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Functions allow filtering based on properties of nodes or variables. Functions can be applied in the query root or in filters. Comparison functions (eq, ge, gt, le, lt) in the query root (func:) can only be applied on indexed predicates. Comparison functions can be used on @filter directives even on predicates that haven’t been indexed. Filtering on non-indexed predicates can be slow for large datasets, as they require iterating over all of the possible values at the level where the filter is being used. All other functions, in the query root or in the filter can only be applied to indexed predicates. For functions on string valued predicates, if no language preference is given, the function is applied to all languages and strings without a language tag. If a language preference is given, the function is applied only to strings of the given language.

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Term matching

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allofterms

Syntax Example: allofterms(predicate, "space-separated term list") Schema Types: string Index Required: term Matches strings that have all specified terms in any order, case-insensitive.

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Usage at root

Query Example: all nodes that have name containing terms indiana and jones, returning the English name and genre in English. 
{
  me(func: allofterms(name@en, "jones indiana")) {
    name@en
    genre { name@en }
  }
}

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Usage as filter

Query Example: all Steven Spielberg films that contain the words indiana and jones. The @filter(has(director.film)) removes nodes with name Steven Spielberg that aren’t the director --- the data also contains a character in a film called Steven Spielberg. 
{
  me(func: eq(name@en, "Steven Spielberg"))
  @filter(has(director.film)) {
    name@en
    director.film
    @filter(allofterms(name@en, "jones indiana")) {
      name@en
    }
  }
}

​
anyofterms

Syntax Example: anyofterms(predicate, "space-separated term list") Schema Types: string Index Required: term Matches strings that have any of the specified terms in any order (case insensitive).

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Usage at root

Query Example: All nodes that have a name containing either poison or peacock. Many of the returned nodes are movies, but people like Joan Peacock also meet the search terms because without a cascade directive the query doesn’t require a genre. 
{
  me(func: anyofterms(name@en, "poison peacock")) {
    name@en
    genre { name@en }
  }
}

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Usage as filter

Query Example: All Steven Spielberg movies that contain war or spies. The @filter(has(director.film)) removes nodes with name Steven Spielberg that aren’t the director --- the data also contains a character in a film called Steven Spielberg. 
{
  me(func: eq(name@en, "Steven Spielberg"))
  @filter(has(director.film)) {
    name@en
    director.film
    @filter(anyofterms(name@en, "war spies")) { name@en }
  }
}
Syntax Examples: ngram(predicate, "a string of text") Schema Types: string Index Required: ngram The ngram index tokenizes a string into shingles (contiguous sequences of n words), with support for stop word removal and stemming. The ngram function matches strings that contain the given sequence of terms.

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Usage at root

Query example: all nodes that have a name containing quick, brown, and fox. 
{
  me(func: ngram(name@en, "quick brown fox")) {
    name@en
  }
}

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Regular expressions

Syntax Examples: regexp(predicate, /regular-expression/) or case insensitive regexp(predicate, /regular-expression/i) Schema Types: string Index Required: trigram Matches strings by regular expression. The regular expression language is that of go regular expressions. Query Example: At root, match nodes with Steven Sp at the start of name, followed by any characters. For each such matched UID, match the films containing ryan. Note the difference with allofterms, which would match only ryan but regular expression search also matches within terms, such as bryan. 
{
  directors(func: regexp(name@en, /^Steven Sp.\*$/)) {
    name@en
    director.film
    @filter(regexp(name@en, /ryan/i)) { name@en }
  }
}

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Technical details

A Trigram is a substring of three continuous runes. For example, Dgraph has trigrams Dgr, gra, rap, aph. To ensure efficiency of regular expression matching, Dgraph uses trigram indexing. Dgraph converts the regular expression to a trigram query, uses the trigram index and trigram query to find possible matches and applies the full regular expression search only to the possibles.

