The Asterix Query Language, Version 1.0

1. Introduction [Back to TOC]

This document is intended as a reference guide to the full syntax and semantics of the Asterix Query Language (AQL), the language for talking to AsterixDB. This guide covers both the data manipulation language (DML) aspects of AQL, including its support for queries and data modification, as well as its data definition language (DDL) aspects. New AsterixDB users are encouraged to read and work through the (friendlier) guide “AsterixDB 101: An ADM and AQL Primer” before attempting to make use of this document. In addition, readers are advised to read and understand the Asterix Data Model (ADM) reference guide since a basic understanding of ADM concepts is a prerequisite to understanding AQL. In what follows, we detail the features of the AQL language in a grammar-guided manner: We list and briefly explain each of the productions in the AQL grammar, offering examples for clarity in cases where doing so seems needed or helpful.

2. Expressions [Back to TOC]

Query ::= Expression

An AQL query can be any legal AQL expression.

Expression ::= ( OperatorExpr | IfThenElse | FLWOR | QuantifiedExpression )

AQL is a fully composable expression language. Each AQL expression returns zero or more Asterix Data Model (ADM) instances. There are four major kinds of expressions in AQL. At the topmost level, an AQL expression can be an OperatorExpr (similar to a mathematical expression), an IfThenElse (to choose between two alternative values), a FLWOR expression (the heart of AQL, pronounced “flower expression”), or a QuantifiedExpression (which yields a boolean value). Each will be detailed as we explore the full AQL grammar.

Primary Expressions

PrimaryExpr ::= Literal
              | VariableRef
              | ParenthesizedExpression
              | FunctionCallExpr
              | DatasetAccessExpression
              | ListConstructor
              | RecordConstructor

The most basic building block for any AQL expression is the PrimaryExpr. This can be a simple literal (constant) value, a reference to a query variable that is in scope, a parenthesized expression, a function call, an expression accessing the ADM contents of a dataset, a newly constructed list of ADM instances, or a newly constructed ADM record.


Literal        ::= StringLiteral
                 | IntegerLiteral
                 | FloatLiteral
                 | DoubleLiteral
                 | "null"
                 | "true"
                 | "false"
StringLiteral  ::= ("\"" (<ESCAPE_QUOT> | ~["\""])* "\"")
                 | ("\'" (<ESCAPE_APOS> | ~["\'"])* "\'")
<ESCAPE_QUOT>  ::= "\\\""
<ESCAPE_APOS>  ::= "\\\'"
IntegerLiteral ::= <DIGITS>
<DIGITS>       ::= ["0" - "9"]+
FloatLiteral   ::= <DIGITS> ( "f" | "F" )
                 | <DIGITS> ( "." <DIGITS> ( "f" | "F" ) )?
                 | "." <DIGITS> ( "f" | "F" )
DoubleLiteral  ::= <DIGITS>
                 | <DIGITS> ( "." <DIGITS> )?
                 | "." <DIGITS>

Literals (constants) in AQL can be strings, integers, floating point values, double values, boolean constants, or the constant value null. The null value in AQL has “unknown” or “missing” value semantics, similar to (though not identical to) nulls in the relational query language SQL.

The following are some simple examples of AQL literals. Since AQL is an expression language, each example is also a complete, legal AQL query (!).

"a string"

Variable References

VariableRef ::= <VARIABLE>
<VARIABLE>  ::= "$" <LETTER> (<LETTER> | <DIGIT> | "_")*
<LETTER>    ::= ["A" - "Z", "a" - "z"]

A variable in AQL can be bound to any legal ADM value. A variable reference refers to the value to which an in-scope variable is bound. (E.g., a variable binding may originate from one of the for or let clauses of a FLWOR expression or from an input parameter in the context of an AQL function body.)


Parenthesized Expressions

ParenthesizedExpression ::= "(" Expression ")"

As in most languages, an expression may be parenthesized.

Since AQL is an expression language, the following example expression is actually also a complete, legal AQL query whose result is the value 2. (As such, you can have Big Fun explaining to your boss how AsterixDB and AQL can turn your 1000-node shared-nothing Big Data cluster into a $5M calculator in its spare time.)

