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https://issues.apache.org/jira/browse/LUCENE-9937?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17355161#comment-17355161
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Michael Sokolov commented on LUCENE-9937:
-----------------------------------------
I ran some further tests using hnswlib and KnnGraphTester, trying to look more
closely at how they differ. The test setup was: index 1M vectors (I used the
enwiki GloVe-100 vectors from luceneutil) using M=16,
efConstruction/beam-width=500, and then run 10K queries retrieving top K=10 for
each. For the latency comparison table at the end, I set fanout for the Lucene
KNN = 5 since this yielded the same 0.778 recall as I calculated for hnswlib
with these settings. First I looked at some deeper per-search internal metrics:
"hop count" and "number of updates", as well as total number of nodes visited
(proxy for number of distance calculations). "hop count" says how many graph
arcs we traverse during the search; we compute distances for all the neighbors
of each visted node, but many of those neighbors get discarded, so we never
"hop" to them. "update count" says how many times we found a competitive node
and update the priority queue.
I hacked hnswlib so it would emit these metrics in a "debug" mode, by printing
them to stdout, and then calculated some summary statistics. These tables were
computed with beam-width=200, not 500, and fanout=0, and I did not compute
recall against true-NN, but used the score distribution as a proxy for that:
h3. hnswlib
{noformat}
max.score mean.score visited hops updates
Min. :0.6565 Min. :0.6364 Min. : 43.0 Min. :10.0 Min. :
17.00
1st Qu.:0.9680 1st Qu.:0.9585 1st Qu.:120.0 1st Qu.:11.0 1st Qu.:
31.00
Median :0.9871 Median :0.9841 Median :173.0 Median :12.0 Median :
38.00
Mean :0.9734 Mean :0.9667 Mean :175.7 Mean :12.7 Mean :
42.35
3rd Qu.:0.9954 3rd Qu.:0.9928 3rd Qu.:217.0 3rd Qu.:14.0 3rd Qu.:
47.00
Max. :0.9997 Max. :0.9996 Max. :463.0 Max. :33.0 Max.
:195.00 {noformat}
h3. lucene-knn
{noformat}
top.score mean.score visited hops updates
Min. :0.6995 Min. :0.6843 Min. : 82.0 Min. :12.00 Min. :
41.0
1st Qu.:0.9681 1st Qu.:0.9582 1st Qu.:181.0 1st Qu.:18.00 1st Qu.:
83.0
Median :0.9870 Median :0.9839 Median :205.0 Median :20.00 Median :
98.0
Mean :0.9731 Mean :0.9664 Mean :210.7 Mean :20.55 Mean
:100.3
3rd Qu.:0.9953 3rd Qu.:0.9928 3rd Qu.:237.0 3rd Qu.:23.00 3rd
Qu.:113.0
Max. :0.9997 Max. :0.9996 Max. :391.0 Max. :39.00 Max.
:232.0{noformat}
I think this shows the two implementations are performing similarly, in the
sense that they produce similar-scoring results for similar parameter settings
(see max score and mean score). Lucene KNN visits more nodes, but not that many
more. It has quite a few more hops and updates. I think this is accounted for
by the fact that we don't have heirarchical nodes. Also - in the hnswlib case I
did not include the traversal of the upper layers of the hierarchical graph -
this would add a few more visits, hops, and updates, although not as many as we
need to have since we start at random places in the graph, and it takes a few
more hops to converge on the nodes near to the target.
Removing the debug code, I ran a latency comparison, and in this test
||impl||recall||latency (ms)||warm count||
|hnswlib|0.778|0.032|n/a|
|lucene|0.778|0.54|cold|
|lucene|0.778|0.11|1000|
|lucene|0.778|0.08|10000|
Clearly there is a strong impact of warming; in KnnGraphTester warming is
implemented by pre-computing matches from the test set with the idea of paging
in the index (and the test vectors) as well as giving hotspot compiler a chance
to work its magic. This warming is kind of cheating since it is computing the
same results we compute in the test, but it does show the difficulty of
head-to-head latency comparisons of these different systems, and the
sensitivity of the Java/Lucene implementation to such effects. So I think we
need to take the ann-benchmarks results with a grain of salt; the test
framework there isn't really set up for such warming. Maybe we can tweak it to
do this somehow?
Another takeaway is that implementing the "H" in HNSW might actually be worth
it; it clearly saves some work in terms of updating the priority queue, and
doing traversals, and to some extent on the number of visited nodes as well.