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Writing efficient regular expressions and limitations

Keep the following in mind when designing regular expression queries.
  • At least one trigram must be matched by the regular expression (patterns shorter than 3 runes aren’t supported) since Dgraph requires regular expressions that can be converted to a trigram query.
  • The number of alternative trigrams matched by the regular expression should be as small as possible ([a-zA-Z][a-zA-Z][0-9] isn’t a good idea). Many possible matches means the full regular expression is checked against many strings; where as, if the expression enforces more trigrams to match, Dgraph can make better use of the index and check the full regular expression against a smaller set of possible matches.
  • Thus, the regular expression should be as precise as possible. Matching longer strings means more required trigrams, which helps to effectively use the index.
  • If repeat specifications (*, +, ?, {n,m}) are used, the entire regular expression must not match the empty string or any string: for example, * may be used like [Aa]bcd* but not like (abcd)* or (abcd)|((defg)*)
  • Repeat specifications after bracket expressions (e.g. [fgh]{7}, [0-9]+ or [a-z]{3,5}) are often considered as matching any string because they match too many trigrams.
  • If the partial result (for subset of trigrams) exceeds 1000000 UIDs during index scan, the query is stopped to prohibit expensive queries.

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Fuzzy matching

Syntax: match(predicate, string, distance) Schema Types: string Index Required: trigram Matches predicate values by calculating the Levenshtein distance to the string, also known as fuzzy matching. The distance parameter must be greater than zero (0). Using a greater distance value can yield more but less accurate results. Query Example: At root, fuzzy match nodes similar to Stephen, with a distance value of less than or equal to 8. 
{
  directors(func: match(name@en, Stephen, 8)) {
    name@en
  }
}
Same query with a Levenshtein distance of 3. 
{
  directors(func: match(name@en, Stephen, 3)) {
    name@en
  }
}
Syntax Examples: similar_to(predicate, 3, "[0.9, 0.8, 0, 0]") Alternatively the vector can be passed as a variable: similar_to(predicate, 3, $vec) This function finds the nodes that have predicate close to the provided vector. The search is based on the distance metric specified in the index (cosine, euclidean, or dotproduct). The shorter distance indicates more similarity. The second parameter, 3 specifies that top 3 matches be returned. Schema Types: float32vector Index Required: hnsw

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Full-Text Search

Syntax Examples: alloftext(predicate, "space-separated text") and anyoftext(predicate, "space-separated text") Schema Types: string Index Required: fulltext Apply full-text search with stemming and stop words to find strings matching all or any of the given text. The following steps are applied during index generation and to process full-text search arguments:
  1. Tokenization (according to Unicode word boundaries).
  2. Conversion to lowercase.
  3. Unicode-normalization (to Normalization Form KC).
  4. Stemming using language-specific stemmer (if supported by language).
  5. Stop words removal (if supported by language).
Dgraph uses bleve for its full-text search indexing. See also the bleve language specific stop word lists. Following table contains all supported languages, corresponding country-codes, stemming and stop words filtering support.
LanguageCountry CodeStemmingStop words
Arabicar✓✓
Armenianhy✓
Basqueeu✓
Bulgarianbg✓
Catalanca✓
Chinesezh✓✓
Czechcs✓
Danishda✓✓
Dutchnl✓✓
Englishen✓✓
Finnishfi✓✓
Frenchfr✓✓
Gaelicga✓
Galiciangl✓
Germande✓✓
Greekel✓
Hindihi✓✓
Hungarianhu✓✓
Indonesianid✓
Italianit✓✓
Japaneseja✓✓
Koreanko✓✓
Norwegianno✓✓
Persianfa✓
Portuguesept✓✓
Romanianro✓✓
Russianru✓✓
Spanishes✓✓
Swedishsv✓✓
Turkishtr✓✓
Query Example: All names that have dog, dogs, bark, barks, barking, etc. Stop word removal eliminates the and which. 
{
  movie(func: alloftext(name@en, "the dog which barks")) {
    name@en
  }
}