( 1 + 1 )

Function Calls

FunctionCallExpr ::= FunctionOrTypeName "(" ( Expression ( "," Expression )* )? ")"

Functions are included in AQL, like most languages, as a way to package useful functionality or to componentize complicated or reusable AQL computations. A function call is a legal AQL query expression that represents the ADM value resulting from the evaluation of its body expression with the given parameter bindings; the parameter value bindings can themselves be any AQL expressions.

The following example is a (built-in) function call expression whose value is 8.

string-length("a string")

Dataset Access

DatasetAccessExpression ::= "dataset" ( ( Identifier ( "." Identifier )? )
                          | ( "(" Expression ")" ) )
Identifier              ::= <IDENTIFIER> | StringLiteral
<SPECIALCHARS>          ::= ["$", "_", "-"]

Querying Big Data is the main point of AsterixDB and AQL. Data in AsterixDB reside in datasets (collections of ADM records), each of which in turn resides in some namespace known as a dataverse (data universe). Data access in a query expression is accomplished via a DatasetAccessExpression. Dataset access expressions are most commonly used in FLWOR expressions, where variables are bound to their contents.

Note that the Identifier that identifies a dataset (or any other Identifier in AQL) can also be a StringLiteral. This is especially useful to avoid conficts with AQL keywords (e.g. “dataset”, “null”, or “type”).

The following are three examples of legal dataset access expressions. The first one accesses a dataset called Customers in the dataverse called SalesDV. The second one accesses the Customers dataverse in whatever the current dataverse is. The third one does the same thing as the second but uses a slightly older AQL syntax.

dataset SalesDV.Customers
dataset Customers


ListConstructor          ::= ( OrderedListConstructor | UnorderedListConstructor )
OrderedListConstructor   ::= "[" ( Expression ( "," Expression )* )? "]"
UnorderedListConstructor ::= "{{" ( Expression ( "," Expression )* )? "}}"
RecordConstructor        ::= "{" ( FieldBinding ( "," FieldBinding )* )? "}"
FieldBinding             ::= Expression ":" Expression

A major feature of AQL is its ability to construct new ADM data instances. This is accomplished using its constructors for each of the major ADM complex object structures, namely lists (ordered or unordered) and records. Ordered lists are like JSON arrays, while unordered lists have bag (multiset) semantics. Records are built from attributes that are field-name/field-value pairs, again like JSON. (See the AsterixDB Data Model document for more details on each.)

The following examples illustrate how to construct a new ordered list with 3 items, a new unordered list with 4 items, and a new record with 2 fields, respectively. List elements can be homogeneous (as in the first example), which is the common case, or they may be heterogeneous (as in the second example). The data values and field name values used to construct lists and records in constructors are all simply AQL expressions. Thus the list elements, field names, and field values used in constructors can be simple literals (as in these three examples) or they can come from query variable references or even arbitrarily complex AQL expressions.

[ "a", "b", "c" ]

{{ 42, "forty-two", "AsterixDB!", 3.14f }}

  "project name": "AsterixDB"
  "project members": {{ "vinayakb", "dtabass", "chenli" }}

When constructing nested records there needs to be a space between the closing braces to avoid confusion with the }} token that ends an unordered list constructor: { "a" : { "b" : "c" }} will fail to parse while { "a" : { "b" : "c" } } will work.

Path Expressions

ValueExpr ::= PrimaryExpr ( Field | Index )*
Field     ::= "." Identifier
Index     ::= "[" ( Expression | "?" ) "]"

Components of complex types in ADM are accessed via path expressions. Path access can be applied to the result of an AQL expression that yields an instance of such a type, e.g., a record or list instance. For records, path access is based on field names. For ordered lists, path access is based on (zero-based) array-style indexing. AQL also supports an “I’m feeling lucky” style index accessor, [?], for selecting an arbitrary element from an ordered list. Attempts to access non-existent fields or list elements produce a null (i.e., missing information) result as opposed to signaling a runtime error.

The following examples illustrate field access for a record, index-based element access for an ordered list, and also a composition thereof.

