Found with "versus" test (TestScorchVersusUpsideDownBoltSmallMNSAM),
which had a boolean query with a MustNot that was the same as the Must
parameters. This replicates a situation found by
Aruna/Mihir/testrunner/RQG (MB-27291). Example:
"query": {
"must_not": {"disjuncts": [
{"field": "body", "match": "hello"}
]},
"must": {"conjuncts": [
{"field": "body", "match": "hello"}
]}
}
The nested searchers along the MustNot pathway would end up looking
roughly like...
booleanSearcher
MustNot
=> disjunctionSearcher
=> disjunctionSearcher
=> termSearcher
On the first Next() call by the collector, the two disjunction
searchers would run through their respective Next() method processing,
which includes their initSearcher() processing on the first time.
This has the effect of driving the leaf termSearcher through two
Next() invocations.
That is, if there were 3 docs (doc-1, doc-2, doc-3), the leaf
termSearcher would at this point have moved to point to doc-3, while
the topmost MustNot would have received doc-1.
Next, the booleanSearcher's Must searcher would produce doc-2, so the
booleanSearcher would try to Advance() the MustNot searcher to doc-2.
But, in scorch, the leafmost termSearcher had already gotten past
doc-2 and would return its doc-3.
In upsidedown, in contrast, the leaf termSearcher would then drive the
KVStore iterator with a Seek(doc-2), and the KVStore iterator would
perform a backwards seek to reach doc-2.
In scorch, however, backwards iteration seeking isn't supported.
So, this fix checks the state of the disjunction searcher to see if we
already have the necessary state so that we don't have to perform
actual Advance()'es on the underlying searchers. This not only fixes
the behavior w.r.t. scorch, but also can have an effect of potentially
making upsidedown slightly faster as we're avoiding some backwards
KVStore iterator seeks.
This is somewhat like a simple, unit-test'ish version of testrunner's
random query generator, where this does not have a dependency on an
external elasticsearch server, and instead depends on functional
correctness when comparing to upsidedown/bolt.
The term range query is not often used in full-text queries, but
can be useful when filtering on keyword indexed text terms in
the index.
The JSON syntax to do a TermRange query is the same as for
NumericRange, but the min/max values must be string and not
float64.
- make geo queries accessible from top-level bleve
- update query parsing to support same geo point formats as
document parsing
- add constructor for easier sorting by geo distance in Go
- additional integration tests using alternate (GeoJSON) style points
when parsing json, when we encounter the key "terms", we first
try to parse as traditional phrase query, then if that fails,
we also try parsing it as multi-phrase
This is a change in search result behavior in that location
information is no longer provided by default with search results.
Although this looks like a wide-ranging change, it's mostly a
mechanical replacement of the explain bool flag with a new
search.SearcherOptions struct, which holds both the Explain bool flag
and the IncludeTermVectors bool flag.
the index mapping contains some relatively messy logic
and the top-level bleve package only cares about a relatively
small portion of this
the motivation for this change is to codify the part that the
top-level bleve package cares about into an interface
then move all the details into its own package
NOTE: the top-level bleve package still has hard dependency on
the actual implementation (for now) because it must deserialize
mappings from JSON and simply assumes it is this one instance.
this is seen as OK for now, and this issue could be revisited
in a future change. moving the logic into a separate package
is seen as a simplification of top-level bleve, even though
we still depend on the one particular implementation.
fixes#378
this bug was introduced by:
f2aba116c4
theory of operation for this collector (top N, skip K)
- collect the highest scoring N+K results
- if K > 0, skip K and return the next N
internal details
- the top N+K are kept in a list
- the list is ordered from lowest scoring (first) to highest scoring (last)
- as a hit comes in, we find where this new hit would fit into this list
- if this caused the list to get too big, trim off the head (lowest scoring hit)
theory of the optimization
- we were not tracking the lowest score in the list
- so if the score was lower than the lowest score, we would add/remove it
- by keeping track of the lowest score in the list, we can avoid these ops
problem with the optimization
- the optimization worked by returning early
- by returning early there was a subtle change to documents which had the same score
- the reason is that which docs end up in the top N+K changed by returning early
- why was that? docs are coming in, in order by key ascending
- when finding the correct position to insert a hit into the list, we checked <, not <= the score
- this has the subtle effect that docs with the same score end up in reverse order
for example consider the following in progress list:
doc ids [ c a b ]
scores [ 1 5 9 ]
if we now see doc d with score 5, we get:
doc ids [ c a d b ]
scores [ 1 5 5 9 ]
While that appears in order (a, d) it is actually reverse order, because when we
produce the top N we start at the end.
theory of the fix
- previous pagination depended on later hits with the same score "bumping" earlier
hits with the same score off the bottom of the list
- however, if we change the logic to <= instead of <, now the list in the previous
example would look like:
doc ids [ c d a b ]
scores [ 1 5 5 9 ]
- this small change means that now earlier (lower id) will score higher, and
thus we no longer depend on later hits bumping things down, which means returning
early is a valid thing to do
NOTE: this does depend on the hits coming back in order by ID. this is not
something strictly guaranteed, but it was the same assumption that allowed the
original behavior
This also has the side-effect that 2 hits with the same score come back in
ascending ID order, which is somehow more pleasing to me than reverse order.
you can now use terms like:
test?string*
and similar text in query strings to perform wildcard
searches. also if you use:
/aregexp/
it will perform a regexp search as well
having boost of 0 led to invalid scores of NaN
added integration test for wildcard query
added ability to run single integration test at a time
added assertion that scoare is not NaN/+Inf/-Inf
Steve Yen