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Approximate Nearest Neighbor Search with Vector Similarity Indexes [experimental]

Nearest neighborhood search is the problem of finding the M closest vectors to a given vector in an N-dimensional vector space. The most straightforward approach to solve this problem is an exhaustive (brute-force) search which computes the distance between the reference vector and all other points in the vector space. While method guarantees a perfectly accurate result, but it is usually too slow for practical applications. As an alternative, approximative algorithms use greedy heuristics to find the M closest vectors much faster. This allows to semantic search of picture, song, text embeddings in milliseconds.

Blogs:

In terms of SQL, a nearest neighborhood search can be expressed as follows:

SELECT [...]
FROM table, [...]
ORDER BY DistanceFunction(vectors, reference_vector)
LIMIT N

where

The query returns the N closest points in vectors to reference_vector.

Exhaustive search computes the distance between reference_vector and all vectors in vectors. As such, its runtime is linear in the number of stored vectors. Approximate search relies on special data structures (e.g. graphs, random forests, etc.) which allow to find the closest vectors to a given reference vector quickly (i.e. in sub-linear time). ClickHouse provides such a data structure in the form of "vector similarity indexes", a type of skipping index.

Creating and Using Vector Similarity Indexes

Syntax to create a vector similarity index

CREATE TABLE table
(
  id Int64,
  vectors Array(Float32),
  INDEX index_name vectors TYPE vector_similarity(method, distance_function[, quantization, hnsw_max_connections_per_layer, hnsw_candidate_list_size_for_construction]) [GRANULARITY N]
)
ENGINE = MergeTree
ORDER BY id;
Note

USearch indexes are currently experimental, to use them you first need to SET allow_experimental_vector_similarity_index = 1.

The index can be build on a column of type Array(Float64), Array(Float32), or Array(BFloat16).

Index parameters:

  • method: Currently only hnsw is supported.
  • distance_function: either L2Distance (the Euclidean distance: the length of a line between two points in Euclidean space), or cosineDistance (the cosine distance: the angle between two non-zero vectors).
  • quantization: either f64, f32, f16, bf16, or i8 for storing vectors with reduced precision (optional, default: bf16)
  • hnsw_max_connections_per_layer: the number of neighbors per HNSW graph node, also known as M in the HNSW paper. Optional, default: 32. Value 0 means using the default value.
  • hnsw_candidate_list_size_for_construction: the size of the dynamic candidate list when constructing the HNSW graph, also known as ef_construction in the original HNSW paper. Optional, default: 128. Value 0 means using the default value.

For normalized data, L2Distance is usually the best choice, otherwise cosineDistance is recommended to compensate for scale.

Example:

CREATE TABLE table
(
  id Int64,
  vectors Array(Float32),
  INDEX idx vectors TYPE vector_similarity('hnsw', 'L2Distance') -- Alternative syntax: TYPE vector_similarity(hnsw, L2Distance)
)
ENGINE = MergeTree
ORDER BY id;

All arrays must have same length. To avoid errors, you can use a CONSTRAINT, for example, CONSTRAINT constraint_name_1 CHECK length(vectors) = 256. Empty Arrays and unspecified Array values in INSERT statements (i.e. default values) are not supported as well.

Vector similarity indexes are based on the USearch library, which implements the HNSW algorithm, i.e., a hierarchical graph where each node represents a vector and the edges between nodes represent similarity. Such hierarchical structures can be very efficient on large collections. They may often fetch 0.05% or less data from the overall dataset, while still providing 99% recall. This is especially useful when working with high-dimensional vectors which are expensive to load and compare. USearch also utilizes SIMD to accelerate distance computations on modern x86 (AVX2 and AVX-512) and ARM (NEON and SVE) CPUs.

Vector similarity indexes are built during column insertion and merge. The HNSW algorithm is known to provide slow inserts. As a result, INSERT and OPTIMIZE statements on tables with vector similarity index will be slower than for ordinary tables. Vector similarity indexes are ideally used only with immutable or rarely changed data, respectively when are far more read requests than write requests. Three additional techniques are recommended to speed up index creation:

  • Index creation can be parallelized. The maximum number of threads can be configured using server setting max_build_vector_similarity_index_thread_pool_size.
  • Index creation on newly inserted parts may be disabled using setting materialize_skip_indexes_on_insert. Search on such parts will fall back to exact search but as inserted parts are typically small compared to the total table size, the performance impact is negligible.
  • As parts are incrementally merged into bigger parts, and these new parts are merged into even bigger parts ("write amplification"), vector similarity indexes are possibly build multiple times for the same vectors. To avoid that, you may suppress merges during insert using statement SYSTEM STOP MERGES, respectively start merges once all data has been inserted using SYSTEM START MERGES.

Vector similarity indexes support this type of query:

WITH [...] AS reference_vector
SELECT *
FROM table
WHERE ...                       -- WHERE clause is optional
ORDER BY Distance(vectors, reference_vector)
LIMIT N

To search using a different value of HNSW parameter hnsw_candidate_list_size_for_search (default: 256), also known as ef_search in the original HNSW paper, run the SELECT query with SETTINGS hnsw_candidate_list_size_for_search = <value>.

Restrictions: Approximate vector search algorithms require a limit, hence queries without LIMIT clause cannot utilize vector similarity indexes. The limit must also be smaller than setting max_limit_for_ann_queries (default: 100).

Differences to Regular Skip Indexes Similar to regular skip indexes, vector similarity indexes are constructed over granules and each indexed block consists of GRANULARITY = [N]-many granules ([N] = 1 by default for normal skip indexes). For example, if the primary index granularity of the table is 8192 (setting index_granularity = 8192) and GRANULARITY = 2, then each indexed block will contain 16384 rows. However, data structures and algorithms for approximate neighborhood search are inherently row-oriented. They store a compact representation of a set of rows and also return rows for vector search queries. This causes some rather unintuitive differences in the way vector vector similarity indexes behave compared to normal skip indexes.

When a user defines an vector similarity index on a column, ClickHouse internally creates an vector similarity "sub-index" for each index block. The sub-index is "local" in the sense that it only knows about the rows of its containing index block. In the previous example and assuming that a column has 65536 rows, we obtain four index blocks (spanning eight granules) and an vector similarity sub-index for each index block. A sub-index is theoretically able to return the rows with the N closest points within its index block directly. However, since ClickHouse loads data from disk to memory at the granularity of granules, sub-indexes extrapolate matching rows to granule granularity. This is different from regular skip indexes which skip data at the granularity of index blocks.

The GRANULARITY parameter determines how many vector similarity sub-indexes are created. Bigger GRANULARITY values mean fewer but larger vector similarity sub-indexes, up to the point where a column (or a column's data part) has only a single sub-index. In that case, the sub-index has a "global" view of all column rows and can directly return all granules of the column (part) with relevant rows (there are at most LIMIT [N]-many such granules). In a second step, ClickHouse will load these granules and identify the actually best rows by performing a brute-force distance calculation over all rows of the granules. With a small GRANULARITY value, each of the sub-indexes returns up to LIMIT N-many granules. As a result, more granules need to be loaded and post-filtered. Note that the search accuracy is with both cases equally good, only the processing performance differs. It is generally recommended to use a large GRANULARITY for vector similarity indexes and fall back to a smaller GRANULARITY values only in case of problems like excessive memory consumption of the vector similarity structures. If no GRANULARITY was specified for vector similarity indexes, the default value is 100 million.