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Inequality

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equal to

Syntax Examples:
  • eq(predicate, value)
  • eq(val(varName), value)
  • eq(predicate, val(varName))
  • eq(count(predicate), value)
  • eq(predicate, [val1, val2, ..., valN])
  • eq(predicate, [$var1, "value", ..., $varN])
Schema Types: int, float, bool, string, dateTime Index Required: An index is required for the eq(predicate, ...) forms (see table below) when used at query root. For count(predicate) at the query root, the @count index is required. For variables the values have been calculated as part of the query, so no index is required.
TypeIndex Options
intint
floatfloat
boolbool
stringexact, hash, term, fulltext
dateTimedateTime
Test for equality of a predicate or variable to a value or find in a list of values. The boolean constants are true and false, so with eq this becomes, for example, eq(boolPred, true). Query Example: Movies with exactly thirteen genres. 
{
  me(func: eq(count(genre), 13)) {
    name@en genre { name@en }
  }
}
Query Example: Directors called Steven who have directed 1,2 or 3 movies. 
{
  steve as var(func: allofterms(name@en, "Steven")) {
    films as count(director.film)
  }

  stevens(func: uid(steve)) @filter(eq(val(films), [1,2,3])) { name@en numFilms :
  val(films)
  }
}

​
less than, less than or equal to, greater than and greater than or equal to

Syntax Examples: for inequality IE
  • IE(predicate, value)
  • IE(val(varName), value)
  • IE(predicate, val(varName))
  • IE(count(predicate), value)
With IE replaced by
  • le less than or equal to
  • lt less than
  • ge greater than or equal to
  • gt greater than
Schema Types: int, float, string, dateTime Index required: An index is required for the IE(predicate, ...) forms (see table below) when used at query root. For count(predicate) at the query root, the @count index is required. For variables the values have been calculated as part of the query, so no index is required.
TypeIndex Options
intint
floatfloat
stringexact
dateTimedateTime
Query Example: Ridley Scott movies released before 1980. 
{
  me(func: eq(name@en, "Ridley Scott")) {
    name@en director.film
    @filter(lt(initial_release_date, "1980-01-01")) { initial_release_date name@en
    }
  }
}
Query Example: Movies with directors with Steven in name and have directed more than 100 actors. 
{
  ID as var(func: allofterms(name@en, "Steven")) {
    director.film { num_actors as count(starring) }
    total as sum(val(num_actors))
  }

  dirs(func: uid(ID)) @filter(gt(val(total), 100)) { name@en total_actors :
  val(total)
  }
}
Query Example: A movie in each genre that has over 30000 movies. Because there is no order specified on genres, the order will be by UID. The count index records the number of edges out of nodes and makes such queries more. 
{
  genre(func: gt(count(~genre), 30000)) {
    name@en ~genre (first:1) { name@en }
  }
}
Query Example: Directors called Steven and their movies which have initial_release_date greater than that of the movie Minority Report. 
{
  var(func: eq(name@en,"Minority Report")) {
    d as initial_release_date
  }

  me(func: eq(name@en, "Steven Spielberg")) {
    name@en director.film
    @filter(ge(initial_release_date, val(d))) { initial_release_date name@en
  }
}

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between

Syntax Example: between(predicate, startDateValue, endDateValue) Schema Types: Scalar types, including dateTime, int, float and string Index Required: dateTime, int, float, and exact on strings Returns nodes that match an inclusive range of indexed values. The between keyword performs a range check on the index to improve query efficiency, helping to prevent a wide-ranging query on a large set of data from running slowly. A common use case for the between keyword is to search within a dataset indexed by dateTime. The following example query demonstrates this use case. Query Example: Movies initially released in 1977, listed by genre. 
{
  me(func: between(initial_release_date, "1977-01-01", "1977-12-31")) {
    name@en genre { name@en }
  }
}

