Their simplicity and performance makes them an attractive alternative to the well known B-tree algorithms. Testing reveals a dramatic speed advantage for skip lists when compared to B-trees. In addition to the basic speed advantage of the algorithm, skip lists also show an additional speed advantage for large data sets. The binary search doubles the number of items covered at each iteration. The binary search is the way you play the old high-low guessing game.
Binary trees have a bad worst-case performance when the tree becomes one sided.
Figure 1 gives a visual example of a balanced tree for the numbers 1 through 7. Note at this point that there is one and only one balanced tree for a given set of data. The search for this single solution is a dominant factor in the execution speed of any B-Tree algorithm. To find an item in a B-Tree start at the highest level of the tree and then follow either the left or right node depending on whether the compare is less than or greater than the current node. The process is repeated until the item is equal or until a node is reached where there are no leafs.