This article needs attention from an expert in computing.(February 2010)
Reservoir sampling is a family of randomized algorithms for randomly choosing a sample of k items from a list S containing 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.
- 1 Example: Sample size 10
- 2 Algorithm R
- 3 Reservoir with Random Sort
- 4 Weighted Random Sampling using Reservoir
- 5 Distributed Reservoir Sampling
- 6 Relation to Fisher-Yates shuffle
- 7 Fast Approximation
- 8 Example implementation
- 9 Statistical properties
- 10 Limitations
- 11 See also
- 12 References
Example: Sample size 10
Suppose we see a sequence of items, one at a time. We want to keep ten items in memory, and we want them to be selected at random from the sequence. If we know the total number of items (n), then the solution is easy: select ten distinct indices i between 1 and n with equal probability, and keep the i-th elements. The problem is that we do not always know n in advance. A possible solution is the following:
- Keep the first ten items in memory.
- When the i-th item arrives (for ):
- with probability , keep the new item (discard an old one, selecting which to replace at random, each with chance 1/10)
- with probability , keep the old items (ignore the new one)
- when there are 10 items or fewer, each is kept with probability 1;
- when there are 11 items, each of them is kept with probability 10/11; for the old items, that is (1)(1/11 + (10/11)(9/10)) = 1/11 + 9/11 = 10/11
- In other words, the 10 old items are kept either if the new one is not selected (1/11) or the new one is selected to replace one of the other 9 items (10/11)*(9/10), and since "or" is represented with addition, we derive (1/11 + (10/11)(9/10)).
- when there are 12 items, the twelfth item is kept with probability 10/12, and each of the previous 11 items are also kept with probability (10/11)(2/12 + (10/12)(9/10)) = (10/11)(11/12) = 10/12;
- by induction, it is easy to prove that when there are n items, each item is kept with probability 10/n.
The most common example was labelled Algorithm R by Jeffrey Vitter in his paper on the subject. This simple O(n) algorithm as described in the Dictionary of Algorithms and Data Structures consists of the following steps (assuming k < n and using one-based array indexing):
(* S has items to sample, R will contain the result *) ReservoirSample(S[1..n], R[1..k]) // fill the reservoir array for i = 1 to k R[i] := S[i] // replace elements with gradually decreasing probability for i = k+1 to n j := random(1, i) // important: inclusive range if j <= k R[j] := S[i]
The algorithm creates a "reservoir" array of size and populates it with the first items of . It then iterates through the remaining elements of until is exhausted. At the th element of , the algorithm generates a random number between 1 and . If is less than or equal to , the th element of the reservoir array is replaced with the th element of . In effect, for all , the th element of is chosen to be included in the reservoir with probability . Similarly, at each iteration the th element of the reservoir array is chosen to be replaced with probability , which simplifies to . It can be shown that when the algorithm has finished executing, each item in has equal probability (i.e. ) of being chosen for the reservoir.
To see this, consider the following proof by induction. After the th round, let us assume, the probability of a number being in the reservoir array is . Since the probability of the number being replaced in the th round is , the probability that it survives the th round is . Thus, the probability that a given number is in the reservoir after the th round is the product of these two probabilities, i.e. the probability of being in the reservoir after the th round, and surviving replacement in the th round. This is . Hence, the result holds for , and is therefore true by induction.
Reservoir with Random Sort
A simple reservoir-based algorithm can be designed using random sort and implemented using priority queue data structure. This algorithm assigns random number as keys to each item and maintain k items with minimum value for keys. In essence, this is equivalent to assigning a random number to each item as key, sorting items using these keys and taking top k items. The worse case run time of the algorithm is while the best case runtime is . Even though the worse case runtime is not as good as Algorithm R, this algorithm can easily be extended to weighted sampling. Note that both algorithms can operate on streams of unspecified lengths.
(* S is a stream of items to sample, R will contain the result S.Current returns current item in stream S.Next advances stream to next position max-priority-queue supports: Count -> number of items in priority queue Maximum -> returns maximum key value of all items Extract-Max() -> Remove the item with max key Insert(key, Item) -> Adds item with specified key *) ReservoirSample(S[1..?], R[1..k]) H = new max-priority-queue while S has data r = Random(0,1) if H.Count < k H.Insert(r, S.Current) else if H.Maximum > r H.Extract-Max() H.Insert(r, S.Current) S.Next
Weighted Random Sampling using Reservoir
In many applications sampling is required to be according to the weights that are assigned to each items available in set. For example, it might be required to sample queries in a search engine with weight as number of times they were performed so that the sample can be analyzed for overall impact on user experience. There are two ways to interpret weights assigned to each item in the set:
- Let the weight of each item be and sum of all weights be W. We can convert weight to probability of item getting selected in sample as .
