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https://issues.apache.org/jira/browse/LUCENE-7253?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15268847#comment-15268847
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Shai Erera commented on LUCENE-7253:
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I thought so, but that still needs to be benchmarked. Maybe [~prog] has an idea
of an implementation that will keep both efficient? Maybe read time isn't
affected, but merge is? Maybe if we choose to sparsely encode fields that are
very sparse, the read time isn't affected? As a first step that can work. Point
is we shouldn't shoot down an idea before we have code/results to back the
shooting.
And I agree that if we had iterator-like API it would make a stronger case for
sparse DV. Maybe though both need not be coupled and one can be done before the
other.
> Sparse data in doc values and segments merging
> -----------------------------------------------
>
> Key: LUCENE-7253
> URL: https://issues.apache.org/jira/browse/LUCENE-7253
> Project: Lucene - Core
> Issue Type: Improvement
> Affects Versions: 5.5, 6.0
> Reporter: Pawel Rog
> Labels: performance
>
> Doc Values were optimized recently to efficiently store sparse data.
> Unfortunately there is still big problem with Doc Values merges for sparse
> fields. When we imagine 1 billion documents index it seems it doesn't matter
> if all documents have value for this field or there is only 1 document with
> value. Segment merge time is the same for both cases. In most cases this is
> not a problem but there are several cases in which one can expect having many
> fields with sparse doc values.
> I can describe an example. During performance tests of a system with large
> number of sparse fields I realized that Doc Values merges are a bottleneck. I
> had hundreds of different numeric fields. Each document contained only small
> subset of all fields. Average document contains 5-7 different numeric values.
> As you can see data was very sparse in these fields. It turned out that
> ingestion process was CPU-bound. Most of CPU time was spent in DocValues
> related methods (SingletonSortedNumericDocValues#setDocument,
> DocValuesConsumer$10$1#next, DocValuesConsumer#isSingleValued,
> DocValuesConsumer$4$1#setNext, ...) - mostly during merging segments.
> Adrien Grand suggested to reduce the number of sparse fields and replace them
> with smaller number of denser fields. This helped a lot but complicated
> fields naming.
> I am not much familiar with Doc Values source code but I have small
> suggestion how to improve Doc Values merges for sparse fields. I realized
> that Doc Values producers and consumers use Iterators. Let's take an example
> of numeric Doc Values. Would it be possible to replace Iterator which
> "travels" through all documents with Iterator over collection of non empty
> values? Of course this would require storing object (instead of numeric)
> which contains value and document ID. Such an iterator could significantly
> improve merge time of sparse Doc Values fields. IMHO this won't cause big
> overhead for dense structures but it can be game changer for sparse
> structures.
> This is what happens in NumericDocValuesWriter on flush
> {code}
> dvConsumer.addNumericField(fieldInfo,
> new Iterable<Number>() {
> @Override
> public Iterator<Number> iterator() {
> return new NumericIterator(maxDoc, values,
> docsWithField);
> }
> });
> {code}
> Before this happens during addValue, this loop is executed to fill holes.
> {code}
> // Fill in any holes:
> for (int i = (int)pending.size(); i < docID; ++i) {
> pending.add(MISSING);
> }
> {code}
> It turns out that variable called pending is used only internally in
> NumericDocValuesWriter. I know pending is PackedLongValues and it wouldn't be
> good to change it with different class (some kind of list) because this may
> break DV performance for dense fields. I hope someone can suggest interesting
> solutions for this problem :).
> It would be great if discussion about sparse Doc Values merge performance can
> start here.
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