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Simhash and near-duplicate detection
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README.md

Simhash Near-Duplicate Detection

This library enables the identification of near-duplicate documents. In this context, a document is simply a bytestring -- be it the content of a webpage or an essay or a text file.

It contains a C++-level extension designed to speed up queries, as well as facilities to distribute the lookup tables. This implementation follows that described in the Google paper on the subject of near-duplicate detection with simhash.

Building

This library links against libJudy, which must be installed before building this. It also depends on Cython. With those pieces in place, it's business as usual

python setup.py install

Usage

A Corpus is a collection of all the tables necessary to perform the query efficiently. There are two parameters, num_blocks and diff_bits which describe the number of blocks into which the 64-bit hashes should be divided (see more about this below) and the number of bits by which two hashes may differ before being considered near-duplicates. The number of tables needed is a function of these two parameters.

import simhash

# 6 blocks, 3 bits may differ
corpus = simhash.Corpus(6, 3)

With a corpus, you can then insert, remove and query the data structure. You may be interested in just any near-duplicate fingerprint in which case you can use find_first or find_first_bulk. If you're interested in finding all matches then you should use find_all or find_all_bulk:

# Generate 1M random hashes and random queries
import random
hashes  = [random.randint(0, 1 << 64) for i in range(1000000)]
queries = [random.randint(0, 1 << 64) for i in range(1000000)]

# Insert the hashes
corpus.insert_bulk(hashes)

# Find matches; returns a list of results, each element contains the match
# for the corresponding element in the query
matches = corpus.find_first_bulk(queries)

# Find more matches; returns a list of lists, each of which corresponds to 
# the query of the same index
matches = corpus.find_all_bulk(queries)

Benchmark

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 2011-ish MacBook Pro:

# ./bench.py --random 1000000 --blocks 5 --bits 3
Generating 1000000 hashes
Generating 1000000 queries
Starting Bulk Insertion
     Ran Bulk Insertion in 2.534197s
Starting Bulk Find First
     Ran Bulk Find First in 4.795310s
Starting Bulk Find All
     Ran Bulk Find All in 7.415205s
Starting Bulk Removal
     Ran Bulk Removal in 3.346022s

Architecture

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. 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.

Example

Let's suppose that our corpus has a fingerprint:

0100101110111011001000101111101110111100001010011101100110110101

and we have a query:

0100101110111011011000101111101110011100001010011100100110110101

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.

Building

Pretty simple, actually. The one wrinkle is to install libjudy first, see:

http://judy.sourceforge.net/

After that, it's only a matter of running the normal:

sudo python setup.py install
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