Ofek Gila, Michael T. Goodrich, Robert E. Tarjan. “Zip-zip Trees: Making Zip Trees More Balanced, Biased, Compact, or Persistent”. To appear in the Workshop on Algorithms and Data Structures 2023.
We define simple variants of zip trees, called zip-zip trees, which provide several advantages over zip trees, including overcoming a bias that favors smaller keys over larger ones. We analyze zip-zip trees theoretically and empirically, showing, e.g., that the expected depth of a node in an n-node zip-zip tree is at most 1.3863 log n − 1 +o(1), which matches the expected depth of treaps and binary search trees built by uniformly random insertions. Unlike these other data structures, however, zip-zip trees achieve their bounds using only O(log log n) bits of metadata per node, w.h.p., as compared to the Θ(log n) bits per node required by treaps. In fact, we even describe a ``just-in-time'' zip-zip tree variant, which needs just an expected O(1) number of bits of metadata per node. Moreover, we can define zip-zip trees to be strongly history independent, whereas treaps are generally only weakly history independent. We also introduce _biased zip-zip trees_, which have an explicit bias based on key weights, so the expected depth of a key, k, with weight, w_k, is O(log(W/w_k)), where W is the weight of all keys in the weighted zip-zip tree. Finally, we show that one can easily make zip-zip trees partially persistent with only O(n) space overhead w.h.p.