295. Find Median from Data Stream

HardDesignTwo PointersData StreamSortingHeap (Priority Queue)

Problem Description

The problem requires designing a class, MedianFinder, that can handle a stream of integers and provide a way to find the median at any given time. Median, by definition, is the middle value in an ordered list of numbers. If the list has an odd number of elements, the median is simply the middle element. If the list has an even number of elements, there is no single middle element, so the median is calculated by taking the average of the two middle numbers.

Intuition

To find the median efficiently, we need a data structure that allows quick access to the middle elements. Utilizing two heaps is an elegant solution: a max heap to store the smaller half of the numbers and a min heap to store the larger half. This way, the largest number in the smaller half or the smallest number in the larger half can easily give us the median.

In Python, the default heapq module provides a min heap implementation. To get a max heap behavior, we insert negatives of the numbers into a heap.

By balancing the heaps so that their size differs by at most one, we ensure that we either have a single middle element when the combined size is odd (this will be at the top of the larger heap), or two middle elements when the combined size is even (the top of each heap).

The addNum method works by adding a new number to the max heap (smaller half) first, by pushing its negative value. We then pop the top value from the max heap and push it to the min heap (larger half) to maintain the order and balance. If the larger half has more than one extra element compared to the smaller half, we move the top element from the larger half to the smaller half.

findMedian checks the current size of the heaps. If the heaps are of the same size, the median is the average of the top values of both heaps. If the sizes differ, the median is the top element of the larger heap.

Learn more about Two Pointers, Data Stream, Sorting and Heap (Priority Queue) patterns.

Solution Approach

The solution is based on maintaining two heaps (h1 and h2), which are used to simulate a max heap and a min heap respectively.

h1 (max heap) stores the smaller half of the numbers, and we simulate it by pushing negatives of the numbers into a min heap.
h2 (min heap) stores the larger half of the numbers as they are, since the heapq library already implements a min heap.

Here's how the methods of the MedianFinder class work:

The __init__ method simply initializes two empty lists, h1 and h2, that we'll use as heaps.
addNum adds a new number to the data structure:
1. We first add the number to h1 (which is simulating a max heap) by pushing its negative.
2. Then, we balance the max heap by popping an element from h1 and pushing it onto h2 (min heap).
3. We check if h2 (larger half) has more than one element than h1 (smaller half). If it does, we balance the heaps by moving the top element from h2 to h1.
findMedian computes the median based on the current elements:
1. If h2 has more elements than h1, the median is just the top element of h2 (the smallest number in the larger half).
2. If h1 and h2 have the same number of elements, the median is the average of the top element of h1 (the largest number in the smaller half, remember h1 is storing negatives) and the top element of h2 (the actual smallest number in the larger half).

The efficiency of adding numbers is O(log n) due to the heap operations, and finding the median is O(1) since it involves only accessing the tops of the heaps. This solution is quite efficient and handles the streaming data well.

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Example Walkthrough

Let's walk through a small example to illustrate the implementation of the MedianFinder class using the given solution approach:

Initialize the MedianFinder class, so we start with two empty heaps, h1 (max heap) and h2 (min heap).
We execute addNum(3). Here's how it works:
- We insert -3 into h1 to keep track of the max heap.
- We pop -3 from h1 (which is 3 in its original form) and push it to h2.
- Since h1 is now empty and h2 has only one element, no balancing is needed.
Next, we execute addNum(1):
- We insert -1 into h1.
- We pop -1 from h1 (which is 1 in its original form) and push it to h2.
- Now h2 has two elements (1 and 3), we pop 1 from h2 and push its negative into h1 to balance the heaps.
At this point, h1 has the single element -1, and h2 has the single element 3.
We add another number by calling addNum(5):
- We insert -5 into h1.
- We pop -5 from h1 (which is 5 in its original form) and push it into h2.
- h2 now having 3 and 5, is larger than h1 which only has -1. No additional balancing is needed.
Now, when calling findMedian():
- Since we have a total of 3 elements and h1 has 1 element and h2 has 2 elements, the median is the top of h2 which is 3.
Let's add another number by executing addNum(2):
- We insert -2 into h1.
- We pop -2 from h1 (which is 2 in its original form) and push it to h2.
- Since h2 (with elements 2, 3, and 5) now has more elements, we pop 2 from h2 and push its negative into h1 to balance the heaps. Now h1 has -2 and -1, and h2 has 3 and 5.
Calling findMedian() now gives us:
- Since h1 and h2 have the same number of elements (2 each), the median is the average of the top element of h1 (-1, which is 1 when we negate it) and the top element of h2 (which is 3), giving us a median of (1 + 3) / 2 = 2.

