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https://issues.apache.org/jira/browse/LUCENE-9136?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17052727#comment-17052727
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Xin-Chun Zhang commented on LUCENE-9136:
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Hi, [~jtibshirani], thanks for you excellent work!
??I was thinking we could actually reuse the existing `PostingsFormat` and
`DocValuesFormat` implementations.??
Yes, the codes could be simple by reusing these formats. But I agree with
[~tomoko] that ANN search is a pretty new feature to Lucene, it's better to use
a dedicated format for maintaining reasons. Moreover, If we are going to use a
dedicated vector format for HNSW, this could also applied to IVFFlat because
IVFFlat and HNSW are used for the same purpose of ANN search. It may be strange
to users if IVFFlat and HNSW perform completely different.
??In particular, it doesn’t require random access for doc values, they are only
accessed through forward iteration.??
Actually, we need random access to the vector values! For a typical search
engine, we are going to retrieving the best matched documents after obtaining
the TopK docIDs. Retrieving vectors via these docIDs requires random access to
the vector values.
> 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
> Attachments: 1581409981369-9dea4099-4e41-4431-8f45-a3bb8cac46c0.png,
> image-2020-02-16-15-05-02-451.png
>
> Time Spent: 50m
> Remaining Estimate: 0h
>
> 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 based 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.
> IVFFlat is better for high-precision applications such as face recognition,
> while HNSW performs better in general scenarios including recommendation and
> personalized advertisement. *The recall ratio of IVFFlat could be gradually
> increased by adjusting the query parameter (nprobe), while it's hard for HNSW
> to improve its accuracy*. In theory, IVFFlat could achieve 100% recall ratio.
> 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.
> The latest branch is
> [*lucene-9136-ann-ivfflat*]([https://github.com/irvingzhang/lucene-solr/commits/jira/lucene-9136-ann-ivfflat)|https://github.com/irvingzhang/lucene-solr/commits/jira/lucene-9136-ann-ivfflat]
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