Class DocValuesTermsQuery

  • All Implemented Interfaces:

    public class DocValuesTermsQuery
    extends Query
    implements Accountable
    A Query that only accepts documents whose term value in the specified field is contained in the provided set of allowed terms.

    This is the same functionality as TermsQuery (from queries/), but because of drastically different implementations, they also have different performance characteristics, as described below.

    NOTE: be very careful using this query: it is typically much slower than using TermsQuery, but in certain specialized cases may be faster.

    With each search, this query translates the specified set of Terms into a private LongBitSet keyed by term number per unique IndexReader (normally one reader per segment). Then, during matching, the term number for each docID is retrieved from the cache and then checked for inclusion using the LongBitSet. Since all testing is done using RAM resident data structures, performance should be very fast, most likely fast enough to not require further caching of the DocIdSet for each possible combination of terms. However, because docIDs are simply scanned linearly, an index with a great many small documents may find this linear scan too costly.

    In contrast, TermsQuery builds up an FixedBitSet, keyed by docID, every time it's created, by enumerating through all matching docs using PostingsEnum to seek and scan through each term's docID list. While there is no linear scan of all docIDs, besides the allocation of the underlying array in the FixedBitSet, this approach requires a number of "disk seeks" in proportion to the number of terms, which can be exceptionally costly when there are cache misses in the OS's IO cache.

    Generally, this filter will be slower on the first invocation for a given field, but subsequent invocations, even if you change the allowed set of Terms, should be faster than TermsQuery, especially as the number of Terms being matched increases. If you are matching only a very small number of terms, and those terms in turn match a very small number of documents, TermsQuery may perform faster.

    Which query is best is very application dependent.

    WARNING: This API is experimental and might change in incompatible ways in the next release.