ElasticSearch::SearchBuilder - A Perlish compact query language for ElasticSearch
Version 0.16
Compatible with ElasticSearch version 0.19.11
The 'text' queries have been renamed 'match' queries in elasticsearch 0.19.9. If you need support for an older version of elasticsearch, please use https://metacpan.org/release/DRTECH/ElasticSearch-SearchBuilder-0.15/.
The Query DSL for ElasticSearch (see Query DSL), which is used to write queries and filters, is simple but verbose, which can make it difficult to write and understand large queries.
ElasticSearch::SearchBuilder is an SQL::Abstract-like query language which exposes the full power of the query DSL, but in a more compact, Perlish way.
This module is considered stable. If you have suggestions for improvements to the API or the documenation, please contact me.
my $sb = ElasticSearch::SearchBuilder->new();
my $query = $sb->query({
body => 'interesting keywords',
-filter => {
status => 'active',
tags => ['perl','python','ruby'],
created => {
'>=' => '2010-01-01',
'<' => '2011-01-01'
},
}
})NOTE: ElasticSearch::SearchBuilder is fully integrated with the ElasticSearch API. Wherever you can specify query, filter or facet_filter in ElasticSearch, you can automatically use SearchBuilder by specifying queryb, filterb, facet_filterb instead.
$es->search( queryb => { body => 'interesting keywords' } )my $sb = ElasticSearch::SearchBuilder->new()Creates a new instance of the SearchBuilder - takes no parameters.
my $es_query = $sb->query($compact_query)Returns a query in the ElasticSearch query DSL.
$compact_query can be a scalar, a hash ref or an array ref.
$sb->query('foo')
# { "query" : { "match" : { "_all" : "foo" }}}
$sb->query({ ... }) or $sb->query([ ... ])
# { "query" : { ... }}my $es_filter = $sb->filter($compact_filter)Returns a filter in the ElasticSearch query DSL.
$compact_filter can be a scalar, a hash ref or an array ref.
$sb->filter('foo')
# { "filter" : { "term" : { "_all" : "foo" }}}
$sb->filter({ ... }) or $sb->filter([ ... ])
# { "filter" : { ... }}IMPORTANT: If you are not familiar with ElasticSearch then you should read "ELASTICSEARCH CONCEPTS" before continuing.
This module was inspired by SQL::Abstract but they are not compatible with each other.
The easiest way to explain how the syntax works is to give examples:
There are two contexts:
filtercontextFilter are fast and cacheable. They should be used to include/exclude docs, based on simple term values. For instance, exclude all docs that have neither tag
perlnorpython.Typically, most of your clauses should be filters, which reduce the number of docs that need to be passed to the query.
querycontextQueries are smarter than filters, but more expensive, as they have to calculate search relevance (ie
_score).They should be used where:
relevance is important, eg: in a search for tags
perlorpython, a doc that has BOTH tags is more relevant than a doc that has only onewhere search terms need to be analyzed as full text, eg: find me all docs where the
contentfield includes the words "Perl is GREAT", no matter how those words are capitalized.
The available operators (and the query/filter clauses that are generated) differ according to which context you are in.
The initial context depends upon which method you use: "query()" puts you into query context, and "filter()" into filter context.