Through these steps, we can see how the MedianFinder class handles the addition of numbers and maintains a structure that allows us to easily find the median at any point. The use of two heaps is instrumental in efficiently managing the median calculation for a stream of data.

Solution Implementation

1from heapq import heappush, heappop
2
3class MedianFinder:
4    def __init__(self):
5        """
6        Initialize your data structure here.
7        """
8        self.small = []  # Max heap (simulated with negative values)
9        self.large = []  # Min heap
10
11    def addNum(self, num: int) -> None:
12        """
13        Adds a number into the data structure.
14        """
15        heappush(self.small, -num)  # Add to max heap
16        # Move the largest element of small (max heap) to large (min heap)
17        heappush(self.large, -heappop(self.small))
18
19        # If large heap has more elements, move the smallest element of large to small
20        if len(self.large) > len(self.small):
21            heappush(self.small, -heappop(self.large))
22
23    def findMedian(self) -> float:
24        """
25        Returns the median of current data stream
26        """
27        # If the heaps have equal size, the median is the average of the two heap's top elements
28        if len(self.small) == len(self.large):
29            return (-self.small[0] + self.large[0]) / 2.0
30        # If the small heap has more elements, return the top element of the small heap (which is the max element)
31        elif len(self.small) > len(self.large):
32            return -self.small[0]
33        # Otherwise, return the top element of the large heap (which is the min element)
34        else:
35            return self.large[0]
36

1import java.util.PriorityQueue;
2import java.util.Collections;
3
4class MedianFinder {
5    private PriorityQueue<Integer> minHeap = new PriorityQueue<>(); // Min-heap to store the larger half of the numbers
6    private PriorityQueue<Integer> maxHeap = new PriorityQueue<>(Collections.reverseOrder()); // Max-heap to store the smaller half of the numbers
7
8    /** Initialize MedianFinder. */
9    public MedianFinder() {
10        // The constructor is kept empty as there's nothing to initialize outside the declarations.
11    }
12
13    /** Adds a number into the data structure. */
14    public void addNum(int num) {
15        minHeap.offer(num); // Add the number to the min-heap
16        maxHeap.offer(minHeap.poll()); // Balance the heaps by moving the smallest number of min-heap to max-heap
17
18        // Ensure max-heap has equal or one more element than the min-heap
19        if (maxHeap.size() > minHeap.size() + 1) {
20            minHeap.offer(maxHeap.poll()); // Move the maximum number of max-heap to min-heap
21        }
22    }
23
24    /** Returns the median of current data stream. */
25    public double findMedian() {
26        if (maxHeap.size() > minHeap.size()) {
27            // If max-heap has more elements, the median is the top of the max-heap
28            return maxHeap.peek();
29        }
30        // Otherwise, the median is the average of the tops of both heaps
31        return (minHeap.peek() + maxHeap.peek()) / 2.0;
32    }
33}
34
35/**
36 * Example of usage:
37 * MedianFinder medianFinder = new MedianFinder();
38 * medianFinder.addNum(num); // Add a number
39 * double median = medianFinder.findMedian(); // Get the current median
40 */
41

