This API (unexported) will estimate the amount of memory needed to execute
a search query over an index before the collector begins data collection.
Sample estimates for certain queries:
{Size: 10, BenchmarkUpsidedownSearchOverhead}
ESTIMATE BENCHMEM
TermQuery 4616 4796
MatchQuery 5210 5405
DisjunctionQuery (Match queries) 7700 8447
DisjunctionQuery (Term queries) 6514 6591
ConjunctionQuery (Match queries) 7524 8175
Nested disjunction query (disjunction of disjunctions) 10306 10708
…
previously we parsed/returned large sections of the documents
back index row in order to compute facet information. this
would require parsing the protobuf of the entire back index row.
unfortunately this creates considerable garbage.
this new version introduces a visitor/callback approach to
working with data inside the back index row. the benefit
of this approach is that we can let the higher-level code
see values, prior to any copies of data being made or
intermediate garbage being created. implementations of
the callback must copy any value which they would like to
retain beyond the callback.
NOTE: this approach is duplicates code from the
automatically generated protobuf code
NOTE: this approach assumes that the "field" field be serialized
before the "terms" field. This is guaranteed by our currently
generated protobuf encoder, and is recommended by the protobuf
spec. But, decoders SHOULD support them occuring in any order,
which we do not.
this originated from a misunderstanding of mine going back
several years. the values need not be float64 just because
we plan to serialize them as json.
there are still larger questions about what the right type should
be, and where should any conversions go. but, this commit
simply attempts to address the most egregious problems
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.
primary change is going back to sort values be []string
and not []interface{}, this avoid allocatiosn converting
into the interface{}
that sounds obvious, so why didn't we just do that first?
because a common (default) sort is score, which is naturally
a number, not a string (like terms). converting into the
number was also expensive, and the common case.
so, this solution also makes the change to NOT put the score
into the sort value list. instead you see the dummy value
"_score". this is just a placeholder, the actual sort impl
knows that field of the sort is the score, and will sort
using the actual score.
also, several other aspets of the benchmark were cleaned up
so that unnecessary allocations do not pollute the cpu profiles
Here are the updated benchmarks:
$ go test -run=xxx -bench=. -benchmem -cpuprofile=cpu.out
BenchmarkTop10of100000Scores-4 3000 465809 ns/op 2548 B/op 33 allocs/op
BenchmarkTop100of100000Scores-4 2000 626488 ns/op 21484 B/op 213 allocs/op
BenchmarkTop10of1000000Scores-4 300 5107658 ns/op 2560 B/op 33 allocs/op
BenchmarkTop100of1000000Scores-4 300 5275403 ns/op 21624 B/op 213 allocs/op
PASS
ok github.com/blevesearch/bleve/search/collectors 7.188s
Prior to this PR, master reported:
$ go test -run=xxx -bench=. -benchmem
BenchmarkTop10of100000Scores-4 3000 453269 ns/op 360161 B/op 42 allocs/op
BenchmarkTop100of100000Scores-4 2000 519131 ns/op 388275 B/op 219 allocs/op
BenchmarkTop10of1000000Scores-4 200 7459004 ns/op 4628236 B/op 52 allocs/op
BenchmarkTop100of1000000Scores-4 200 8064864 ns/op 4656596 B/op 232 allocs/op
PASS
ok github.com/blevesearch/bleve/search/collectors 7.385s
So, we're pretty close on the smaller datasets, and we scale better on the larger datasets.
We also show fewer allocations and bytes in all cases (some of this is artificial due to test cleanup).
The syntax used is an array of strings. The strings "_id" and
"_score" are special and reserved to mean sorting on the document
id and score repsectively. All other strings refer to the literal
field name with that value. If the string is prefixed with "-"
the order of that sort is descending, without it, it defaults to
ascending.
Examples:
"sort":["-abv","-_score"]
This will sort results in decreasing order of the "abv" field.
Results which have the same value of the "abv" field will then
be sorted by their score, also decreasing.
If no value for "sort" is provided in the search request the
default soring is the same as before, which is decreasing score.
the motivation for this commit is long and detailed and has been
documented externally here:
https://gist.github.com/mschoch/5cc5c9cf4669a5fe8512cb7770d3c1a2
the core of the changes are:
1. recognize that collector/searcher need only a fixed number
of DocumentMatch instances, and this number can be determined
from the structure of the query, not the size of the data
2. knowing this, instances can be allocated in bulk, up front
and they can be reused without locking (since all search
operations take place in a single goroutine
3. combined with previous commits which enabled reuse of
the IndexInternalID []byte, this allows for no allocation/copy
of these bytes as well (by using DocumentMatch Reset() method
when returning entries to the pool
previous attempt was flawed (but maked by Reset() method)
new approach is to do this work in the Reset() method itself,
logically this is where it belongs.
but further we acknowledge that IndexInternalID []byte lifetime
lives beyond the TermFieldDoc, so another copy is made into
the DocumentMatch. Although this introduces yet another copy
the theory being tested is that it allows each of these
structuress to reuse memory without additional allocation.
instead of separate DocumentMatch/DocumentMatchInternal
rules are simple, everything operates on the IndexInternalID field
until the results are returned, then ID is set correctly
the IndexInternalID field is not exported to JSON
index id's are now opaque (until finally returned to top-level user)
- the TermFieldDoc's returned by TermFieldReader no longer contain doc id
- instead they return an opaque IndexInternalID
- items returned are still in the "natural index order"
- but that is no longer guaranteed to be "doc id order"
- correct behavior requires that they all follow the same order
- but not any particular order
- new API FinalizeDocID which converts index internal ID's to public string ID
- APIs used internally which previously took doc id now take IndexInternalID
- that is DocumentFieldTerms() and DocumentFieldTermsForFields()
- however, APIs that are used externally do not reflect this change
- that is Document()
- DocumentIDReader follows the same changes, but this is less obvious
- behavior clarified, used to iterate doc ids, BUT NOT in doc id order
- method STILL available to iterate doc ids in range
- but again, you won't get them in any meaningful order
- new method to iterate actual doc ids from list of possible ids
- this was introduced to make the DocIDSearcher continue working
searchers now work with the new opaque index internal doc ids
- they return new DocumentMatchInternal (which does not have string ID)
scorerers also work with these opaque index internal doc ids
- they return DocumentMatchInternal (which does not have string ID)
collectors now also perform a final step of converting the final result
- they STILL return traditional DocumentMatch (with string ID)
- but they now also require an IndexReader (so that they can do the conversion)
This optimization changes the search.Search.Next() interface API,
adding an optional, pre-allocated *DocumentMatch parameter.
When it's non-nil, the TermSearcher and TermQueryScorer will use that
pre-allocated *DocumentMatch, instead of allocating a brand new
DocumentMatch instance.
an IndexAlias allows you easily work with one logical Index
while changing the actual Index its pointing to behind the scenes
Changing which actual Index is backing an IndexAlias can be done
atomically so that your application smoothly transitions from
one Index to another.
A separate use of IndexAlias is allowed when the IndexAlias is
defined to point to multiple Indexes. In this case only the
Search() operation is supported, but the Search will be run
on each of the underlying indexes in parallel, and the results
will be merged.
abhinavdangeti