However, you can switch from one context to another as follows:
$sb->query({
# query context
foo => 1,
bar => 2,
-filter => {
# filter context
foo => 1,
bar => 2,
-query => {
# query context
foo => 1
}
}
})Switch from query context to filter context:
# query field content for 'brown cow', and filter documents
# where status is 'active' and tags contains the term 'perl'
{
content => 'brown cow',
-filter => {
status => 'active',
tags => 'perl'
}
}
# no query, just a filter:
{ -filter => { status => 'active' }}See Filtered Query and Constant Score Query
Use a query as a filter:
# query field content for 'brown cow', and filter documents
# where status is 'active', tags contains the term 'perl'
# and a match query on field title contains 'important'
{
content => 'brown cow',
-filter => {
status => 'active',
tags => 'perl',
-query => {
title => 'important'
}
}
}See Query Filter
Key-value pairs are equivalent to the = operator, discussed below. They are converted to match queries or term filters:
# Field 'foo' contains term 'bar'
# equiv: { foo => { '=' => 'bar' }}
{ foo => 'bar' }
# Field 'foo' contains 'bar' or 'baz'
# equiv: { foo => { '=' => ['bar','baz'] }}
{ foo => ['bar','baz']}
# Field 'foo' contains terms 'bar' AND 'baz'
# equiv: { foo => { '-and' => [ {'=' => 'bar'}, {'=' => 'baz'}] }}
{ foo => ['-and','bar','baz']}
### FILTER ONLY ###
# Field 'foo' is missing ie has no value
# equiv: { -missing => 'foo' }
{ foo => undef }Arrays are OR'ed, hashes are AND'ed:
# tags = 'perl' AND status = 'active:
{
tags => 'perl',
status => 'active'
}
# tags = 'perl' OR status = 'active:
[
tags => 'perl',
status => 'active'
]
# tags = 'perl' or tags = 'python':
{ tags => [ 'perl','python' ]}
{ tags => { '=' => [ 'perl','python' ] }}
# tags begins with prefix 'p' or 'r'
{ tags => { '^' => [ 'p','r' ] }}The logic in an array can changed from OR to AND by making the first element of the array ref -and:
# tags has term 'perl' AND 'python'
{ tags => ['-and','perl','python']}
{
tags => [
-and => { '=' => 'perl'},
{ '=' => 'python'}
]
}However, the first element in an array ref which is used as the value for a field operator (see "FIELD OPERATORS") is not special:
# WRONG
{ tags => { '=' => [ '-and','perl','python' ] }}
# RIGHT
{ tags => ['-and' => [ {'=' => 'perl'}, {'=' => 'python'} ] ]}...otherwise you would never be able to search for the term -and. So if you might possibly have the terms -and or -or in your data, use:
{ foo => {'=' => [....] }}instead of:
{ foo => [....]}These unary operators allow you apply and, or and not logic to nested queries or filters.
# Field foo has both terms 'bar' and 'baz'
{ -and => [
foo => 'bar',
foo => 'baz'
]}
# Field 'name' contains 'john smith', or the name field is missing
# and the 'desc' field contains 'john smith'
{ -or => [
{ name => 'John Smith' },
{
desc => 'John Smith'
-filter => { -missing => 'name' },
}
]}The -and, -or and -not constructs emit bool queries when in query context, and and, or and not clauses when in filter context.
See also: "NAMED FILTERS", Bool Query, And Filter, Or Filter and Not Filter
Most operators (eg =, gt, geo_distance etc) are applied to a particular field. These are known as Field Operators. For example:
# Field foo contains the term 'bar'
{ foo => 'bar' }
{ foo => {'=' => 'bar' }}
# Field created is between Jan 1 and Dec 31 2010
{ created => {
'>=' => '2010-01-01',
'<' => '2011-01-01'
}}
# Field foo contains terms which begin with prefix 'a' or 'b' or 'c'
{ foo => { '^' => ['a','b','c' ]}}Some field operators are available as symbols (eg =, *, ^, gt) and others as words (eg geo_distance or -geo_distance - the dash is optional).
Multiple field operators can be applied to a single field. Use {} to imply this AND that:
# Field foo has any value from 100 to 200
{ foo => { gte => 100, lte => 200 }}
# Field foo begins with 'p' but is not python
{ foo => {
'^' => 'p',
'!=' => 'python'
}}Or [] to imply this OR that
# foo is 5 or foo greater than 10
{ foo => [
{ '=' => 5 },
{ 'gt' => 10 }
]}All word operators may be negated by adding not_ to the beginning, eg:
# Field foo does NOT contain a term beginning with 'bar' or 'baz'
{ foo => { not_prefix => ['bar','baz'] }}There are other operators which don't fit this { field => { op => value }} model.
For instance:
An operator might apply to multiple fields:
# Search fields 'title' and 'content' for text 'brown cow' { -match => { query => 'brown cow', fields => ['title','content'] } }The field might BE the value:
# Find documents where the field 'foo' is blank or undefined { -missing => 'foo' } # Find documents where the field 'foo' exists and has a value { -exists => 'foo' }For combining other queries or filters:
# Field foo has terms 'bar' and 'baz' but not 'balloo' { -and => [ foo => 'bar', foo => 'baz', -not => { foo => 'balloo' } ] }Other:
# Script query { -script => "doc['num1'].value > 1" }
These operators are called unary operators and ALWAYS begin with a dash - to distinguish them from field names.
Unary operators may also be prefixed with not_ to negate their meaning.
