The performance of the BinaryTrie structure is not that impressive. The number of elements, , stored in the structure is at most , so . In other words, any of the comparisonbased SSet structures described in other parts of this book are at least as efficient as a BinaryTrie, and don't have the restriction of only being able to store integers.
Next we describe the XFastTrie, which is just a BinaryTrie with hash tablesone for each level of the trie. These hash tables are used to speed up the operation to time. Recall that the operation in a BinaryTrie is almost complete once we reach a node, , where the search path for would like to proceed to (or ) but has no right (respectively, left) child. At this point, the search uses to jump to a leaf, , of the BinaryTrie and either return or its successor in the linked list of leaves. An XFastTrie speeds up the search process by using binary search on the levels of the trie to locate the node .
To use binary search, we need a way to determine if the node we are looking for is above a particular level, , of if is at or below level . This information is given by the highestorder bits in the binary representation of ; these bits determine the search path that takes from the root to level . For an example, refer to Figure 13.6; in this figure the last node, , on search path for 14 (whose binary representation is 1110) is the node labelled at level 2 because there is no node labelled at level 3. Thus, we can label each node at level with an bit integer. Then the node we are searching for is at or below level if and only if there is a node at level whose label matches the highestorder bits of .

In an XFastTrie, we store, for each , all the nodes at level in a USet, , that is implemented as a hash table (). Using this USet allows us to check in constant expected time if there is a node at level whose label matches the highestorder bits of . In fact, we can even find this node using .
The hash tables allow us to use binary search to find . Initially, we know that is at some level with . We therefore initialize and and repeatedly look at the hash table , where . If contains a node whose label matches 's highestorder bits then we set ( is at or below level ), otherwise we set ( is above level ). This process terminates when , in which case we determine that is at level . We then complete the operation using and the doublylinked list of leaves.
T find(T x) { int l = 0, h = w+1, ix = it.intValue(x); Node v, u = r, q = newNode(); while (hl > 1) { int i = (l+h)/2; q.prefix = ix >>> wi; if ((v = t[i].find(q)) == null) { h = i; } else { u = v; l = i; } } if (l == w) return u.x; Node pred = (((ix >>> wl1) & 1) == 1) ? u.jump : u.jump.child[0]; return (pred.child[next] == dummy) ? null : pred.child[next].x; }Each iteration of the loop in the above method decreases by roughly a factor of 2, so this loop finds after iterations. Each iteration performs a constant amount of work and one operation in a USet, which takes constant expected time. The remaining work takes only constant time, so the method in an XFastTrie takes only expected time.
The and methods for an XFastTrie are almost identical to the same methods in a BinaryTrie. The only modifications are for managing the hash tables ,..., . During the operation, when a new node is created at level , this node is added to . During a operation, when a node is removed form level , this node is removed from . Since adding and removing from a hash table take constant expected time, this does not increase the running times of and by more than a constant factor. We omit a code listing or and since it is almost identical to the (long) code listing already provided for the same methods in a BinaryTrie.
The following theorem summarizes the performance of an XFastTrie:
opendatastructures.org