- Let the weight of two items i and j be and . Let the probability of item i getting selected in sample be , then we give .
The following algorithm was given by Efraimidis and Spirakis that uses interpretation 1:
(* S is a stream of items to sample, R will contain the result S.Current returns current item in stream S.Weight returns weight of current item in stream S.Next advances stream to next position The power operator is represented by ^ min-priority-queue supports: Count -> number of items in priority queue Minimum() -> returns minimum key value of all items Extract-Min() -> Remove the item with minimum key Insert(key, Item) -> Adds item with specified key *) ReservoirSample(S[1..?], R[1..k]) H = new min-priority-queue while S has data r = Random(0,1) ^ (1/S.Weight) // important: inclusive range if H.Count < k H.Insert(r, S.Current) else if H.Minimum < r H.Extract-Min() H.Insert(r, S.Current) S.Next
This algorithm is identical to the algorithm given in Reservoir Sampling with Random Sort except for the line how we generate the key using random number generator. The algorithm is equivalent to assigning each item a key where r is the random number and then sort items using these keys and finally select top k items for the sample.
Following algorithm was given by M. T. Chao uses interpretation 2:
(* S has items to sample, R will contain the result S[i].Weight contains weight for each item *) WeightedReservoir-Chao(S[1..n], R[1..k]) WSum = 0 // fill the reservoir array for i = 1 to k R[i] := S[i] WSum = WSum + S[i].Weight/k for i = k+1 to n WSum = WSum + S[i].Weight/k p = S[i].Weight / WSum // probability for this item j := random(0, 1); // important: inclusive range if j <= p // select item according to probability R[random(1,k)] := S[i] //uniform selection in reservoir for replacement
For each item, its relative weight is calculated and used to randomly decide if the item will be added into the reservoir. If the item is selected, then one of the existing items of the reservoir is uniformly selected and replaced with the new item. The trick here is that, if the probabilities of all items in the reservoir are already proportional to their weights, then by selecting uniformly which item to replace, the probabilities of all items remain proportional to their weight after the replacement.
Distributed Reservoir Sampling
In many applications, amount of data from which a small sample is needed is too large and it is desirable to distribute sampling tasks among many machines in parallel to speed up the process. A simple approach that is often used, although less performant, is to assign a random number as key to each item and then perform a distributed sort and finally obtain a sample of desired size from top k items. If weighted sample is desired then key is computed using where r is the random number and is the weight of an item. The inefficiency in this approach obviously arises from required distributed sort on very large amount of data.
Another more efficient approach for distributed weighted random sampling is as follows:
- Distribute data among m machines.
- Each machine does its own weighted sampling using key as described in previous section and produces a sample of size <= k items.
- Collects all m samples of size <= k. We should have total items .
- Now sample k items from items from step 3 using key that was already computed in Step 2. This means instead of re-generating key using random number generator in sampling algorithm, we use the key we already had assigned in step 2.
The Step 4 uses keys from Step 2 because we might have unbalanced data distribution on machines. For example, lets say k = 1, machine m1 only gets 1 item with weight 10 while machine m2 gets 2 items each with weight 100. Intuitively probability for items from m1 getting in final sample is 10/210. In Step 3, we will get 1 item from m1 as well as m2. If we recalculate keys in step 4 then the probability that item from m1 will be in final sample is 10/110 instead of required 10/210. Now observe that weighted reservoir sampling algorithm from previous section decreases max key value in priority queue as it processes more items. Therefore, items sampled from machine with larger chunk will have lower key values and thus higher chance of getting selected.