The -all operator matches all documents:
# match all
{ -all => 1 }
{ -all => 0 }
{ -all => {} }In query context, the match_all query usually scores all docs as 1 (ie having the same relevance). By specifying a norms_field, the relevance can be read from that field (at the cost of a slower execution time):
# Query context only
{ -all =>{
boost => 1,
norms_field => 'doc_boost'
}}These operators answer the question: "Does this field contain this term?"
Filter equality operators work only with exact terms, while query equality operators (the match family of queries) will "do the right thing", ie work with terms for not_analyzed fields and with analyzed text for analyzed fields.
These operators all generate match queries:
# Analyzed field 'title' contains the terms 'Perl is GREAT'
# (which is analyzed to the terms 'perl','great')
{ title => 'Perl is GREAT' }
{ title => { '=' => 'Perl is GREAT' }}
{ title => { match => 'Perl is GREAT' }}
# Not_analyzed field 'status' contains the EXACT term 'ACTIVE'
{ status => 'ACTIVE' }
{ status => { '=' => 'ACTIVE' }}
{ status => { match => 'ACTIVE' }}
# Same as above but with extra parameters:
{ title => {
match => {
query => 'Perl is GREAT',
boost => 2.0,
operator => 'and',
analyzer => 'default',
fuzziness => 0.5,
fuzzy_rewrite => 'constant_score_default',
lenient => 1,
max_expansions => 100,
minimum_should_match => 2,
prefix_length => 2,
}
}}Operators <>, != and not_match are synonyms for each other and just wrap the operator in a not clause.
See Match Query
These operators look for a complete phrase.
For instance, given the text
The quick brown fox jumped over the lazy dog.
# matches
{ content => { '==' => 'Quick Brown' }}
# doesn't match
{ content => { '==' => 'Brown Quick' }}
{ content => { '==' => 'Quick Fox' }}The slop parameter can be used to allow the phrase to match words in the same order, but further apart:
# with other parameters
{ content => {
phrase => {
query => 'Quick Fox',
slop => 3,
analyzer => 'default'
boost => 1,
lenient => 1,
}}See Match Query
To run a match | =, phrase or phrase_prefix query against multiple fields, you can use the -match unary operator:
{
-match => {
query => "Quick Fox",
type => 'boolean',
fields => ['content','title'],
use_dis_max => 1,
tie_breaker => 0.7,
boost => 2.0,
operator => 'and',
analyzer => 'default',
fuzziness => 0.5,
fuzzy_rewrite => 'constant_score_default',
lenient => 1,
max_expansions => 100,
minimum_should_match => 2,
prefix_length => 2,
}
}The type parameter can be boolean (equivalent of match | =) which is the default, phrase or phrase_prefix.
See Multi-match Query.
The term/terms operators are provided for completeness. You should almost always use the match/= operator instead.
There are only two use cases:
To find the exact (ie not analyzed) term 'foo' in an analyzed field:
{ title => { term => 'foo' }}To match a list of possible terms, where more than 1 value must match:
# match 2 or more of these tags { tags => { terms => { value => ['perl','python','php'], minimum_match => 2, boost => 1, } }}The above can also be achieved with the "-bool" operator.
term and terms are synonyms, as are not_term and not_terms.
These operators result in term or terms filters, which look for fields which contain exactly the terms specified:
# Field foo has the term 'bar':
{ foo => 'bar' }
{ foo => { '=' => 'bar' }}
{ foo => { 'term' => 'bar' }}
# Field foo has the term 'bar' or 'baz'
{ foo => ['bar','baz'] }
{ foo => { '=' => ['bar','baz'] }}
{ foo => { 'term' => ['bar','baz'] }}<> and != are synonyms:
# Field foo does not contain the term 'bar':
{ foo => { '!=' => 'bar' }}
{ foo => { '<>' => 'bar' }}
# Field foo contains neither 'bar' nor 'baz'
{ foo => { '!=' => ['bar','baz'] }}
{ foo => { '<>' => ['bar','baz'] }}The terms filter can take an execution parameter which affects how the filter of multiple terms is executed and cached.
For instance:
{ foo => {
-terms => {
value => ['foo','bar'],
execution => 'bool'
}
}}See Term Filter and Terms Filter
These operators imply a range query or filter, which can be numeric or alphabetical.
