Reservoir sampling is a family of randomized algorithms for randomly choosing *k *samples from a list of *n* items, where *n* is either a very large or unknown number. Typically *n *is large enough that the list doesn’t fit into main memory. For example, a list of search queries in Google and Facebook.

So we are given a big array (or stream) of numbers (to simplify), and we need to write an efficient function to randomly select *k* numbers where *1 <= k <= n*. Let the input array be *stream[].*

A **simple solution **is to create an array *reservoir[]* of maximum size *k*. One by one randomly select an item from *stream[0..n-1]*. If the selected item is not previously selected, then put it in *reservoir[]*. To check if an item is previously selected or not, we need to search the item in *reservoir[]*. The time complexity of this algorithm will be *O(k^2)*. This can be costly if *k* is big. Also, this is not efficient if the input is in the form of a stream.

It **can be solved in O(n) time**. The solution also suits well for input in the form of stream. The idea is similar to this post. Following are the steps.

**1)** Create an array *reservoir[0..k-1]* and copy first *k* items of *stream[]* to it.

**2) **Now one by one consider all items from *(k+1)*th item to *n*th item.

…**a)** Generate a random number from 0 to *i* where *i* is index of current item in *stream[]*. Let the generated random number is *j*.

…**b)** If* j* is in range 0 to *k-1*, replace *reservoir[j]* with *arr[i]*

Following is C implementation of the above algorithm.

## C/C++

// An efficient program to randomly select k items from a stream of items #include <stdio.h> #include <stdlib.h> #include <time.h> // A utility function to print an array void printArray(int stream[], int n) { for (int i = 0; i < n; i++) printf("%d ", stream[i]); printf("\n"); } // A function to randomly select k items from stream[0..n-1]. void selectKItems(int stream[], int n, int k) { int i; // index for elements in stream[] // reservoir[] is the output array. Initialize it with // first k elements from stream[] int reservoir[k]; for (i = 0; i < k; i++) reservoir[i] = stream[i]; // Use a different seed value so that we don't get // same result each time we run this program srand(time(NULL)); // Iterate from the (k+1)th element to nth element for (; i < n; i++) { // Pick a random index from 0 to i. int j = rand() % (i+1); // If the randomly picked index is smaller than k, then replace // the element present at the index with new element from stream if (j < k) reservoir[j] = stream[i]; } printf("Following are k randomly selected items \n"); printArray(reservoir, k); } // Driver program to test above function. int main() { int stream[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; int n = sizeof(stream)/sizeof(stream[0]); int k = 5; selectKItems(stream, n, k); return 0; }

## Java

// An efficient Java program to randomly // select k items from a stream of items import java.util.Arrays; import java.util.Random; public class ReservoirSampling { // A function to randomly select k items from stream[0..n-1]. static void selectKItems(int stream[], int n, int k) { int i; // index for elements in stream[] // reservoir[] is the output array. Initialize it with // first k elements from stream[] int reservoir[] = new int[k]; for (i = 0; i < k; i++) reservoir[i] = stream[i]; Random r = new Random(); // Iterate from the (k+1)th element to nth element for (; i < n; i++) { // Pick a random index from 0 to i. int j = r.nextInt(i + 1); // If the randomly picked index is smaller than k, // then replace the element present at the index // with new element from stream if(j < k) reservoir[j] = stream[i]; } System.out.println("Following are k randomly selected items"); System.out.println(Arrays.toString(reservoir)); } //Driver Program to test above method public static void main(String[] args) { int stream[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; int n = stream.length; int k = 5; selectKItems(stream, n, k); } } //This code is contributed by Sumit Ghosh

Output:

Following are k randomly selected items 6 2 11 8 12 Note: Output will differ every time as it selects and prints random elements

Time Complexity: O(n)

**How does this work?**

To prove that this solution works perfectly, we must prove that the probability that any item *stream[i] *where *0 <= i < n *will be in final *reservoir[]* is *k/n*. Let us divide the proof in two cases as first* k* items are treated differently.

**Case 1: For last n-k stream items, i.e., for stream[i] where k <= i < n **

For every such stream item

*stream[i]*, we pick a random index from 0 to

*i*and if the picked index is one of the first

*k*indexes, we replace the element at picked index with

*stream[i]*

To simplify the proof, let us first consider the *last item*. The probability that the last item is in final reservoir = The probability that one of the first *k* indexes is picked for last item = *k/n *(the probability of picking one of the *k* items from a list of size* n*)

Let us now consider the *second last item*. The probability that the second last item is in final *reservoir[]* = [Probability that one of the first *k* indexes is picked in iteration for *stream[n-2]*] X [Probability that the index picked in iteration for *stream[n-1]* is not same as index picked for *stream[n-2]* ] = [*k/(n-1)]*[(n-1)/n*] = *k/n*.

Similarly, we can consider other items for all stream items from *stream[n-1]* to *stream[k]* and generalize the proof.

**Case 2: For first k stream items, i.e., for stream[i] where 0 <= i < k **

The first

*k*items are initially copied to

*reservoir[]*and may be removed later in iterations for

*stream[k]*to

*stream[n]*.

The probability that an item from

*stream[0..k-1]*is in final array = Probability that the item is not picked when items

*stream[k], stream[k+1], …. stream[n-1]*are considered =

*[k/(k+1)] x [(k+1)/(k+2)] x [(k+2)/(k+3)] x … x [(n-1)/n] = k/n*

References:

http://en.wikipedia.org/wiki/Reservoir_sampling

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