Optimise merges involving multiple clustering columns

Since the new storage format is dead in the water for the moment, we should do our best to optimise current behaviour. When merging data from multiple sstables with multiple clustering columns, currently we must incur the full costs of comparison for the entire matching prefix, and must heapify every cell in our PriorityQueue, incurring lg(N) of these costlier comparisons for every cell we merge, where N is the number of sources we're merging.

Essentially I'm proposing a trie-based merge approach as a replacement for the ManyToOne MergeIterator, wherein we treat each clustering component as a tree underwhich all Cells with a common prefix occur. We then perform a tree merge, rather than a flat merge. For byte-order fields this trie can even be a full binary-trie (although built on the fly). The advantage here is that we rapidly prune merges involving disjoint ranges, so that instead of always incurring lg(N) costs on each new record, we may often incur O(1) costs. For timeseries data, for instance, we could merge dozens of files and so long as they were non-overlapping our CPU burden would be little more than reading from a single file.

On top of this, we no longer incur any of the shared prefix repetition costs, since we compare each prefix piece-wise, and only once.