({"list": [ "a", "b", "c"]}).list

(["a", "b", "c"])[2]

({ "list": [ "a", "b", "c"]}).list[2]

Logical Expressions

OperatorExpr ::= AndExpr ( "or" AndExpr )*
AndExpr      ::= RelExpr ( "and" RelExpr )*

As in most languages, boolean expressions can be built up from smaller expressions by combining them with the logical connectives and/or. Legal boolean values in AQL are true, false, and null. (Nulls in AQL are treated much like SQL treats its unknown truth value in boolean expressions.)

The following is an example of a conjuctive range predicate in AQL. It will yield true if $a is bound to 4, null if $a is bound to null, and false otherwise.

$a > 3 and $a < 5

Comparison Expressions

RelExpr ::= AddExpr ( ( "<" | ">" | "<=" | ">=" | "=" | "!=" | "~=" ) AddExpr )?

AQL has the usual list of suspects, plus one, for comparing pairs of atomic values. The “plus one” is the last operator listed above, which is the “roughly equal” operator provided for similarity queries. (See the separate document on AsterixDB Similarity Queries for more details on similarity matching.)

An example comparison expression (which yields the boolean value true) is shown below.

5 > 3

Arithmetic Expressions

AddExpr  ::= MultExpr ( ( "+" | "-" ) MultExpr )*
MultExpr ::= UnaryExpr ( ( "*" | "/" | "%" | "^"| "idiv" ) UnaryExpr )*
UnaryExpr ::= ( ( "+" | "-" ) )? ValueExpr

AQL also supports the usual cast of characters for arithmetic expressions. The example below evaluates to 25.

3 ^ 2 + 4 ^ 2

FLWOR Expression

FLWOR         ::= ( ForClause | LetClause ) ( Clause )* ("return"|"select") Expression
Clause         ::= ForClause | LetClause | WhereClause | OrderbyClause
                 | GroupClause | LimitClause | DistinctClause
ForClause      ::= ("for"|"from") Variable ( "at" Variable )? "in" ( Expression )
LetClause      ::= ("let"|"with") Variable ":=" Expression
WhereClause    ::= "where" Expression
OrderbyClause  ::= "order" "by" Expression ( ( "asc" ) | ( "desc" ) )?
                   ( "," Expression ( ( "asc" ) | ( "desc" ) )? )*
GroupClause    ::= "group" "by" ( Variable ":=" )? Expression ( "," ( Variable ":=" )? Expression )*
                   ("with"|"keeping") VariableRef ( "," VariableRef )*
LimitClause    ::= "limit" Expression ( "offset" Expression )?
DistinctClause ::= "distinct" "by" Expression ( "," Expression )*
Variable       ::= <VARIABLE>

The heart of AQL is the FLWOR (for-let-where-orderby-return) expression. The roots of this expression were borrowed from the expression of the same name in XQuery. A FLWOR expression starts with one or more clauses that establish variable bindings. A for clause binds a variable incrementally to each element of its associated expression; it includes an optional positional variable for counting/numbering the bindings. By default no ordering is implied or assumed by a for clause. A let clause binds a variable to the collection of elements computed by its associated expression.

Following the initial for or let clause(s), a FLWOR expression may contain an arbitrary sequence of other clauses. The where clause in a FLWOR expression filters the preceding bindings via a boolean expression, much like a where clause does in a SQL query. The order by clause in a FLWOR expression induces an ordering on the data. The group by clause, discussed further below, forms groups based on its group by expressions, optionally naming the expressions’ values (which together form the grouping key for the expression). The with subclause of a group by clause specifies the variable(s) whose values should be grouped based on the grouping key(s); following the grouping clause, only the grouping key(s) and the variables named in the with subclause remain in scope, and the named grouping variables now contain lists formed from their input values. The limit clause caps the number of values returned, optionally starting its result count from a specified offset. (Web applications can use this feature for doing pagination.) The distinct clause is similar to the group-by clause, but it forms no groups; it serves only to eliminate duplicate values. As indicated by the grammar, the clauses in an AQL query can appear in any order. To interpret a query, one can think of data as flowing down through the query from the first clause to the return clause.

