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https://issues.apache.org/jira/browse/LUCENE-9136?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17019507#comment-17019507
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Xin-Chun Zhang commented on LUCENE-9136:
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I worked on this issue for about three to four days. And it now works fine for
searching. My [personal
branch|[https://github.com/irvingzhang/lucene-solr/tree/jira/LUCENE-9136]] is
here. The clustering process was optimized when the number of vectors is large
(e.g. > 40,000 per segment). The query performance of IVFFlat seem slightly
better than HNSW. The insert performance of IVFFlat is also better than HNSW
due to it has no extra executions while HNSW need to maintain the graph.
However, IVFFlat consumes more time in flushing due to the k-means clustering.
The designed format of IVFFlat index is presented in the class
Lucene90IvfFlatIndexFormat. My test class for IVFFlat is under the directory of
[https://github.com/irvingzhang/lucene-solr/blob/jira/LUCENE-9136/lucene/core/src/test/org/apache/lucene/util/ivfflat/|https://github.com/irvingzhang/lucene-solr/blob/jira/LUCENE-9136/lucene/core/src/test/org/apache/lucene/util/ivfflat/TestKnnIvfFlat.java].
Performance comparison between IVFFlat and HNSW is in the class
TestKnnGraphAndIvfFlat.
The work is still in its early stage. The performance could be further
optimized. Now it has some codes that are similar to HNSW, which could be
refactored. Moreover, there must some bugs need to be fixed and and would like
to hear more comments.
> Introduce IVFFlat to Lucene for ANN similarity search
> -----------------------------------------------------
>
> Key: LUCENE-9136
> URL: https://issues.apache.org/jira/browse/LUCENE-9136
> Project: Lucene - Core
> Issue Type: New Feature
> Reporter: Xin-Chun Zhang
> Priority: Major
>
> Representation learning (RL) has been an established discipline in the
> machine learning space for decades but it draws tremendous attention lately
> with the emergence of deep learning. The central problem of RL is to
> determine an optimal representation of the input data. By embedding the data
> into a high dimensional vector, the vector retrieval (VR) method is then
> applied to search the relevant items.
> With the rapid development of RL over the past few years, the technique has
> been used extensively in industry from online advertising to computer vision
> and speech recognition. There exist many open source implementations of VR
> algorithms, such as Facebook's FAISS and Microsoft's SPTAG, providing various
> choices for potential users. However, the aforementioned implementations are
> all written in C++, and no plan for supporting Java interface, making it hard
> to be integrated in Java projects or those who are not familier with C/C++
> [[https://github.com/facebookresearch/faiss/issues/105]].
> The algorithms for vector retrieval can be roughly classified into four
> categories,
> # Tree-base algorithms, such as KD-tree;
> # Hashing methods, such as LSH (Local Sensitive Hashing);
> # Product quantization algorithms, such as IVFFlat;
> # Graph-base algorithms, such as HNSW, SSG, NSG;
> where IVFFlat and HNSW are the most popular ones among all the VR algorithms.
> Recently, the implementation of HNSW (Hierarchical Navigable Small World,
> LUCENE-9004) for Lucene, has made great progress. The issue draws attention
> of those who are interested in Lucene or hope to use HNSW with Solr/Lucene.
> As an alternative for solving ANN similarity search problems, IVFFlat is also
> very popular with many users and supporters. Compared with HNSW, IVFFlat has
> smaller index size but requires k-means clustering, while HNSW is faster in
> query (no training required) but requires extra storage for saving graphs
> [indexing 1M
> vectors|[https://github.com/facebookresearch/faiss/wiki/Indexing-1M-vectors]].
> Another advantage is that IVFFlat can be faster and more accurate when
> enables GPU parallel computing (current not support in Java). Both algorithms
> have their merits and demerits. Since HNSW is now under development, it may
> be better to provide both implementations (HNSW && IVFFlat) for potential
> users who are faced with very different scenarios and want to more choices.
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