments for a memory efficient perfect hashing for large records. A container ID set is divided into multiple fixed range sizes. These ranges are then mapped into perfect hash buckets until each bucket is filled to uniformly distribute the container IDs across different perfect hash buckets so that the number of CIDs in every perfect hash bucket is the same or nearly the same. Individual perfect hash functions are created for each perfect hash bucket. With container IDs as keys, the process maps n keys to n positions to reduce any extra memory overhead. The perfect hash function is implemented using a compress, hash, displace (CHD) algorithm using two levels of hash functions. The level 1 hash functions divides the keys into multiple internal buckets with a defined average number of keys per bucket. The CHD algorithm iteratively tries different level 2 hash variables to achieve collision-free mapping.
机译:用于大型记录的记忆有效完美散列的实施例。容器ID集分为多个固定范围大小。然后将这些范围映射到完美的哈希铲斗,直到填充每个铲斗以均匀地分布在不同的完美哈希桶上的容器ID,以便每个完美的哈希铲斗中的CID数量相同或几乎相同。为每个完美的哈希桶创建个性完美的哈希函数。使用容器ID作为键,过程将n个键映射到N个位置以减少任何额外的存储器开销。完美的散列函数是使用压缩,散列,取代(CHD)算法实现的两个散列函数实现。 1级散列函数将键划分为多个内部桶,每个桶的平均平均键数量。 CHD算法迭代地尝试不同的2级散列变量来实现无碰撞映射。

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