1#include <queue>
2#include <vector>
3
4class MedianFinder {
5public:
6    MedianFinder() {
7        // Constructor doesn't need any code,
8        // as the priority queues are automatically initialized.
9    }
10
11    // Inserts a number into the data structure.
12    void addNum(int num) {
13        // Add the new number to the max heap.
14        maxHeap.push(num);
15
16        // Now balance the heaps by always having the top of the max heap
17        // ready to move to the min heap to maintain the ordering.
18        minHeap.push(maxHeap.top());
19        maxHeap.pop();
20
21        // If the min heap has more elements than the max heap,
22        // move the top element back to the max heap to maintain
23        // size property of heaps.
24        if (minHeap.size() > maxHeap.size()) {
25            maxHeap.push(minHeap.top());
26            minHeap.pop();
27        }
28    }
29
30    // Finds and returns the median of all numbers inserted.
31    double findMedian() {
32        // If the max heap is larger, the median is at the top of the max heap.
33        if (maxHeap.size() > minHeap.size()) {
34            return maxHeap.top();
35        }
36        // If both heaps have the same size, the median is the average of
37        // the tops of both heaps.
38        return (double) (minHeap.top() + maxHeap.top()) / 2;
39    }
40
41private:
42    // Max heap for the lower half of the numbers.
43    std::priority_queue<int> maxHeap;
44    // Min heap for the upper half of the numbers
45    // (using greater<> to make it a min heap).
46    std::priority_queue<int, std::vector<int>, std::greater<int>> minHeap;
47};
48
49/**
50 * The MedianFinder object will be instantiated and called as such:
51 * MedianFinder* obj = new MedianFinder();
52 * obj->addNum(num); // Method to add a number into the MedianFinder.
53 * double median = obj->findMedian(); // Method to find and return the median.
54 */
55

1class MedianFinder {
2    private small: number[]; // Simulated Max Heap (we'll use Min Heap logic but with negated values)
3    private large: number[]; // Min Heap
4
5    constructor() {
6        this.small = [];
7        this.large = [];
8    }
9
10    addNum(num: number): void {
11        // Add to max heap (simulated by adding negative number to min heap)
12        this.heappush(this.small, -num);
13        // Ensure the smallest of the max heap moves to the min heap
14        this.heappush(this.large, -this.heappop(this.small));
15
16        // Balance the heaps so that either both have the same size or small has one more element
17        if (this.large.length > this.small.length) {
18            this.heappush(this.small, -this.heappop(this.large));
19        }
20    }
21
22    findMedian(): number {
23        if (this.small.length > this.large.length) {
24            // If the small heap is larger, the median is its top element (remember to negate it back)
25            return -this.small[0];
26        } else if (this.small.length === this.large.length) {
27            // If the heaps are of equal size, the median is the average of the tops of both heaps
28            return (-this.small[0] + this.large[0]) / 2;
29        } else {
30            // If the large heap somehow ends up with more elements, this will handle that case
31            return this.large[0];
32        }
33    }
34
35    private heappush(heap: number[], val: number): void {
36        heap.push(val);
37        heap.sort((a, b) => a - b); // This simulates the heap push by sorting
38    }
39
40    private heappop(heap: number[]): number {
41        return heap.shift()!; // This simulates the heap pop by removing the first element
42    }
43}
44
45/**
46* Your MedianFinder object will be instantiated and called as such:
47* var obj = new MedianFinder()
48* obj.addNum(num)
49* var param_2 = obj.findMedian()
50*/
51

Time and Space Complexity

The provided class MedianFinder has two methods: addNum and findMedian, used for adding numbers and finding the median, respectively. The class maintains two heaps: h1 is the min-heap that stores the larger half of the numbers, and h2 is the max-heap that stores the smaller half of the numbers (as negatives).

`addNum` Method

Time Complexity:

Inserting an element into a heap (heappush) has a time complexity of O(log n), where n is the number of elements in the heap.
Removing the smallest element from a min heap (heappop on h1) or the largest element from a max heap (heappop on h2) has a time complexity of O(log n).
The addNum method does at most two push and pop operations each time it is called:
1. heappush(self.h1, num): Insert num into the min-heap h1.
2. heappush(self.h2, -heappop(self.h1)): Pop the minimum value from h1, negate it (to simulate a max-heap), and push it into h2.
3. heappush(self.h1, -heappop(self.h2)): This happens only if the size of h2 exceeds the size of h1 by more than one, ensuring the difference in the number of elements in the two heaps never exceeds one.

Considering the above steps, the time complexity for addNum is O(log n) because of the heap operations.

Space Complexity:

The space complexity of the MedianFinder class is O(n), where n is the number of elements inserted into the MedianFinder. This is due to the storage of all elements across two heaps.

`findMedian` Method

Time Complexity:

Accessing the top element of a heap (minimum value in h1 and maximum value in h2) has a time complexity of O(1).
findMedian performs at most one arithmetic operation, which is done in constant time.

Therefore, the time complexity for findMedian is O(1).

Space Complexity:

The findMedian method does not use any additional space that depends on the input size, so its space complexity is O(1).

Learn more about how to find time and space complexity quickly using problem constraints.

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