Suppose we have a large collection of documents, and we wish you identify which documents are approximately the same as each other. For instance, we may have crawled the web over some period of time, and expect to have fetched the “same page” several times, but to see slight differences in metadata, or that we have several revisions of a page following small edits.
In this post I want to explore the method of approximate deduplication via Jaccard similarity and the MinHash approximation trick. This is a commonlyused approach to this problem (e.g. the GPT3 paper describes using it as part of their dataset preparation pipeline), but one I had not encountered until recently, and which I find to be pretty interesting.
Similarity 🔗︎
Our approach to approximate deduplication will be to define a notion of “similarity” between any two documents, and then to search for pairs where their similarity value is above some threshold. So if we have some universe of possible documents \(U\), we might define a similarity measure between pairs of documents: $$S: U \times U \rightarrow [0,1]$$ and consider two documents “approximate duplicates” if \(S(A,B) \geq S_\textrm{crit}\).
It’s worth noticing that this definition is not in general transitive: we may well have three documents \(A, B, C\) such that \(S(A,B)\geq{}S_\textrm{crit}\) and \(S(B,C) \geq{} S_\textrm{crit}\) but \(S(A,C) < S_\textrm{crit}\). That means that “approximately identical” is not an equivalence relation, and is part of the reason that approximate deduplication is trickier to reason about, and to perform at scale, compared to finding exact matches.
Jaccard similarity 🔗︎
One measure of similarity widely used across several domains, including largescale text processing is the Jaccard index, also known as the Jaccard similarity coefficient.
The Jaccard index is a function that compares sets, and characterizes the similarity of two finite sets as the ratio of their overlap to the size of their union:
$$J(A,B) = \frac{A\cap{}B}{A\cup{}B}$$
I find this calculation makes some intuitive sense: if two sets are similar, they should have mostly the same elements. This means the sets are of similar sizes, and their union is only slightly larger, and their intersection only slightly smaller. If the sets are very different, or of very different sizes, then the union will be large and the intersection small.
It has two very natural limit points, which define its range: For two disjoint sets, the numerator \(A\cap{}B\) is zero, and the index goes to zero. But if the sets are identical, \(A\cap{}B = A\cup{}B = A = B\), and the Jaccard similarity is 1.
Note that Jaccard similarity operates on sets, but we’re starting with documents (typically represented as Unicode strings). I’ll return at the end to how we can turn textual documents into sets, but for now I’ll just assume that we’ve done so. I’ll refer to these sets as “feature sets,” where the individual elements are “features” of the document.
Scaling Jaccard similarity 🔗︎
We now have a definition of “approximate similarity”: We convert documents into feature sets, and then search for sets with high Jaccard similarity.
For very small corpora, we could potentially apply that definition directly. However, considering every pair of documents scales as \(O(n^2)\) with the size of our corpus, which rapidly becomes infeasible.
For finding exact duplicates, we avoid the quadratic cost by hashing; we hash documents and group them by their hash value, which puts identical documents (and, with good probability, only identical documents) into the same hash bucket. We want to find a similar shortcut for approximate duplicates; in the language of the field, we want a localitysensitive hash.
It turns out such techniques exist for Jaccard similarity! Let’s see how it works.
Approximating Jaccard similarity 🔗︎
We’ll first consider the problem of approximating the Jaccard similarity between two documents. We’ll find an approximation that avoid examining the entire sets, and which only requires a small, fixedsize “signature,” which we can precompute for each document independently. Then, we’ll use the structure of that signature to find ways to group documents such that (with good probability) similar documents, and mostlyonly similar documents, group together.
MinHash signatures 🔗︎
Recall that the Jaccard similarity is the ratio of two sizes: the intersection and the union of our two input sets.
$$J(A,B) = \frac{A\cap{}B}{A\cup{}B}$$
To estimate a ratio of areas like this, one classic strategy is sampling. If we can generate random elements (uniformly in an appropriate sense), and we can query whether those elements are present in the two sides of the ratio, we can produce an empirical estimate which will approach the true value.
