In this blog post, I will be giving a brief introduction about another data structure that is very important for technical interviews.This data structure is called K-way Merge. This pattern is very useful to solve problems that involve a sets of sorted arrays. Whenever, we are given K sorted arrays, we can use a Heap to efficiently perform a sorted traversal of all elements of all arrays.
We can push the smallest element of each sorted array in a Min Heap to get the overall minimum. While inserting elements to the Min Heap, we keep track of which array the element came from.
The K-way Merge pattern looks like this;
- We can push the smallest (first) element of each sorted array in a Min Heap to get the overall minimum.
- After this step, we can take out the smallest (top) element from the heap and then add it to the merged list.
- After removing the smallest element from the heap, insert the next element of the same list into the heap.
- We can repeat steps 2 and 3 to populate the merged list in sorted order.
For this pattern,
Time Complexity = O(N log K) where N is the total number of elements in all the K input arrays.
Space Complexity = O(K)
Ways to identify this pattern:
- For this kind of pattern, the problem will feature sorted arrays, lists, or a matrix
- The problem will ask us to merge sorted lists, find the smallest element in a sorted list.