- Elasticsearch Guide: other versions:
- Getting Started
- Setup
- Breaking changes
- Breaking changes in 2.2
- Breaking changes in 2.1
- Breaking changes in 2.0
- Removed features
- Network changes
- Multiple
path.data
striping - Mapping changes
- CRUD and routing changes
- Query DSL changes
- Search changes
- Aggregation changes
- Parent/Child changes
- Scripting changes
- Index API changes
- Snapshot and Restore changes
- Plugin and packaging changes
- Setting changes
- Stats, info, and
cat
changes - Java API changes
- API Conventions
- Document APIs
- Search APIs
- Aggregations
- Metrics Aggregations
- Avg Aggregation
- Cardinality Aggregation
- Extended Stats Aggregation
- Geo Bounds Aggregation
- Geo Centroid Aggregation
- Max Aggregation
- Min Aggregation
- Percentiles Aggregation
- Percentile Ranks Aggregation
- Scripted Metric Aggregation
- Stats Aggregation
- Sum Aggregation
- Top hits Aggregation
- Value Count Aggregation
- Bucket Aggregations
- Children Aggregation
- Date Histogram Aggregation
- Date Range Aggregation
- Filter Aggregation
- Filters Aggregation
- Geo Distance Aggregation
- GeoHash grid Aggregation
- Global Aggregation
- Histogram Aggregation
- IPv4 Range Aggregation
- Missing Aggregation
- Nested Aggregation
- Range Aggregation
- Reverse nested Aggregation
- Sampler Aggregation
- Significant Terms Aggregation
- Terms Aggregation
- Pipeline Aggregations
- Avg Bucket Aggregation
- Derivative Aggregation
- Max Bucket Aggregation
- Min Bucket Aggregation
- Sum Bucket Aggregation
- Stats Bucket Aggregation
- Extended Stats Bucket Aggregation
- Percentiles Bucket Aggregation
- Moving Average Aggregation
- Cumulative Sum Aggregation
- Bucket Script Aggregation
- Bucket Selector Aggregation
- Serial Differencing Aggregation
- Caching heavy aggregations
- Returning only aggregation results
- Aggregation Metadata
- Metrics Aggregations
- Indices APIs
- Create Index
- Delete Index
- Get Index
- Indices Exists
- Open / Close Index API
- Put Mapping
- Get Mapping
- Get Field Mapping
- Types Exists
- Index Aliases
- Update Indices Settings
- Get Settings
- Analyze
- Index Templates
- Warmers
- Shadow replica indices
- Indices Stats
- Indices Segments
- Indices Recovery
- Indices Shard Stores
- Clear Cache
- Flush
- Refresh
- Force Merge
- Optimize
- Upgrade
- cat APIs
- Cluster APIs
- Query DSL
- Mapping
- Field datatypes
- Meta-Fields
- Mapping parameters
analyzer
boost
coerce
copy_to
doc_values
dynamic
enabled
fielddata
format
geohash
geohash_precision
geohash_prefix
ignore_above
ignore_malformed
include_in_all
index
index_options
lat_lon
fields
norms
null_value
position_increment_gap
precision_step
properties
search_analyzer
similarity
store
term_vector
- Dynamic Mapping
- Transform
- Analysis
- Analyzers
- Tokenizers
- Token Filters
- Standard Token Filter
- ASCII Folding Token Filter
- Length Token Filter
- Lowercase Token Filter
- Uppercase Token Filter
- NGram Token Filter
- Edge NGram Token Filter
- Porter Stem Token Filter
- Shingle Token Filter
- Stop Token Filter
- Word Delimiter Token Filter
- Stemmer Token Filter
- Stemmer Override Token Filter
- Keyword Marker Token Filter
- Keyword Repeat Token Filter
- KStem Token Filter
- Snowball Token Filter
- Phonetic Token Filter
- Synonym Token Filter
- Compound Word Token Filter
- Reverse Token Filter
- Elision Token Filter
- Truncate Token Filter
- Unique Token Filter
- Pattern Capture Token Filter
- Pattern Replace Token Filter
- Trim Token Filter
- Limit Token Count Token Filter
- Hunspell Token Filter
- Common Grams Token Filter
- Normalization Token Filter
- CJK Width Token Filter
- CJK Bigram Token Filter
- Delimited Payload Token Filter
- Keep Words Token Filter
- Keep Types Token Filter
- Classic Token Filter
- Apostrophe Token Filter
- Decimal Digit Token Filter
- Character Filters
- Modules
- Index Modules
- Testing
- Glossary of terms
- Release Notes
WARNING: Version 2.2 of Elasticsearch has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
query Section
editquery
Section
editThe query
section contains detailed timing of the query tree executed by Lucene on a particular shard.