In this case, we know the union is at least as large as the intersection, so we want a uniformlyrandom sample from \(A\cup{}B\). It’s not clear how to do that given the sets themselves, but it turns out we can do so cheaply if we’re allowed precomputation on each set!
 First, we make the problem apparently more complicated. Assume that features are integers in some finite range \(0 \leq f_i \leq F\), and then pick a random permutation on \(\mathbb{Z}_F\). Call that permutation \(P(x)\). Now, we can select a random element by selecting the feature in our set that has the smallest value under this permutation^{1}:
$$ x_{\textrm{random}} \leftarrow{} \argmin_{x\in{}A\cup{}B}{P(x)} $$
 It’s not really feasible to work with trulyrandom permutations, but we can approximate one using a good hash function. This also avoids the need for features to be represented as fixedrange integers; by accepting a miniscule risk of collision and storing only the hash values, we can map any reasonable featurespace into fixedsize hash values:
$$ x_{\textrm{sig}} \leftarrow{} \min_{x\in{}A\cup{}B}{H(x)} $$
 Next, we’ll exploit the fact that
min
is associative, and rewrite the above to preprocess each set individually:
Let’s step back, and consider what we’ve achieved. If we pick a good hash function on features, we can compute a “signature” for each set, individually, consisting of the minimum hash value of all its features. Given any two sets, then, we can take the minimum of those signatures, and we have (the hash of) some element, drawn uniformlyatrandom from their union.
We want to know whether that element is present in the intersection, or whether it’s misssing from one side. But this construction also makes that trivial! We know that \(x_\textrm{sig}\) is the minimum hash value of any element in either set. Therefore, if it is present in, say, set \(A\), it must also be the minimum hash value in that set. But we know the minimum hash values of each set – that’s precisely what we have!
Thus, we don’t actually need to compute \(x_\textrm{sig}\); we can instead just ask whether \(a_\textrm{min} = b_\textrm{min}\)! For any two sets, this equality with hold with probability equal to \(J(A, B)\)!
Using more hash functions 🔗︎
That probability is taken over the universe of permutations of \(\mathbb{Z}_F\) (aka, with some caveats, “over our choice of hash function”). With a single hash function and a single minhash, we only have a boolean estimate for each pair – “equal” or “not equal.”
We can improve on that by instead selecting \(k\) different hash functions from some appropriate hash family, and summarizing each document into a \(k\)element vector:
$$ A_\textrm{sig} = \begin{pmatrix} \displaystyle\min_{x\in{}A}H_1(x) & \displaystyle\min_{x\in{}A}H_2(x) & \cdots{} & \displaystyle\min_{x\in{}A}H_k(x) \end{pmatrix} $$Given two of these signatures, we can approximate the Jaccard similarity by counting how many hashes match:
$$ J(A,B) \approx{} \frac{1}{k}\sum_{i=1}^{k} (A_\textrm{sig}[i] = B_\textrm{sig}[i]) $$
One caveat to mention: the choice of the hash family function here is a bit subtle. We are attempting to approximate a random permutation over the universe of features, but the number of such permutations grows extremely quickly, and so our hash family will represent a tiny fraction of all possible permutations. We need to be sure that members of our hash family are not inappropriately correlated – formally, the salient property here is referred to as “minwise independence”. Fortunately, this problem is reasonably wellstudied, and efficient solutions are available in the literature.
Comparing all documents 🔗︎
We’ve now condensed each document into a \(k\)element fingerprint of hash values, which allows efficient approximation of Jaccard similarities.
The next problem is to find approximate duplicates throughout our entire corpus – documents with a high similarity – without considering every pair of documents. As alluded to above, our strategy will be to define some set of keys that we can group documents by, and then only perform the full comparison within each group. We will aim to construct the grouping key so that similar documents group together with good probability, and dissimilar ones do not.
