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LESSON 457 OF 675 · HEAP

Kth Largest Element in a Stream

Practice O(log k)

Numbers arrive one at a time, like the scores of a game coming in live, and after each one you must say which number is in k-th place: the k-th largest of all the numbers so far. Equal numbers count separately: in [8, 7, 7, 5] the 2nd largest is 7, and so is the 3rd.

Implement the class KthLargest:

  • KthLargest(k, nums) creates the object with the integer k and the numbers nums that have arrived already.
  • add(val) adds the number val and returns the k-th largest of all the numbers so far.

The input describes one new KthLargest and a list of add calls, applied in order. The output lists what the add calls return, in order. The starter code spells these names the way your language does.

 

Example 1:

Input:
    KthLargest(3, [4, 5, 8, 2])
    add(3)
    add(5)
    add(10)
    add(9)
    add(4)
Output: 4 5 5 8 8
Explanation:
After add(3) the numbers are [2, 3, 4, 5, 8]: the three largest are 8, 5 and 4, so the 3rd largest is 4.
After add(5): 8, 5, 5 are the three largest, so the answer is 5.
After add(10): 10, 8, 5, answer 5. After add(9): 10, 9, 8, answer 8.
add(4) changes nothing at the top: 4 is smaller than the 3rd largest, so the answer stays 8.

Example 2:

Input:
    KthLargest(1, [])
    add(-3)
    add(-2)
    add(-4)
    add(0)
    add(4)
Output: -3 -2 -2 0 4
Explanation: With k = 1, add returns the largest number so far. No numbers have arrived when the object is created, so after the first add, -3 is the only one, and the largest.

Example 3:

Input:
    KthLargest(4, [7, 7, 7, 7, 8, 3])
    add(2)
    add(10)
    add(9)
    add(9)
Output: 7 7 7 8
Explanation: Equal numbers count separately: in [8, 7, 7, 7, 7, 3, 2] the 4th largest is the third 7. After 10 and 9 arrive the four largest are 10, 9, 8, 7. The second 9 pushes the last 7 out: 10, 9, 9, 8, so the answer becomes 8.

 

Constraints:

  • 1 <= k <= 105
  • 0 <= nums.length <= 105
  • -104 <= nums[i], val <= 104
  • At most 105 calls are made to add.
  • When add returns, at least k numbers have arrived, so the k-th largest always exists.

Note:

Each add should run in O(log k) time, and the object should keep only O(k) values. Sorting all the numbers again on every add would take O(n log n) time per call, where n is how many numbers have arrived so far.

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