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UID

Syntax Examples:
  • q(func: uid(<uid>))
  • predicate @filter(uid(<uid1>, ..., <uidn>))
  • predicate @filter(uid(a)) for variable a
  • q(func: uid(a,b)) for variables a and b
  • q(func: uid($uids)) for multiple uids in DQL Variables. You have to set the value of this variable as a string (e.g"[0x1, 0x2, 0x3]") in queryWithVars.
Filters nodes at the current query level to only nodes in the given set of UIDs. For query variable a, uid(a) represents the set of UIDs stored in a. For value variable b, uid(b) represents the UIDs from the UID to value map. With two or more variables, uid(a,b,...) represents the union of all the variables. uid(<uid>), like an identity function, will return the requested UID even if the node does not have any edges.
If the UID of a node is known, values for the node can be read directly.
Query Example: The films of Priyanka Chopra by known UID. 
{
  films(func: uid(0x2c964)) {
    name@hi actor.film { performance.film { name@hi } }
  }
}
Query Example: The films of Taraji Henson by genre. 
{
  var(func: allofterms(name@en, "Taraji Henson")) {
    actor.film { F as performance.film { G as genre } }
  }

  Taraji_films_by_genre(func: uid(G)) { genre_name : name@en films : ~genre
  @filter(uid(F)) { film_name : name@en } }
}
Query Example: Taraji Henson films ordered by number of genres, with genres listed in order of how many films Taraji has made in each genre. 
{
  var(func: allofterms(name@en, "Taraji Henson")) {
    actor.film {
      F as performance.film { G as count(genre) genre { C as count(~genre
      @filter(uid(F))) } } }
  }
}

Taraji_films_by_genre_count(func: uid(G), orderdesc: val(G)) { film_name :
name@en genres : genre (orderdesc: val(C)) { genre_name : name@en } } }

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uid_in

Syntax Examples:
  • q(func: ...) @filter(uid_in(predicate, <uid>))
  • predicate1 @filter(uid_in(predicate2, <uid>))
  • predicate1 @filter(uid_in(predicate2, [<uid1>, ..., <uidn>]))
  • predicate1 @filter(uid_in(predicate2, uid(myVariable) ))
Schema Types: UID Index Required: none While the uid function filters nodes at the current level based on UID, function uid_in allows looking ahead along an edge to check that it leads to a particular UID. This can often save an extra query block and avoids returning the edge. uid_in cannot be used at root. It accepts multiple UIDs as its argument, and it accepts a UID variable (which can contain a map of UIDs). Query Example: The collaborations of Marc Caro and Jean-Pierre Jeunet (UID 0x99706). If the UID of Jean-Pierre Jeunet is known, querying this way removes the need to have a block extracting his UID into a variable and the extra edge traversal and filter for ~director.film. 
{
  caro(func: eq(name@en, "Marc Caro")) {
    name@en director.film
    @filter(uid_in(~director.film, 0x99706)) { name@en }
  }
}
You can also query for Jean-Pierre Jeunet if you don’t know his UID and use it in a UID variable. 
{
  getJeunet as q(func: eq(name@fr, "Jean-Pierre Jeunet"))

  caro(func: eq(name@en, "Marc Caro")) {
    name@en director.film
    @filter(uid_in(~director.film, uid(getJeunet))) { name@en }
  }
}

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type

Query Example: all nodes of type “Animal” 
{
  q(func: type(Animal)) { uid name }
}
type(Animal) equivalent to eq(dgraph.type,"Animal") type() can also be used as a filter: 
{
  q(func: has(parent)) { uid parent @filter(type(Person)) { uid name } }
}

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has

Syntax Examples: has(predicate) Schema Types: all Determines if a node has a particular predicate. Query Example: First five directors and all their movies that have a release date recorded. Directors have directed at least one film --- equivalent semantics to gt(count(director.film), 0). 
{
  me(func: has(director.film), first: 5) {
    name@en director.film
    @filter(has(initial_release_date)) { initial_release_date name@en }
  }
}

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Geolocation

As of now we only support indexing Point, Polygon and MultiPolygon geometry types. However, Dgraph can store other types of gelocation data.
Note that for geo queries, any polygon with holes is replace with the outer loop, ignoring holes. Also, as for version 0.7.7 polygon containment checks are approximate.