Using the full signature 🔗︎
The simplest choice is to simply to use all \(k\) MinHash values together as a grouping key, and consider two documents “approximate duplicates” iff all of their MinHash values match. I’m pretty sure is what the GPT3 paper cited above means when they say “we fuzzily deduplicated documents […] using Spark’s MinHashLSH implementation with 10 hashes.” They split each document into features, computed 10 MinHash values for each document (using 10 different hashes^{2}), and then grouped documents by that 10vector, and kept only one document per group.
The strongest virtue of this approach is its simplicity and efficiency. Grouping documents by a single highcardinality bytestring is an efficient operation and easy to scale horizontally, and is offered as a basic primitive in essentially any dataprocessing toolkit (it’s arguably the core primitive in MapReduce, taking the form of the “shuffle” between the map and reduce stages).
How does this approach behave? For a single pair of documents, we expect each MinHash value to be equal with probability \(J(A,B)\), so we expect all 10 to match with \(p=J(A,B)^k\). For \(k=10\), here’s what that looks like:
As well as some quantiles:
p(all match)  1%  10%  25%  50%  75%  90% 

Jaccard  0.63  0.79  0.87  0.93  0.97  0.99 
We can see that documents with similarities below 0.6 or so will almostnever collide, and that the odds of matches become large around 0.95 or so. If we’re primarily concerned about documents that are very close siblings, this approach may be sufficient. And in fact I suspect – but haven’t verified – that in many corpora, we will encounter fairly bimodal Jaccard values – two clusters, near 1 and 0. Unrelated documents have similarity close to 0, and similar documents will largely be “nearlyidentical” – e.g. two slight revisions of an article, or two copies of the same context with different timestamps or metadata.
It’s also worth noting that the \(J^{k}\) calculation holds for a single pair of documents. If we have many documents that are all similar, the pairwise probabilities are not at all independent. In practice, given many verysimilar documents, they’re likely to end up hashed into atmost two or three buckets, and so we will find “almost all” of the duplication, in some sense.
Going fuzzier 🔗︎
(Note: this discussion is primarily sourced from “Mining of Massive Datasets” section 3.4. I haven’t played with this strategy, and don’t know whether or when it’s used in practice, so far).
What if we want to detect “fuzzier” duplicates? Perhaps after some empirical study, we determine that we want to find pairs with similarities above 0.8 or 0.7, instead of only “near 1.”
By using a subset of our \(k\) MinHash hashes as a grouping key, we can increase the likelihood of collisions at lower similarity values, and then compare the full signatures within each bucket to weed out false collisions. For instance, we might group by the first 4 MinHash values, and then – within each colliding group – use all of our MinHash values to estimate the trule similariy.
Using fewer hashes is helpful, but only so far; \(J^r\) will always be smaller than \(J\), and if we push \(r\) too small, the rate of spurious matches will become unacceptable.
What we can do instead is to generate multiple keys per document, and place each document into several buckets, one per key, using a different subset of MinHashes for each key. If we compute \(k=20\) hashes as our signature, we might place each document into \(b=4\) different buckets, using \(r=5\) hashes to construct each key, and then compare each pair within each bucket.
What are the odds that two documents end up hashed together in at least one bucket?
 The odds that two documents collide using a single key is \(J^r\)
 So the odds they don’t collide based on that key is \(1J^r\)
 So the odds that don’t collide in any of the buckets is \(1J^r)^b\)
Thus, the odds that they collide atleastonce ends up as:
\[ p = 1  (1J^r)^b \]
For example, the above example – using 4 groups of 5 hashes – produces a probability curve like this:
The curve is not as steep as our earlier example, but we’ve successfully shifted it to the left – the odds of colliding become 50% at somewhere around \(J=0.7\).
It turns out, that for any choice of \(r\) and \(b\) greater than 1, the resulting curve is Sshaped in a broadly similar fashion, and so varying those values gives us a rich tradeoff space over sensitivity, recall, and performance costs.
