Range Queries for Frequencies of array elements

Given an array of n non-negative integers. The task is to find frequency of a particular element in the arbitrary range of array[]. The range is given as positions (not 0 based indexes) in array. There can be multiple queries of given type.

Examples: 

Input : arr[] = {2, 8, 6, 9, 8, 6, 8, 2, 11};
 left = 2, right = 8, element = 8
 left = 2, right = 5, element = 6 
Output : 3
 1
The element 8 appears 3 times in arr[left-1..right-1]
The element 6 appears 1 time in arr[left-1..right-1]

Naive approach: is to traverse from left to right and update count variable whenever we find the element. 

Below is the code of Naive approach:- 

Frequency of 2 from 1 to 6 = 1
Frequency of 8 from 4 to 9 = 2

Time complexity of this approach is O(right - left + 1) or O(n) 
Auxiliary space: O(1)

An Efficient approach is to use hashing. In C++, we can use unordered_map

• At first, we will store the position in map[] of every distinct element as a vector like that 

 int arr[] = {2, 8, 6, 9, 8, 6, 8, 2, 11};
 map[2] = {1, 8}
 map[8] = {2, 5, 7}
 map[6] = {3, 6} 
 ans so on...

• As we can see that elements in map[] are already in sorted order (Because we inserted elements from left to right), the answer boils down to find the total count in that hash map[] using binary search like method. 
   
• In C++ we can use lower_bound which will returns an iterator pointing to the first element in the range [first, last] which has a value not less than 'left'. and upper_bound returns an iterator pointing to the first element in the range [first,last) which has a value greater than 'right'. 
   
• After that we just need to subtract the upper_bound() and lower_bound() result to get the final answer. For example, suppose if we want to find the total count of 8 in the range from [1 to 6], then the map[8] of lower_bound() function will return the result 0 (pointing to 2) and upper_bound() will return 2 (pointing to 7), so we need to subtract the both the result like 2 - 0 = 2 . 
   

Below is the code of above approach 

Frequency of 2 from 1 to 6 = 1
Frequency of 8 from 4 to 9 = 2

This approach will be beneficial if we have a large number of queries of an arbitrary range asking the total frequency of particular element.
Time complexity: O(log N) for single query.
Auxiliary Space: O(N)