> ann-benchmarks results for HNSW search
> --------------------------------------
>
> Key: LUCENE-9937
> URL: https://issues.apache.org/jira/browse/LUCENE-9937
> Project: Lucene - Core
> Issue Type: Task
> Reporter: Julie Tibshirani
> Priority: Minor
>
> I hooked up our HNSW implementation to
> [ann-benchmarks|https://github.com/erikbern/ann-benchmarks], a widely used
> repo for benchmarking nearest neighbor search libraries against large
> datasets. I found the results interesting and opened this issue to share and
> discuss. My benchmarking code can be found
> [here|https://github.com/jtibshirani/lucene/pull/1] – it's hacky and not easy
> to commit but I’m happy to help anyone get set up with it.
> Approaches
> * LuceneVectorsOnly: a baseline that only indexes vectors, and performs a
> brute force scan to determine nearest neighbors
> * LuceneHnsw: our HNSW implementation
> * [hnswlib|https://github.com/nmslib/hnswlib]: a C++ HNSW implementation
> from the author of the paper
> Datasets
> * sift-128-euclidean: 1 million SIFT feature vectors, dimension 128,
> comparing euclidean distance
> * glove-100-angular: ~1.2 million GloVe word vectors, dimension 100,
> comparing cosine similarity
> *Results on sift-128-euclidean*
> Parameters: M=16, efConstruction=500
> {code:java}
> Approach Recall QPS
> LuceneVectorsOnly() 1.000 6.764
> LuceneHnsw(n_cands=10) 0.603 7736.968
> LuceneHnsw(n_cands=50) 0.890 3605.000
> LuceneHnsw(n_cands=100) 0.953 2237.429
> LuceneHnsw(n_cands=500) 0.996 570.900
> LuceneHnsw(n_cands=800) 0.998 379.589
> hnswlib(n_cands=10) 0.713 69662.756
> hnswlib(n_cands=50) 0.950 28021.582
> hnswlib(n_cands=100) 0.985 16108.538
> hnswlib(n_cands=500) 1.000 4115.886
> hnswlib(n_cands=800) 1.000 2729.680
> {code}
> *Results on glove-100-angular*
> Parameters: M=32, efConstruction=500
> {code:java}
> Approach Recall QPS
> LuceneVectorsOnly() 1.000 6.764
> LuceneHnsw(n_cands=10) 0.507 5036.236
> LuceneHnsw(n_cands=50) 0.760 2099.850
> LuceneHnsw(n_cands=100) 0.833 1233.690
> LuceneHnsw(n_cands=500) 0.941 309.077
> LuceneHnsw(n_cands=800) 0.961 203.782
> hnswlib(n_cands=10) 0.597 43543.345
> hnswlib(n_cands=50) 0.832 14719.165
> hnswlib(n_cands=100) 0.897 8299.948
> hnswlib(n_cands=500) 0.981 1931.985
> hnswlib(n_cands=800) 0.991 881.752
> {code}
> Notes on benchmark:
> * By default, the ann-benchmarks repo retrieves 10 nearest neighbors and
> computes the recall against the true neighbors. The recall calculation has a
> small 'fudge factor' that allows neighbors that are within a small epsilon of
> the best distance. Queries are executed serially to obtain the QPS.
> * I chose parameters where hnswlib performed well, then passed these same
> parameters to Lucene HNSW. For index-time parameters, I set maxConn as M and
> beamWidth as efConstruction. For search parameters, I set k to k, and fanout
> as (num_cands - k) so that the beam search is of size num_cands. Note that
> our default value for beamWidth is 16, which is really low – I wasn’t able to
> obtain acceptable recall until I bumped it to closer to 500 to match the
> hnswlib default.
> * I force-merged to one segment before running searches since this gives the
> best recall + QPS, and also to match hnswlib.
> Some observations:
> * It'd be really nice to extend luceneutil to measure vector search recall
> in addition to latency. That would help ensure we’re benchmarking a more
> realistic scenario, instead of accidentally indexing/ searching at a very low
> recall. Tracking recall would also guard against subtle, unintentional
> changes to the algorithm. It's easy to make an accidental change while
> refactoring, and with approximate algorithms, unit tests don't guard as well
> against this.
> * Lucene HNSW gives a great speed-up over the baseline without sacrificing
> too much recall. But it doesn't perform as well as hnswlib in terms of both
> recall and QPS. We wouldn’t expect the results to line up perfectly, since
> Lucene doesn't actually implement HNSW – the current algorithm isn't actually
> hierarchical and only uses a single graph layer. Does this difference might
> indicate we're leaving performance 'on the table' by not using layers, which
> (I don't think) adds much index time or space? Or are there other algorithmic
> improvements would help close the gap?
> * Setting beamWidth to 500 *really* slowed down indexing. I'll open a
> separate issue with indexing speed results, keeping this one focused on
> search.
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