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Mutations

To make use of the geo functions you would need an index on your predicate. 
loc: geo @index(geo) .
Here is how you would add a Point. 
{
  set {
    <_:0xeb1dde9c> <loc>
      "{\"type\":\"Point\",\"coordinates\":[-122.4220186,37.772318]}"^^<geo:geojson>
      . <_:0xeb1dde9c> <name> "Hamon Tower" . <\_:0xeb1dde9c> <dgraph.type> "Location"
      .
  }
}
Here is how you would associate a Polygon with a node. Adding a MultiPolygon is also similar. 
{
  set {
    <_:0xf76c276b> <loc>
      "{\"type\":\"Polygon\",\"coordinates\":[[[-122.409869,37.7785442],[-122.4097444,37.7786443],[-122.4097544,37.7786521],[-122.4096334,37.7787494],[-122.4096233,37.7787416],[-122.4094004,37.7789207],[-122.4095818,37.7790617],[-122.4097883,37.7792189],[-122.4102599,37.7788413],[-122.409869,37.7785442]],[[-122.4097357,37.7787848],[-122.4098499,37.778693],[-122.4099025,37.7787339],[-122.4097882,37.7788257],[-122.4097357,37.7787848]]]}"^^<geo:geojson>
      . <_:0xf76c276b> <name> "Best Western Americana Hotel" . <\_:0xf76c276b>
      <dgraph.type> "Location" .
  }
}
The above examples have been picked from our SF Tourism dataset.

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Query

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near

Syntax Example: near(predicate, [long, lat], distance) Schema Types: geo Index Required: geo Matches all entities where the location given by predicate is within distance meters of geojson coordinate [long, lat]. Query Example: Tourist destinations within 1000 meters (1 kilometer) of a point in Golden Gate Park in San Francisco. 
{
  tourist(func: near(loc, [-122.469829, 37.771935], 1000)) { name }
}

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within

Syntax Example: within(predicate, [[[long1, lat1], ..., [longN, latN]]]) Schema Types: geo Index Required: geo Matches all entities where the location given by predicate lies within the polygon specified by the geojson coordinate array. Query Example: Tourist destinations within the specified area of Golden Gate Park, San Francisco. 
{
  tourist(func: within(loc, [[[-122.47266769409178, 37.769018558337926 ], [ -122.47266769409178, 37.773699921075135 ], [ -122.4651575088501, 37.773699921075135 ], [ -122.4651575088501, 37.769018558337926 ], [ -122.47266769409178, 37.769018558337926]]])) {
    name
  }
}

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contains

Syntax Examples: contains(predicate, [long, lat]) or contains(predicate, [[long1, lat1], ..., [longN, latN]]) Schema Types: geo Index Required: geo Matches all entities where the polygon describing the location given by predicate contains geojson coordinate [long, lat] or given geojson polygon. Query Example : All entities that contain a point in the flamingo enclosure of San Francisco Zoo. 
{
  tourist(func: contains(loc, [-122.50326097011566, 37.73353615592843])) {
    name
  }
}

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intersects

Syntax Example: intersects(predicate, [[[long1, lat1], ..., [longN, latN]]]) Schema Types: geo Index Required: geo Matches all entities where the polygon describing the location given by predicate intersects the given geojson polygon. 
{
  tourist(func: intersects(loc, [[[-122.503325343132, 37.73345766902749 ], [ -122.503325343132, 37.733903134117966 ], [ -122.50271648168564, 37.733903134117966 ], [ -122.50271648168564, 37.73345766902749 ], [ -122.503325343132, 37.73345766902749]]])) {
    name
  }
}