Closing thoughts 🔗︎
Prior to working in AI, I believed myself to be relatively familiar with common algorithmic tricks, including most common sketch algorithms. But I had somehow never encountered MinHash, or even been aware that such algorithms (localitysensitive hashing) existed or were practical!
I really enjoyed learning how this trick works and digging in. I hope this blog post introduces it to some more engineers for the first time, and/or that it helps fill in the gaps in someone’s understanding. I just love neat mathematical/algorithmic tricks!
Postscript: MinHash and HyperLogLog 🔗︎
While researching and writing this post, I realized that the core MinHash trick reminded me a bit of a classic, somewhat famous, sketch: HyperLogLog.
The core idea in HyperLogLog (going back to a much older algorithm) is to hash each element of a stream, and store a running maximum of “the number of leading zeros” in the resulting stream of hashes.
The algorithm is very different in the details, but there’s a clear similarity: In both cases, we use a hash function to map input elements into a uniform distribution, and then we compute a running extremum, which – with appropriate calculation – allows us to estimate some distributional property using only a constantsize summary of our input.
Indeed, I contend the algorithms are even more similar than they first appear: HyperLogLog counts leading zeros (zeros in the leastsignificant digits). However, we’re assuming our hash function outputs uniform values in \([0, 2^N)\), and so we may as well reverse the bit order and count the number of zeros in the mostsignificant position. But given an Nbit number \(x\), the number of highbit zeros is closely related to \(\log_2(x)\) – the more leading zeros, the smaller \(x\). Thus, we can just as well think of think of HyperLogLog as computing a running minimum of \(log_2(H(x))\), as compared to MinHash’s \(H(x)\).
In addition, it turns out that there’s a sense in which HyperLogLog and MinHash are (somewhat) dual: Given two HyperLogLog structures for two different sets, we can combine them, and estimate the size of their union. Given two MinHash structures for those sets, we can compare them and estimate the (relative) size of their intersection.
Thus, if you combine both structures, you can produce a sketch that lets you ask questions about both intersections and unions of arbitrary sets! This idea was noticed at least by 2013, and it turns out there’s an ongoing literature of sketches that combine ideas from the two data structures in interesting ways. I think that’s neat!
Appendix: Representing documents as sets 🔗︎
I promised I’d return to the question of representing documents as sets, so I’ll also leave a brief note on two common approaches.
First, before applying either of these strategies, we may want to normalize documents in some way. For instance, we likely want to convert to a standard Unicode normalization form, and we may also wish to casefold, collapse runs of whitespace, or perform similar transformations.
ngrams aka “shingles” 🔗︎
We can represent a document as a set of all the ngrams that appear in the document, picking some appropriate value of n
. In the field of largescale text processing, often the literature uses the word “shingle” instead of “ngram,” but I find that needless confusing. We can pick any value of n
, with the primary tradeoff that smaller values will tend to compare documents more coarsely (e.g. most English text probably looks fairly similar through the lens of bigrams), and larger ones generating more distinct features and thus larger sets. At some limit I expect you also lose sensitivity, but I suspect performance problems arise earlier.
According to one source I’ve found^{3}, values of n between 5 and 9 appear to be common choices for a range of applications.
Wordsplitting 🔗︎
We can instead attempt to split the input into “words” or “tokens,” and use those as our features. The except from GPT3 paper above mentions “Spark’s standard tokenizer,” which I believe refers to this class, which simply lowercases the input and then splits on whitespace.
We could use a more sophisticated tokenizer, or we could hybridize the approaches by tokenizing and then using ngrams of tokens. In that case we would use a smaller value of n
, since individual tokens should much be higherentropy than bytes or characters.

If you’ve ever written
SELECT ... FROM table ORDER by random() LIMIT 1
, you’ve used a similar trick! ↩︎ 
If you’re curious, Spark documents its choice of hash family, along with the relevant literature reference. ↩︎

Mining of Massive Datasets §3.2.2 ↩︎