# Field foo contains terms between 'alpha' and 'beta'
{ foo => {
'gte' => 'alpha',
'lte' => 'beta'
}}
# Field foo contains numbers between 10 and 20
{ foo => {
'gte' => '10',
'lte' => '20'
}}
# boost a range *** query only ***
{ foo => {
range => {
gt => 5,
gte => 5,
lt => 10,
lte => 10,
boost => 2.0
}
}}For queries, < is a synonym for lt, > for gt etc.
See Range Query
Note: for filter clauses, the gt,gte,lt and lte operators imply a range filter, while the <, <=, > and >= operators imply a numeric_range filter.
This does not mean that you should use the numeric_range version for any field which contains numbers!
The numeric_range filter should be used for numbers/datetimes which have many distinct values, eg ID or last_modified. If you have a numeric field with few distinct values, eg number_of_fingers then it is better to use a range filter.
See Range Filter and Numeric Range Filter.
*** Filter context only ***
You can use a missing or exists filter to select only docs where a particular field exists and has a value, or is undefined or has no value:
# Field 'foo' has a value:
{ foo => { exists => 1 }}
{ foo => { missing => 0 }}
{ -exists => 'foo' }
# Field 'foo' is undefined or has no value:
{ foo => { missing => 1 }}
{ foo => { exists => 0 }}
{ -missing => 'foo' }
{ foo => undef }The missing filter also supports the null_value and existence parameters:
{
foo => {
missing => {
null_value => 1,
existence => 1,
}
}
}OR
{ -missing => {
field => 'foo',
null_value => 1,
existence => 1,
}}See Missing Filter and Exists Filter
*** Query context only ***
For most full text search queries, the match queries are what you want. These analyze the search terms, and look for documents that contain one or more of those terms. (See "EQUALITY (QUERIES)").
However, there is a more advanced query string syntax (see Lucene Query Parser Syntax) which understands search terms like:
perl AND python tag:recent "this exact phrase" -appleIt is useful for "power" users, but has the disadvantage that, if the syntax is incorrect, ES throws an error. You can use ElasticSearch::QueryParser to fix any syntax errors.
# find docs whose 'title' field matches 'this AND that'
{ title => { qs => 'this AND that' }}
{ title => { query_string => 'this AND that' }}
# With other parameters
{ title => {
field => {
query => 'this that ',
default_operator => 'AND',
analyzer => 'default',
allow_leading_wildcard => 0,
lowercase_expanded_terms => 1,
enable_position_increments => 1,
fuzzy_min_sim => 0.5,
fuzzy_prefix_length => 2,
fuzzy_rewrite => 'constant_score_default',
fuzzy_max_expansions => 1024,
lenient => 1,
phrase_slop => 10,
boost => 2,
analyze_wildcard => 1,
auto_generate_phrase_queries => 0,
rewrite => 'constant_score_default',
minimum_should_match => 3,
quote_analyzer => 'standard',
quote_field_suffix => '.unstemmed'
}
}}The unary form -qs or -query_string can be used when matching against multiple fields:
{ -qs => {
query => 'this AND that ',
fields => ['title','content'],
default_operator => 'AND',
analyzer => 'default',
allow_leading_wildcard => 0,
lowercase_expanded_terms => 1,
enable_position_increments => 1,
fuzzy_min_sim => 0.5,
fuzzy_prefix_length => 2,
fuzzy_rewrite => 'constant_score_default',
fuzzy_max_expansions => 1024,
lenient => 1,
phrase_slop => 10,
boost => 2,
analyze_wildcard => 1,
auto_generate_phrase_queries => 0,
use_dis_max => 1,
tie_breaker => 0.7,
minimum_should_match => 3,
quote_analyzer => 'standard',
quote_field_suffix => '.unstemmed'
}}An mlt or more_like_this query finds documents that are "like" the specified text, where "like" means that it contains some or all of the specified terms.
# Field foo is like "brown cow"
{ foo => { mlt => "brown cow" }}
# With other paramters:
{ foo => {
mlt => {
like_text => 'brown cow',
percent_terms_to_match => 0.3,
min_term_freq => 2,
max_query_terms => 25,
stop_words => ['the','and'],
min_doc_freq => 5,
max_doc_freq => 1000,
min_word_len => 0,
max_word_len => 20,
boost_terms => 2,
boost => 2.0,
analyzer => 'default'
}
}}
# multi fields
{ -mlt => {
like_text => 'brown cow',
fields => ['title','content']
percent_terms_to_match => 0.3,
min_term_freq => 2,
max_query_terms => 25,
stop_words => ['the','and'],
min_doc_freq => 5,
max_doc_freq => 1000,
min_word_len => 0,
max_word_len => 20,
boost_terms => 2,
boost => 2.0,
analyzer => 'default'
}}See MLT Field Query and MLT Query
An flt or fuzzy_like_this query fuzzifies all specified terms, then picks the best max_query_terms differentiating terms. It is a combination of fuzzy with more_like_this.