Relation to Fisher-Yates shuffle
Suppose one wanted to draw k random cards from a deck of playing cards (i.e., n=52). A natural approach would be to shuffle the deck and then take the top k cards. In the general case, the shuffle also needs to work even if the number of cards in the deck is not known in advance, a condition which is satisfied by the inside-out version of the Fisher-Yates shuffle:
To initialize an array a of n elements to a randomly shuffled copy of S, both 0-based:
a ← S
for i from 1 to n - 1 do
r ← random (0 .. i)
a[i] ← a[r]
a[r] ← S[i]
Note that although the rest of the cards are shuffled, only the top k are important in the present context. Therefore, the array a need only track the cards in the top k positions while performing the shuffle, reducing the amount of memory needed. Truncating a to length k, the algorithm is modified accordingly:
To initialize an array a to k random elements of S (which is of length n), both 0-based:
a ← S
for i from 1 to k - 1 do
r ← random (0 .. i)
a[i] ← a[r]
a[r] ← S[i]
for i from k to n - 1 do
r ← random (0 .. i)
if (r < k) then a[r] ← S[i]
Since the order of the first k cards is immaterial, the first loop can be removed and a can be initialized to be the first k items of S. This yields Algorithm R.
(* S has items to sample, R will contain the result The reservoir size is (k) *) FastApproximateReservoirSample(S[1..n], R[1..k]) // fill the reservoir array for i = 1 to k R[i] := S[i] // Threshold (t) determines when to start fast sampling logic // The optimal value for (t) may vary depending on RNG performance characteristics t := 4 * k // Normal reservoir sampling is fastest up to (t) samples i := 1 + k while (i <= n && i <= t) j := random(1, i) // integer from 1 to i, inclusive if j <= k R[j] := S[i] i := i + 1 // Once gap sizes become significant, it pays to use // fast sampling using an approximate sampling gap distribution while (i <= n) // draw gap size (g) from geometric distribution with probability p = k / i p := k / i u := randomFloat() // random float > 0.0 and <= 1.0 g := floor(log(u) / log(1-p)) // advance over the gap, and assign next element to the reservoir i := i + g if (i <= n) j := random(1, k) // integer 1 to k, inclusive R[j] := S[i] i := i + 1
The following is a simple implementation of the algorithm in Python that samples the set of English Wikipedia page titles:
import random SAMPLE_COUNT = 10 # Force the value of the seed so the results are repeatable random.seed(12345) sample_titles =  for index, line in enumerate(open("enwiki-20091103-all-titles-in-ns0")): # Generate the reservoir if index < SAMPLE_COUNT: sample_titles.append(line) else: # Randomly replace elements in the reservoir # with a decreasing probability. # Choose an integer between 0 and index (inclusive) r = random.randint(0, index) if r < SAMPLE_COUNT: sample_titles[r] = line print sample_titles
Probabilities of selection of the reservoir methods are discussed in Chao (1982) and Tillé (2006). While the first-order selection probabilities are equal to (or, in case of Chao's procedure, to an arbitrary set of unequal probabilities), the second order selection probabilities depend on the order in which the records are sorted in the original reservoir. The problem is overcome by the cube sampling method of Deville and Tillé (2004).
Reservoir sampling makes the assumption that the desired sample fits into main memory, often implying that is a constant independent of . In applications where we would like to select a large subset of the input list (say a third, i.e. ), other methods need to be adopted. Distributed implementations for this problem have been proposed.
- Vitter, Jeffrey S. (1 March 1985). "Random sampling with a reservoir" (PDF). ACM Transactions on Mathematical Software. 11 (1): 37–57. doi:10.1145/3147.3165.
- Black, Paul E. (26 January 2015). "Reservoir sampling". Dictionary of Algorithms and Data Structures. Retrieved 2017-02-09.
- Sunter, A. B. (1977). "List Sequential Sampling with Equal or Unequal Probabilities without Replacement". Applied Statistics. 26 (3): 261. doi:10.2307/2346966.
- Efraimidis, Pavlos S.; Spirakis, Paul G. (2006-03-16). "Weighted random sampling with a reservoir". Information Processing Letters. 97 (5): 181–185. doi:10.1016/j.ipl.2005.11.003.
- Chao, M.T. (1982) A general purpose unequal probability sampling plan. Biometrika, 69 (3): 653-656.
- "Gregable: Reservoir Sampling - Sampling from a stream of elements". gregable.com. Retrieved 2015-07-23.
- Erlandson, Erik J. (2015-11-20). "Very Fast Reservoir Sampling".
- Tillé, Y. (2006). Sampling Algorithms. Springer
- Deville, J.-C., and Y. Tillé (2004). Efficient balanced sampling: The cube method. Biometrika 91 (4): 893-912.
- Reservoir Sampling in MapReduce