With a TreeMap
it\'s trivial to provide a custom Comparator
, thus overriding the semantics provided by Comparable
objects added to the
This is an interesting idea, but it's absolutely horrendous for performance. The reason for this is quite fundamental to the idea of a hashtable: the ordering cannot be relied upon. Hashtables are very fast (constant time) because of the way in which they index elements in the table: by computing a pseudo-unique integer hash for that element and accessing that location in an array. It's literally computing a location in memory and directly storing the element.
This contrasts with a balanced binary search tree (TreeMap
) which must start at the root and work its way down to the desired node every time a lookup is required. Wikipedia has some more in-depth analysis. To summarize, the efficiency of a tree map is dependent upon a consistent ordering, thus the order of the elements is predictable and sane. However, because of the performance hit imposed by the "traverse to your destination" approach, BSTs are only able to provide O(log(n)) performance. For large maps, this can be a significant performance hit.
It is possible to impose a consistent ordering on a hashtable, but to do so involves using techniques similar to LinkedHashMap
and manually maintaining the ordering. Alternatively, two separate data structures can be maintained internally: a hashtable and a tree. The table can be used for lookups, while the tree can be used for iteration. The problem of course is this uses more than double the required memory. Also, insertions are only as fast as the tree: O(log(n)). Concurrent tricks can bring this down a bit, but that isn't a reliable performance optimization.
In short, your idea sounds really good, but if you actually tried to implement it, you would see that to do so would impose massive performance limitations. The final verdict is (and has been for decades): if you need performance, use a hashtable; if you need ordering and can live with degraded performance, use a balanced binary search tree. I'm afraid there's really no efficiently combining the two structures without losing some of the guarantees of one or the other.