# Field foo is fuzzily similar to "brown cow"
{ foo => { flt => 'brown cow }}
# With other parameters:
{ foo => {
flt => {
like_text => 'brown cow',
ignore_tf => 0,
max_query_terms => 10,
min_similarity => 0.5,
prefix_length => 3,
boost => 2.0,
analyzer => 'default'
}
}}
# Multi-field
flt => {
like_text => 'brown cow',
fields => ['title','content'],
ignore_tf => 0,
max_query_terms => 10,
min_similarity => 0.5,
prefix_length => 3,
boost => 2.0,
analyzer => 'default'
}}See FLT Field Query and FLT Query
These operators use the match_phrase_prefix query.
For analyzed fields, it analyzes the search terms, and does a match_phrase query, with a prefix query on the last term. Think "auto-complete".
For not_analyzed fields, this behaves the same as the term-based prefix query.
For instance, given the phrase The quick brown fox jumped over the lazy dog:
# matches
{ content => { '^' => 'qui'}}
{ content => { '^' => 'quick br'}}
{ content => { 'phrase_prefix' => 'quick brown f'}}
# doesn't match
{ content => { '^' => 'quick fo' }}
{ content => { 'phrase_prefix' => 'fox brow'}}With extra options
{ content => {
phrase_prefix => {
query => "Brown Fo",
slop => 3,
analyzer => 'default',
boost => 3.0,
max_expansions => 100,
}
}}See http://www.elasticsearch.org/guide/reference/query-dsl/match-query.html
The prefix query is a term-based query - no analysis takes place, even on analyzed fields. Generally you should use ^ instead.
# Field 'lang' contains terms beginning with 'p'
{ lang => { prefix => 'p' }}
# With extra options
{ lang => {
'prefix' => {
value => 'p',
boost => 2,
rewrite => 'constant_score_default',
}
}}See Prefix Query.
# Field foo contains a term which begins with 'bar'
{ foo => { '^' => 'bar' }}
{ foo => { 'prefix' => 'bar' }}
# Field foo contains a term which begins with 'bar' or 'baz'
{ foo => { '^' => ['bar','baz'] }}
{ foo => { 'prefix' => ['bar','baz'] }}
# Field foo contains a term which begins with neither 'bar' nor 'baz'
{ foo => { 'not_prefix' => ['bar','baz'] }}See Prefix Filter
*** Query context only ***
A wildcard is a term-based query (no analysis is applied), which does shell globbing to find matching terms. In other words ? represents any single character, while * represents zero or more characters.
# Field foo matches 'f?ob*'
{ foo => { '*' => 'f?ob*' }}
{ foo => { 'wildcard' => 'f?ob*' }}
# with a boost:
{ foo => {
'*' => { value => 'f?ob*', boost => 2.0 }
}}
{ foo => {
'wildcard' => {
value => 'f?ob*',
boost => 2.0,
rewrite => 'constant_score_default',
}
}}See Wildcard Query
A fuzzy query is a term-based query (ie no analysis is done) which looks for terms that are similar to the the provided terms, where similarity is based on the Levenshtein (edit distance) algorithm:
# Field foo is similar to 'fonbaz'
{ foo => { fuzzy => 'fonbaz' }}
# With other parameters:
{ foo => {
fuzzy => {
value => 'fonbaz',
boost => 2.0,
min_similarity => 0.2,
max_expansions => 10,
rewrite => 'constant_score_default',
}
}}Normally, you should rather use either the "EQUALITY" queries with the fuzziness parameter, or the -flt queries.
See Fuzzy Query.
*** Query context only ***
These constructs allow you to combine multiple queries.
While a bool query adds together the scores of the nested queries, a dis_max query uses the highest score of any matching queries.