The following example shows a FLWOR expression that selects and returns one user from the dataset FacebookUsers.

for $user in dataset FacebookUsers
where $ = 8
return $user

The next example shows a FLWOR expression that joins two datasets, FacebookUsers and FacebookMessages, returning user/message pairs. The results contain one record per pair, with result records containing the user’s name and an entire message.

for $user in dataset FacebookUsers
for $message in dataset FacebookMessages
where $ = $
    "uname": $,
    "message": $message.message

In the next example, a let clause is used to bind a variable to all of a user’s FacebookMessages. The query returns one record per user, with result records containing the user’s name and the set of all messages by that user.

for $user in dataset FacebookUsers
let $messages :=
  for $message in dataset FacebookMessages
  where $ = $
  return $message.message
    "uname": $,
    "messages": $messages

The following example returns all TwitterUsers ordered by their followers count (most followers first) and language. When ordering null is treated as being smaller than any other value if nulls are encountered in the ordering key(s).

  for $user in dataset TwitterUsers
  order by $user.followers_count desc, $user.lang asc
  return $user

The next example illustrates the use of the group by clause in AQL. After the group by clause in the query, only variables that are either in the group by list or in the with list are in scope. The variables in the clause’s with list will each contain a collection of items following the group by clause; the collected items are the values that the source variable was bound to in the tuples that formed the group. For grouping null is handled as a single value.

  for $x in dataset FacebookMessages
  let $messages := $x.message
  group by $loc := $x.sender-location with $messages
      "location" : $loc,
      "message" : $messages

The use of the limit clause is illustrated in the next example.

  for $user in dataset TwitterUsers
  order by $user.followers_count desc
  limit 2
  return $user

The final example shows how AQL’s distinct by clause works. Each variable in scope before the distinct clause is also in scope after the distinct by clause. This clause works similarly to group by, but for each variable that contains more than one value after the distinct by clause, one value is picked nondeterministically. (If the variable is in the distinct by list, then its value will be deterministic.) Nulls are treated as a single value when they occur in a grouping field.

  for $x in dataset FacebookMessages
  distinct by $x.sender-location
      "location" : $x.sender-location,
      "message" : $x.message

In order to allow SQL fans to write queries in their favored ways, AQL provides synonyms: from for for, select for return, with for let, and keeping for with in the group by clause. The following query is such an example.

  from $x in dataset FacebookMessages
  with $messages := $x.message
  group by $loc := $x.sender-location keeping $messages
      "location" : $loc,
      "message" : $messages

Conditional Expression

IfThenElse ::= "if" "(" Expression ")" "then" Expression "else" Expression

A conditional expression is useful for choosing between two alternative values based on a boolean condition. If its first (if) expression is true, its second (then) expression’s value is returned, and otherwise its third (else) expression is returned.

The following example illustrates the form of a conditional expression.

if (2 < 3) then "yes" else "no"

Quantified Expressions

QuantifiedExpression ::= ( ( "some" ) | ( "every" ) ) Variable "in" Expression
                         ( "," Variable "in" Expression )* "satisfies" Expression

Quantified expressions are used for expressing existential or universal predicates involving the elements of a collection.

The following pair of examples illustrate the use of a quantified expression to test that every (or some) element in the set [1, 2, 3] of integers is less than three. The first example yields false and second example yields true.

It is useful to note that if the set were instead the empty set, the first expression would yield true (“every” value in an empty set satisfies the condition) while the second expression would yield false (since there isn’t “some” value, as there are no values in the set, that satisfies the condition).

every $x in [ 1, 2, 3 ] satisfies $x < 3
some $x in [ 1, 2, 3 ] satisfies $x < 3

3. Statements [Back to TOC]

Statement ::= ( SingleStatement ( ";" )? )* <EOF>
SingleStatement ::= DataverseDeclaration
                  | FunctionDeclaration
                  | CreateStatement
                  | DropStatement
                  | LoadStatement
                  | SetStatement
                  | InsertStatement
                  | DeleteStatement
                  | Query

In addition to expresssions for queries, AQL supports a variety of statements for data definition and manipulation purposes as well as controlling the context to be used in evaluating AQL expressions. AQL supports record-level ACID transactions that begin and terminate implicitly for each record inserted, deleted, or searched while a given AQL statement is being executed.

This section details the statements supported in the AQL language.