The overall structure of this query tree will resemble your original Elasticsearch query, but may be slightly
(or sometimes very) different. It will also use similar but not always identical naming. Using our previous
term
query example, let’s analyze the query
section:
"query": [ { "query_type": "BooleanQuery", "lucene": "message:search message:test", "time": "15.52889800ms", "breakdown": {...}, "children": [ { "query_type": "TermQuery", "lucene": "message:search", "time": "4.938855000ms", "breakdown": {...} }, { "query_type": "TermQuery", "lucene": "message:test", "time": "0.5016660000ms", "breakdown": {...} } ] } ]
Based on the profile structure, we can see that our match
query was rewritten by Lucene into a BooleanQuery with two
clauses (both holding a TermQuery). The "query_type"
field displays the Lucene class name, and often aligns with
the equivalent name in Elasticsearch. The "lucene"
field displays the Lucene explanation text for the query, and
is made available to help differentiating between parts of your query (e.g. both "message:search"
and "message:test"
are TermQuery’s and would appear identical otherwise.
The "time"
field shows that this query took ~15ms for the entire BooleanQuery to execute. The recorded time is inclusive
of all children.
The "breakdown"
field will give detailed stats about how the time was spent, we’ll look at
that in a moment. Finally, the "children"
array lists any sub-queries that may be present. Because we searched for two
values ("search test"), our BooleanQuery holds two children TermQueries. They have identical information (query_type, time,
breakdown, etc). Children are allowed to have their own children.
Timing Breakdown
editThe breakdown
component lists detailed timing statistics about low-level Lucene execution:
"breakdown": { "score": 0, "next_doc": 24495, "match": 0, "create_weight": 8488388, "build_scorer": 7016015, "advance": 0 }
Timings are listed in wall-clock nanoseconds and are not normalized at all. All caveats about the overall
time
apply here. The intention of the breakdown is to give you a feel for A) what machinery in Lucene is
actually eating time, and B) the magnitude of differences in times between the various components. Like the overall time,
the breakdown is inclusive of all children times.
The meaning of the stats are as follows:
All parameters:
edit
|
A Query in Lucene must be capable of reuse across multiple IndexSearchers (think of it as the engine that
executes a search against a specific Lucene Index). This puts Lucene in a tricky spot, since many queries
need to accumulate temporary state/statistics associated with the index it is being used against, but the
Query contract mandates that it must be immutable.
|
|
This parameter shows how long it takes to build a Scorer for the query. A Scorer is the mechanism that
iterates over matching documents generates a score per-document (e.g. how well does "foo" match the document?).
Note, this records the time required to generate the Scorer object, not actually score the documents. Some
queries have faster or slower initialization of the Scorer, depending on optimizations, complexity, etc.
|
|
The Lucene method |
|
|
|
Some queries, such as phrase queries, match documents using a "Two Phase" process. First, the document is
"approximately" matched, and if it matches approximately, it is checked a second time with a more rigorous
(and expensive) process. The second phase verification is what the |
|
This records the time taken to score a particular document via it’s Scorer |
On this page