# Run the two queries and use the best score
{ -dismax => [
{ foo => 'bar' },
{ foo => 'baz' }
] }
# With other parameters
{ -dismax => {
queries => [
{ foo => 'bar' },
{ foo => 'baz' }
],
tie_breaker => 0.5,
boost => 2.0
] }See DisMax Query
Normally, there should be no need to use a bool query directly, as these are autogenerated from eg -and, -or and -not constructs. However, if you need to pass any of the other parameters to a bool query, then you can do the following:
{
-bool => {
must => [{ foo => 'bar' }],
must_not => { status => 'inactive' },
should => [
{ tag => 'perl' },
{ tag => 'python' },
{ tag => 'ruby' },
],
minimum_number_should_match => 2,
disable_coord => 1,
boost => 2
}
}See Bool Query
The boosting query can be used to "demote" results that match a given query. Unlike the must_not clause of a bool query, the query still matches, but the results are "less relevant".
{ -boosting => {
positive => { title => 'apple pear' },
negative => { title => 'apple computer' },
negative_boost => 0.2
}}See Boosting Query
The custom_boost query allows you to multiply the scores of another query by the specified boost factor. This is a bit different from a standard boost, which is normalized.
{
-custom_boost => {
query => { title => 'foo' },
boost_factor => 3
}
}See Custom Boost Factor Query.
Nested queries/filters allow you to run queries/filters on nested docs.
Normally, a doc like this would not allow you to associate the name perl with the number 5
{
title: "my title",
tags: [
{ name: "perl", num: 5},
{ name: "python", num: 2}
]
}However, if tags is mapped as a nested field, then you can run queries or filters on each sub-doc individually.
See Nested Type, Nested Query and Nested Filter
{
-nested => {
path => 'tags',
score_mode => 'avg',
_scope => 'my_tags',
query => {
"tags.name" => 'perl',
"tags.num" => { gt => 2 },
}
}
}See Nested Query
{
-nested => {
path => 'tags',
score_mode => 'avg',
_cache => 1,
_name => 'my_filter',
filter => {
tags.name => 'perl',
tags.num => { gt => 2},
}
}
}See Nested Filter
ElasticSearch supports the use of scripts to customise query or filter behaviour. By default the query language is mvel but javascript, groovy, python and native java scripts are also supported.
See Scripting for more on scripting.
*** Query context only ***
The -custom_score query allows you to customise the _score or relevance (and thus the order) of docs returned from a query.
{
-custom_score => {
query => { foo => 'bar' },
lang => 'mvel',
script => "_score * doc['my_numeric_field'].value / pow(param1, param2)"
params => {
param1 => 2,
param2 => 3.1
},
}
}*** Query context only ***
The -custom_filters_score query allows you to boost documents that match a filter, either with a boost parameter, or with a custom script.
This is a very powerful and efficient way to boost results which depend on matching unanalyzed fields, eg a tag or a date. Also, these filters can be cached.
{
-custom_filters_score => {
query => { foo => 'bar' },
score_mode => 'first|max|total|avg|min|multiply', # default 'first'
max_boost => 10,
filters => [
{
filter => { tag => 'perl' },
boost => 2,
},
{
filter => { tag => 'python' },
script => '_score * my_boost',
params => { my_boost => 2},
lang => 'mvel'
},
]
}
}See Custom Filters Score Query
*** Filter context only ***
The -script filter allows you to use a script as a filter. Return a true value to indicate that the filter matches.
# Filter docs whose field 'foo' is greater than 5
{ -script => "doc['foo'].value > 5 " }
# With other params
{
-script => {
script => "doc['foo'].value > minimum ",
params => { minimum => 5 },
lang => 'mvel'
}
}See Script Filter
Documents stored in ElasticSearch can be configured to have parent/child relationships.
See Parent Field for more.
Find child documents that have a parent document which matches a query.
# Find parent docs whose children of type 'comment' have the tag 'perl'
{
-has_parent => {
type => 'comment',
query => { tag => 'perl' },
_scope => 'my_scope',
boost => 1, # Query context only
score_type => 'max' # Query context only
}
}See Has Parent Query and See Has Parent Filter.
Find parent documents that have child documents which match a query.
# Find parent docs whose children of type 'comment' have the tag 'perl'
{
-has_child => {
type => 'comment',
query => { tag => 'perl' },
_scope => 'my_scope',
boost => 1, # Query context only
score_type => 'max' # Query context only
}
}See Has Child Query and See Has Child Filter.
*** Query context only ***
The top_children query runs a query against the child docs, and aggregates the scores to find the parent docs whose children best match.
