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https://issues.apache.org/jira/browse/LUCENE-9087?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16997217#comment-16997217
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Ignacio Vera commented on LUCENE-9087:
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We are currently using one bitset when writing the tree and it is only used to
count the number of documents we are storing in the tree which it can be
different to the number of points if you have multi-value documents.
I think we can detect that situation in the constructor (totalPointCount !=
maxDoc) and therefore we could easily spare that bitset for trees that only
contain single-value documents.
There used to be bitsets of size maxDoc for the handling of deleted documents
before using the radix partitioning, this is not needed any more.
> Should the BKD tree use a fixed maxPointsInLeafNode?
> -----------------------------------------------------
>
> Key: LUCENE-9087
> URL: https://issues.apache.org/jira/browse/LUCENE-9087
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Ignacio Vera
> Priority: Major
>
> Currently the BKD tree uses a fixed maxPointsInLeafNode provided in the
> constructor. For the current default codec the value is set to 1200. This is
> a good compromise between memory usage and performance of the BKD tree.
> Lowering this value can increase search performance but it has a penalty in
> memory usage. Now that the BKD tree can be load off-heap, this can be less of
> a concern. Note that lowering too much that value can hurt performance as
> well as the tree becomes too deep and benefits are gone.
> For data types that use the tree as an effective R-tree (ranges and shapes
> datatypes) the benefits are larger as it can minimise the overlap between
> leaf nodes.
> Finally, creating too many leaf nodes can be dangerous at write time as
> memory usage depends on the number of leaf nodes created. The writer creates
> a long array of length = numberOfLeafNodes.
> What I am wondering here is if we can improve this situation in order to
> create the most efficient tree? My current ideas are:
>
> * We can adapt the points per leaf depending on that number so we create a
> tree with the best depth and best points per leaf. Note that for the for 1D
> case we have an upper estimation of the number of points that the tree will
> be indexing.
> * Add a mechanism so field types can easily define their best points per
> leaf. In the case, field types like ranges or shapes can define its own value
> to minimise overlap.
> * Maybe the default is just too high now that we can load the tree off-heap.
>
> Any thoughts?
>
>
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