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Simhash Near-Duplicate Detection

This library enables the identification of all fingerprints that are nearly identical to a query fingerprint. In this context, a fingerprint is an unsigned 64-bit integer.

It also comes with an auxillary function designed to generate a fingerprint given a char* and a length. This fingeprint is generated with a tokenizer and a hash function (both of which may be provided as template paramters). Using a cyclic hash function, it then performs simhash on a moving window of tokens (as defined by the tokenizer).

The default hash function is jenkins hash and the default tokenizer splits on non-alpha characters.

Usage

Library

The library provides two utilities for finding simhashes:

• Simhash::find_all finds all matching pairs of simhashes
• Simhash::find_clusters finds clusters of matching simhashes (see #clustering)

Binaries

This also provides two binaries to facilitate use from other languages. They both read hashes as newline-separated decimal strings, and print out newline-separated JSON arrays.

• simhash-find-all writes all matching pairs as arrays with two elements
• simhash-find-clusters writes all clusters as arrays of simhashes

Both have the following common arguments:

• --input names a file from which to read (defaults to -, meaning stdin)
• --output names a file to which to write (defaults to -, meaning stdout)
• --blocks sets the number of blocks to use for simhash matching
• --distance sets the maximum bit distance for considering matches

Architecture

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.

Clustering

The current clustering implementation considers the clusters to be the connected components of the connectivity graph. In other words:

For a simhash to be a member of a cluster, it must be a match with at least one
member of that cluster.

This does mean that a cluster may have pairs of members that aren't matches. For examples, (A, B, C, D) might be a cluster where A matches B, which matches C, which matches D, but A and D are too far apart to be a match.