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https://issues.apache.org/jira/browse/SOLR-5894?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14105715#comment-14105715
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Toke Eskildsen commented on SOLR-5894:
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Forked Lucene/Solr on GitHub for better project management:
https://github.com/tokee/lucene-solr . Experimental branches for sparse
faceting are lucene_solr_4_8_sparse and lucene_solr_4_9_sparse and will contain
the latest code. The patch above for 4.7.1 is a little behind the GitHub
branches, as it does not have speed up for second phase (getting counts for
specific terms) of distributed faceting.
> Speed up high-cardinality facets with sparse counters
> -----------------------------------------------------
>
> Key: SOLR-5894
> URL: https://issues.apache.org/jira/browse/SOLR-5894
> Project: Solr
> Issue Type: Improvement
> Components: SearchComponents - other
> Affects Versions: 4.7.1
> Reporter: Toke Eskildsen
> Priority: Minor
> Labels: faceted-search, faceting, memory, performance
> Attachments: SOLR-5894.patch, SOLR-5894.patch, SOLR-5894.patch,
> SOLR-5894.patch, SOLR-5894.patch, SOLR-5894.patch, SOLR-5894.patch,
> SOLR-5894.patch, SOLR-5894.patch, SOLR-5894_test.zip, SOLR-5894_test.zip,
> SOLR-5894_test.zip, SOLR-5894_test.zip, SOLR-5894_test.zip,
> author_7M_tags_1852_logged_queries_warmed.png,
> sparse_2000000docs_fc_cutoff_20140403-145412.png,
> sparse_5000000docs_20140331-151918_multi.png,
> sparse_5000000docs_20140331-151918_single.png,
> sparse_50510000docs_20140328-152807.png
>
>
> Field based faceting in Solr has two phases: Collecting counts for tags in
> facets and extracting the requested tags.
> The execution time for the collecting phase is approximately linear to the
> number of hits and the number of references from hits to tags. This phase is
> not the focus here.
> The extraction time scales with the number of unique tags in the search
> result, but is also heavily influenced by the total number of unique tags in
> the facet as every counter, 0 or not, is visited by the extractor (at least
> for count order). For fields with millions of unique tag values this means
> 10s of milliseconds added to the minimum response time (see
> https://sbdevel.wordpress.com/2014/03/18/sparse-facet-counting-on-a-real-index/
> for a test on a corpus with 7M unique values in the facet).
> The extractor needs to visit every counter due to the current counter
> structure being a plain int-array of size #unique_tags. Switching to a sparse
> structure, where only the tag counters > 0 are visited, makes the extraction
> time linear to the number of unique tags in the result set.
> Unfortunately the number of unique tags in the result set is unknown at
> collect time, so it is not possible to reliably select sparse counting vs.
> full counting up front. Luckily there exists solutions for sparse sets that
> has the property of switching to non-sparse-mode without a switch-penalty,
> when the sparse-threshold is exceeded (see
> http://programmingpraxis.com/2012/03/09/sparse-sets/ for an example). This
> JIRA aims to implement this functionality in Solr.
> Current status: Sparse counting is implemented for field cache faceting, both
> single- and multi-value, with and without doc-values. Sort by count only. The
> patch applies cleanly to Solr 4.6.1 and should integrate well with everything
> as all functionality is unchanged. After patching, the following new
> parameters are possible:
> * facet.sparse=true enables sparse faceting.
> * facet.sparse.mintags=10000 the minimum amount of unique tags in the given
> field for sparse faceting to be active. This is used for auto-selecting
> whether sparse should be used or not.
> * facet.sparse.fraction=0.08 the overhead used for the sparse tracker.
> Setting this too low means that only very small result sets are handled as
> sparse. Setting this too high will result in a large performance penalty if
> the result set blows the sparse tracker. Values between 0.04 and 0.1 seems to
> work well.
> * facet.sparse.packed=true use PackecInts for counters instead of int[]. This
> saves memory, but performance will differ. Whether performance will be better
> or worse depends on the corpus. Experiment with it.
> * facet.sparse.cutoff=0.90 if the estimated number (based on hitcount) of
> unique tags in the search result exceeds this fraction of the sparse tracker,
> do not perform sparse tracking. The estimate is based on the assumption that
> references from documents to tags are distributed randomly.
> * facet.sparse.pool.size=2 the maximum amount of sparse trackers to clear and
> keep in memory, ready for usage. Clearing and re-using a counter is faster
> that allocating it fresh from the heap. Setting the pool size to 0 means than
> a new sparse counter will be allocated each time, just as standard Solr
> faceting works.
> * facet.sparse.stats=true adds a special tag with timing statistics for
> sparse faceting.
> * facet.sparse.stats.reset=true resets the timing statistics and clears the
> pool.
> The parameters needs to be given together with standard faceting parameters,
> such as facet=true&facet.field=myfield&facet.mincount=1&facet.sort=true. The
> defaults should be usable, so simply appending facet.sparse=true to the URL
> is a good start.
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