I would like to implement a map whose number of elements never exceeds a certain limit L
. When the L+1
-th element is inserted, the oldest entry should be removed from the map to empty the space.
I found something similar: Data Structure for Queue using Map Implementations in Java with Size limit of 5. There it is suggested to use a linked hash map, i.e., a hash map that also keeps a linked list of all elements. Unfortunately, that is for java, and I need a solution for C++. I could not find anything like this in the standard library nor in the boost libraries.
Same story here: Add and remove from MAP with limited size
A possible solution for C++ is given here, but it does not address my questions below: C++ how to mix a map with a circular buffer?
I would implement it in a very similar way to what is described there. An hash map to store the key-value pairs and a linked list, or a double-ended queue, of keys to keep the index of the entries. To insert a new value I would add it to the hash map and its key at the end of the index; if the size of the has at this point exceeds the limit, I would pop the first element of the index and remove the entry with that key from the has. Simple, same complexity as adding to the hash map.
Removing an entry requires an iteration over the index to remove the key from there, which has linear complexity for both linked lists and double-ended queue. (Double-ended queues also have the disadvantage that removing an element has itself linear complexity.) So it looks like the remove operation on such a data structure does not preserve the complexity as the underlying has map.
The question is: Is this increase in complexity necessary, or there are some clever ways to implement a limited map data structure so that both insertion and removal keep the same complexity?
Ooops, just posted and immediately realized something important. The size of the index is also limited. If that limit is constant, then the complexity of iterating over it can also be considered constant.
Well, the limit gives an upper bound to the cost of the removal operation.
If the limit is very high one might still prefer a solution that does not involve a linear iteration over the index.