MARKLOGIC SERVER

MarkLogic then sorts the document IDs based on relevance score, calculated using
term frequency data held in the term lists and term frequency statistics. This produces a
score-sorted set of candidate document IDs.
At this point MarkLogic starts iterating over the sorted document IDs. Because this is a
filtered search, MarkLogic opens the highest-scoring document and filters it by looking
inside it to make sure it's a match. If it's not a match, it gets skipped. If it is, it continues
on to the return clause. 1
As each document makes it to the return clause, MarkLogic extracts a few nodes from
the document and constructs a new HTML node in memory for the result sequence.
After 10 hits (because of the [1 to 10] predicate), the search expression finishes and
MarkLogic stops iterating.
Now, what if all indexes aren't enabled? As always, MarkLogic does the best it can
with the indexes you've enabled. For example, if you don't have any positional indexes,
MarkLogic applies the positional constraints during the filter phase. If you don't have
fast element phrase searches to help with the phrase match, MarkLogic
uses fast element word searches and element word positions instead. If you don't have those, it
keeps falling back to other indexes like fast phrase searches. The less specific the indexes,
the more candidate documents to consider and the more documents that might have to
be discarded during the filter phase. The end result after filtering, however, is always
the same.
INDEXING DOCUMENT METADATA
By now you should have a clear understanding of how MarkLogic uses term lists for
indexing both text and structure. MarkLogic doesn't stop there; it turns out that term
lists are useful for indexing many other things such as collections, directories, and
security rules. That's why it's named the Universal Index.
COLLECTION INDEXES
Collections in MarkLogic are a way to tag documents as belonging to a named group (a
taxonomic designation, origin, whether it's draft or published, etc.) without modifying
the document itself. Each document can be tagged as belonging to any number of
collections. A query constraint can limit the scope of a search to a certain collection or
set of collections.
Collection constraints are implemented internally as just another term list to be
intersected. There's a term list for each collection tracking the documents within that
collection. If you limit a query to a collection, it intersects that collection's term list with
the other constraints in the query. If you limit a query to two collections, the two term
lists are unioned into one before being intersected.
1 Previously read term lists and documents get cached for efficiency, a topic discussed later.
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