{
-top_children => {
type => 'blog_tag',
query => { tag => 'perl' },
score => 'max',
factor => 5,
incremental_factor => 2,
_scope => 'my_scope'
}
}For all the geo filters, the normalize parameter defaults to true, meaning that the longitude value will be normalized to -180 to 180 and the latitude value to -90 to 90.
*** Filter context only ***
The geo_distance filter will find locations within a certain distance of a given point:
{
my_location => {
-geo_distance => {
location => { lat => 10, lon => 5 },
distance => '5km',
normalize => 1 | 0,
optimize_bbox => memory | indexed | none,
}
}
}*** Filter context only ***
The geo_distance_range filter is similar to the -geo_distance filter, but expressed as a range:
{
my_location => {
-geo_distance => {
location => { lat => 10, lon => 5 },
from => '5km',
to => '10km',
include_lower => 1 | 0,
include_upper => 0 | 1
normalize => 1 | 0,
optimize_bbox => memory | indexed | none,
}
}
}or instead of from, to, include_lower and include_upper you can use gt, gte, lt, lte.
*** Filter context only ***
The geo_bounding_box filter finds points which lie within the given rectangle:
{
my_location => {
-geo_bbox => {
top_left => { lat => 9, lon => 4 },
bottom_right => { lat => 10, lon => 5 },
normalize => 1 | 0,
type => memory | indexed
}
}
}*** Filter context only ***
The geo_polygon filter is similar to the -geo_bounding_box filter, except that it allows you to specify a polygon instead of a rectangle:
{
my_location => {
-geo_polygon => [
{ lat => 40, lon => -70 },
{ lat => 30, lon => -80 },
{ lat => 20, lon => -90 },
]
}
}or:
{
my_location => {
-geo_polygon => {
points => [
{ lat => 40, lon => -70 },
{ lat => 30, lon => -80 },
{ lat => 20, lon => -90 },
],
normalize => 1 | 0,
}
}
}*** Query context only ***
To run a different query depending on the index name, you can use the -indices query:
{
-indices => {
indices => 'one' | ['one','two],
query => { status => 'active' },
no_match_query => 'all' | 'none' | { another => query }
}
}The `no_match_query` will be run on any indices which don't appear in the specified list. It defaults to all, but can be set to none or to a full query.
See Indices Query.
*** Filter context only ***
To run a different filter depending on the index name, you can use the -indices filter:
{
-indices => {
indices => 'one' | ['one','two],
filter => { status => 'active' },
no_match_filter => 'all' | 'none' | { another => filter }
}
}The `no_match_filter` will be run on any indices which don't appear in the specified list. It defaults to all, but can be set to none or to a full filter.
See Indices Filter.
The _id field is not indexed by default, and thus isn't available for normal queries or filters
Returns docs with the matching _id or _type/_id combination:
# doc with ID 123
{ -ids => 123 }
# docs with IDs 123 or 124
{ -ids => [123,124] }
# docs of types 'blog' or 'comment' with IDs 123 or 124
{
-ids => {
type => ['blog','comment'],
values => [123,124]
}
}See IDs Query abd IDs Filter
*** Filter context only ***
Filters docs with matching _type fields.
While the _type field is indexed by default, ElasticSearch provides the type filter which will work even if indexing of the _type field is disabled.
# Filter docs of type 'comment'
{ -type => 'comment' }
# Filter docs of type 'comment' or 'blog'
{ -type => ['blog','comment' ]}See Type Filter
*** Filter context only ***
The limit filter limits the number of documents (per shard) to execute on:
{
name => "Joe Bloggs",
-filter => { -limit => 100 }
}See Limit Filter
ElasticSearch allows you to name filters, in which each search result will include a matched_filters array containing the names of all filters that matched.
*** Filter context only ***
{ -name => {
popular => { user_rank => { 'gte' => 10 }},
unpopular => { user_rank => { 'lt' => 10 }},
}}Multiple filters are joined with an or filter (as it doesn't make sense to join them with and).
See Named Filters and "-and | -or | -not".
Part of the performance boost that you get when using filters comes from the ability to cache the results of those filters. However, it doesn't make sense to cache all filters by default.
*** Filter context only ***
If you would like to override the default caching, then you can use -cache or -nocache:
# Don't cache the term filter for 'status'
{
content => 'interesting post',
-filter => {
-nocache => { status => 'active' }
}
}
# Do cache the numeric range filter:
{
content => 'interesting post',
-filter => {
-cache => { created => {'>' => '2010-01-01' } }
}
}See Query DSL for more details about what is cached by default and what is not.
