Simhash Near-Duplicate Detection
This library enables the efficient identification of near-duplicate documents using
simhash using a C++ extension.
simhash differs from most hashes in that its goal is to have two similar documents
produce similar hashes, where most hashes have the goal of producing very different
hashes even in the face of small changes to the input.
The input to
simhash is a list of hashes representative of a document. The output is an
unsigned 64-bit integer. The input list of hashes can be produced in several ways, but
one common mechanism is to:
- tokenize the document
- consider overlapping shingles of these tokens (
hashthese overlapping shingles
- input these hashes into
This has the effect of considering phrases in a document, rather than just a bag of the words in it.
Once we've produced a
simhash, we would like to compare it to other documents. For two
documents to be considered near-duplicates, they must have few bits that differ. We can
compare two documents:
import simhash a = simhash.compute(...) b = simhash.compute(...) simhash.num_differing_bits(a, b)
One of the key advantages of
simhash is that it does not require
O(n^2) time to find
all near-duplicate pairs from a set of hashes. Given a whole set of
simhashes, we can
find all pairs efficiently:
import simhash # The `simhash`-es from our documents hashes =  # Number of blocks to use (more in the next section) blocks = 4 # Number of bits that may differ in matching pairs distance = 3 matches = simhash.find_all(hashes, blocks, distance)
All the matches returned are guaranteed to be all pairs where the hashes differ by
distance bits or fewer. The
blocks parameter is less intuitive, but is best described
in this article or in
the paper. The best parameter to
choose depends on the distribution of the input simhashes, but it must always be at least
one greater than the provided
blocks C distance passes to complete. The idea is that as
that value increases (for instance by increasing
blocks), each pass completes faster.
In terms of memory,
O(hashes + matches) memory.
This is installable via
pip install git+https://github.com/seomoz/simhash-py.git
It can also be built from
git submodule update --init --recursive python setup.py install
pip install simhash-py
under osx, you should
export MACOSX_DEPLOYMENT_TARGET = 10.x (10.9,10.10...)
This is a rough benchmark, but should help to give you an idea of the order of
magnitude for the performance available. Running on a single core on a
on a 2015 MacBook Pro:
$ ./bench.py --random 1000000 --blocks 5 --bits 3 Generating 1000000 hashes Starting Find all Ran Find all in 1.595416s
Each document gets associated with a 64-bit hash calculated using a rolling hash function and simhash. This hash can be thought of as a fingerprint for the content. Two documents are considered near-duplicates if their hashes differ by at most k bits, a parameter chosen by the user.
In this context, there is a large corpus of known fingerprints, and we would like to determine all the fingerprints that differ by our query by k or fewer bits. To accomplish this, we divide up the 64 bits into at m blocks, where m is greater than k. If hashes A and B differ by at most k bits, then at least m - k groups are the same.
Choosing all the unique combinations of m - k blocks, we perform a permutation on each of the hashes for the documents so that those blocks are first in the hash. Perhaps a picture would illustrate it better:
63------53|52------42|41-----32|31------21|20------10|09------0| | A | B | C | D | E | F | If m = 6, k = 3, we'll choose permutations: - A B C D E F - A B D C E F - A B E C D F ... - C D F A B E - C E F A B D - D E F A B C
This generates a number of tables that can be put into sorted order, and then a small range of candidates can be found in each of those tables for a query, and then each candidate in that range can be compared to our query.
The corpus is represented by the union of these tables, could conceivably be hosted on a separate machine. And each of these tables is also amenable to sharding, where each shard would comprise a contiguous range of numbers. For example, you might divide a table into 256 shards, where each shard is associated with each of the possible first bytes.
The best partitioning remains to be seen, likely from experimentation, but the
basis of this is the
table tracks hashes inserted into it subject
to a permutation associated with the table. This permutation is described as a
vector of bitmasks of contiguous bit ranges, whose populations sum to 64.
Let's suppose that our corpus has a fingerprint:
and we have a query:
and they differ by only three bits which happen to fall in blocks B, D and E:
63------53|52------42|41-----32|31------21|20------10|09------0| | A | B | C | D | E | F | | | | | | | | 0000000000000000010000000000000000100000000000000001000000000000
Since any fingerprint matching the query differs by at most 3 bits, at most 3
blocks can differ, and at least 3 must match. Whatever table has the 3 blocks
that do not differ as the leading blocks will match the query when doing a scan.
In this case, the table that's permuted
A C F B D E will match. It's important
to note that it's possible for a query to match from more than one table. For
example, if two of the non-matching bits are in the same block, or the query
differs by fewer than 3 bits.
The only requirement of
simhash-py is that it has