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Tested on PostgreSQL 9.6 and 11

This repository contains two SP-GiST index extensions.

Principally, they detail my progress while implementing a [BK-Tree][1] as a native C PostgreSQL indexing extension. This is extremely useful for certain types of searches, primarily related to fuzzy-image searching.

The subdirectories are as follows:

  • vptree: This is a simple Viewpoint-Tree index using the SP-GiST indexing facilities. Originally, this was written by Alexander Korotkov (, though that implementation was for an earlier version of the SP_GiST API. The version in this repository has been updated for modern (9.6, 10) postgresql, and was the starting point for my own index implementation.

    Without this file, I wouldn't have been able to get anywhere, having a functional basis to work from was enormously beneficial, and I'm extremely grateful to Alexander (on the pgsql-hackers mailing list) for the starting point.

  • bktree: This is my index implementation. It implements a BK-tree index across a 64-bit p-hash data type. Basically, it results in a 65-ary tree, where the child nodes are distributed by the edit-distance from the intermediate node.

    This is (basically) a re-implementation of my pure-C++ BK-tree implementation that is located [here][2]. There are minor changes due to the requirements of the SP-GiST system (normally, this would be a 64-ary tree, but because SP-GiST inner tuples cannot store data, you need a additional branch for the matching case (e.g. edit distance of 0), whereas my C++ implementation can store the matching data directly in the inner tuple, so the matching case does not need an additional branch).

    As a side-benefit of having a robust and well-tested C++ implementation of a BK-tree, I can make stronger garantees about the correctness of the SP-GiST index implementation, simply by porting the tests for the C++ version to use the PG version. That was done, and the resulting tests are [here][3] (see all the Test_db_BKTree*.py files).

  • old: Initially, I wasn't sure if I needed a SP-GiST or a plain GiST index, so the contents of this directory were experimenting with plain GiST indexes. It didn't really go anywhere, but it's vaguely interesting. I may revisit the GiST implementation at some point.

Anyways, in benchmarking, the PostgreSQL implementation of a BK-tree is approximately 33% - 50% as fast as the C++ implementation, presumably due to the additional overhead of the PG tuple, SP-GiST and GiST mechanisms. While this is annoying, it's not a significant-enough performance loss to motivate me to continue using the C++ version, due to the significant implementation complexity of maintaining an out-of-database additional index. Adding more RAM to the PostgreSQL host may also help here. My test system has 32 GB of ram, and the C++ BK-Tree implementation alone requires ~18 GB to contain the entire dataset.

[1] :
[2] :
[3] :


This module has a simple makefile that uses pg_config to do it's magic. Check you have the pg_config shell command, and that it's output looks reasonable.

If you do, installing is a two steps:

cd bktree
sudo make install
sudo make installcheck   # to run tests that check everything installed correctly.

Note that installcheck currently fails on postgresql not 9.5, due to minor changes in the output of EXPLAIN ANALYZE. The extension works correctly, but the tests work by diffing the output of queries executed via psql, so minor changes in the output formatting can produce false breakages.

Once you have it installed:

# Enable extension in current database (Note: This is per-database, so if you want to use it on 
# multiple DBs, you'll have to enable it in each.

# Use the enabled extension to create an index. 
# phash_column MUST be a int64 ("bigint") type.
CREATE INDEX bk_index_name ON table_name USING spgist (phash_column bktree_ops);

# Query across the table within a specified edit distance.
SELECT <columns here> FROM table_name WHERE phash_column <@ (target_phash_int64, search_distance_int);

You'll need to replace things like bk_index_name, table_name, target_phash_int64, search_distance_int, and phash_column with appropriate values for your database.

phash_column must be a column of type bigint. Currently, only 64-bit phash values are supported, and they're stored in signed format.


Fast hamming-distance range searches via native GiST Indexing facility in PostgreSQL







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