It is also possible to use a name to identify a cached filter. For instance:
{
-cache_key => {
friends => { person_id => [1,2,3] },
enemies => { person_id => [4,5,6] },
}
}In the above example, the two filters will be joined by an and filter. The following example will have the two filters joined by an or filter:
{
-cache_key => [
friends => { person_id => [1,2,3] },
enemies => { person_id => [4,5,6] },
]
}See _cache_key for more details.
Sometimes, instead of using the SearchBuilder syntax, you may want to revert to the raw Query DSL that ElasticSearch uses.
You can do this by passing a reference to a HASH ref, for instance:
$sb->query({
foo => 1,
-filter => \{ term => { bar => 2 }}
})Would result in:
{
query => {
filtered => {
query => {
match => { foo => 1 }
},
filter => {
term => { bar => 2 }
}
}
}
}An example with OR'ed filters:
$sb->filter([
foo => 1,
\{ term => { bar => 2 }}
])Would result in:
{
filter => {
or => [
{ term => { foo => 1 }},
{ term => { bar => 2 }}
]
}
}An example with AND'ed filters:
$sb->filter({
-and => [
foo => 1 ,
\{ term => { bar => 2 }}
]
})Would result in:
{
filter => {
and => [
{ term => { foo => 1 }},
{ term => { bar => 2 }}
]
}
}Wherever a filter or query is expected, passing a reference to a HASH-ref is accepted.
ElasticSearch supports filters and queries:
A filter just answers the question: "Does this field match? Yes/No", eg:
Does this document have the tag
"beta"?Was this document published in 2011?
A query is used to calculate relevance ( known in ElasticSearch as
_score):Give me all documents that include the keywords
"Foo"and"Bar"and rank them in order of relevance.Give me all documents whose
tagfield contains"perl"or"ruby"and rank documents that contain BOTH tags more highly.
Filters are lighter and faster, and the results can often be cached, but they don't contribute to the _score in any way.
Typically, most of your clauses will be filters, and just a few will be queries.
All data is stored in ElasticSearch as a term, which is an exact value. The term "Foo" is not the same as "foo".
While this is useful for fields that have discreet values (eg "active", "inactive"), it is not sufficient to support full text search.
ElasticSearch has to analyze text to convert it into terms. This applies both to the text that the stored document contains, and to the text that the user tries to search on.
The default analyzer will:
split the text on (most) punctuation and remove that punctuation
lowercase each word
remove English stopwords
For instance, "The 2 GREATEST widgets are foo-bar and fizz_buzz" would result in the terms [2,'greatest','widgets','foo','bar','fizz_buzz'].
It is important that the same analyzer is used both for the stored text and for the search terms, otherwise the resulting terms may be different, and the query won't succeed.
For instance, a term query for GREATEST wouldn't work, but greatest would work. However, a match query for GREATEST would work, because the search text would be analyzed to produce the same terms that are stored in the index.
See Analysis for the list of supported analyzers.
ElasticSearch has a family of DWIM queries called match queries.
Their action depends upon how the field has been defined. If a field is analyzed (the default for string fields) then the match queries analyze the search terms before doing the search:
# Convert "Perl is GREAT" to the terms 'perl','great' and search
# the 'content' field for those terms
{ match: { content: "Perl is GREAT" }}If a field is not_analyzed, then it treats the search terms as a single term:
# Find all docs where the 'status' field contains EXACTLY the term 'ACTIVE'
{ match: { status: "ACTIVE" }}Filters, on the other hand, don't have full text queries - filters operate on simple terms instead.
See Match Query for more about match queries.
Clinton Gormley, <drtech at cpan.org>
If you have any suggestions for improvements, or find any bugs, please report them to https://github.com/clintongormley/ElasticSearch-SearchBuilder/issues. I will be notified, and then you'll automatically be notified of progress on your bug as I make changes.
Add support for span queries.
You can find documentation for this module with the perldoc command.
perldoc ElasticSearch::SearchBuilderYou can also look for information at: http://www.elasticsearch.org
Thanks to SQL::Abstract for providing the inspiration and some of the internals.
Copyright 2011 Clinton Gormley.
This program is free software; you can redistribute it and/or modify it under the terms of either: the GNU General Public License as published by the Free Software Foundation; or the Artistic License.
See http://dev.perl.org/licenses/ for more information.