DataverseDeclaration ::= "use" "dataverse" Identifier

The world of data in an AsterixDB cluster is organized into data namespaces called dataverses. To set the default dataverse for a series of statements, the use dataverse statement is provided.

As an example, the following statement sets the default dataverse to be TinySocial.

use dataverse TinySocial;

The set statement in AQL is used to control aspects of the expression evalation context for queries.

SetStatement ::= "set" Identifier StringLiteral

As an example, the following set statements request that Jaccard similarity with a similarity threshold 0.6 be used for set similarity matching when the ~= operator is used in a query expression.

set simfunction "jaccard";
set simthreshold "0.6f";

When writing a complex AQL query, it can sometimes be helpful to define one or more auxilliary functions that each address a sub-piece of the overall query. The declare function statement supports the creation of such helper functions.

FunctionDeclaration  ::= "declare" "function" Identifier ParameterList "{" Expression "}"
ParameterList        ::= "(" ( <VARIABLE> ( "," <VARIABLE> )* )? ")"

The following is a very simple example of a temporary AQL function definition.

declare function add($a, $b) {
  $a + $b

Lifecycle Management Statements

CreateStatement ::= "create" ( DataverseSpecification
                             | TypeSpecification
                             | DatasetSpecification
                             | IndexSpecification
                             | FunctionSpecification )

QualifiedName       ::= Identifier ( "." Identifier )?
DoubleQualifiedName ::= Identifier "." Identifier ( "." Identifier )?

The create statement in AQL is used for creating persistent artifacts in the context of dataverses. It can be used to create new dataverses, datatypes, datasets, indexes, and user-defined AQL functions.


DataverseSpecification ::= "dataverse" Identifier IfNotExists ( "with format" StringLiteral )?

The create dataverse statement is used to create new dataverses. To ease the authoring of reusable AQL scripts, its optional IfNotExists clause allows creation to be requested either unconditionally or only if the the dataverse does not already exist. If this clause is absent, an error will be returned if the specified dataverse already exists. The with format clause is a placeholder for future functionality that can safely be ignored.

The following example creates a dataverse named TinySocial.

create dataverse TinySocial;


TypeSpecification    ::= "type" FunctionOrTypeName IfNotExists "as" TypeExpr
FunctionOrTypeName   ::= QualifiedName
IfNotExists          ::= ( "if not exists" )?
TypeExpr             ::= RecordTypeDef | TypeReference | OrderedListTypeDef | UnorderedListTypeDef
RecordTypeDef        ::= ( "closed" | "open" )? "{" ( RecordField ( "," RecordField )* )? "}"
RecordField          ::= Identifier ":" ( TypeExpr ) ( "?" )?
NestedField          ::= Identifier ( "." Identifier )*
IndexField           ::= NestedField ( ":" TypeReference )?
TypeReference        ::= Identifier
OrderedListTypeDef   ::= "[" ( TypeExpr ) "]"
UnorderedListTypeDef ::= "{{" ( TypeExpr ) "}}"

The create type statement is used to create a new named ADM datatype. This type can then be used to create datasets or utilized when defining one or more other ADM datatypes. Much more information about the Asterix Data Model (ADM) is available in the data model reference guide to ADM. A new type can be a record type, a renaming of another type, an ordered list type, or an unordered list type. A record type can be defined as being either open or closed. Instances of a closed record type are not permitted to contain fields other than those specified in the create type statement. Instances of an open record type may carry additional fields, and open is the default for a new type (if neither option is specified).

The following example creates a new ADM record type called FacebookUser type. Since it is closed, its instances will contain only what is specified in the type definition. The first four fields are traditional typed name/value pairs. The friend-ids field is an unordered list of 32-bit integers. The employment field is an ordered list of instances of another named record type, EmploymentType.

create type FacebookUserType as closed {
  "id" :         int32,
  "alias" :      string,
  "name" :       string,
  "user-since" : datetime,
  "friend-ids" : {{ int32 }},
  "employment" : [ EmploymentType ]

The next example creates a new ADM record type called FbUserType. Note that the type of the id field is UUID. You need to use this field type if you want to have this field be an autogenerated-PK field. Refer to the Datasets section later for more details.

create type FbUserType as closed {
  "id" :         uuid,
  "alias" :      string,
  "name" :       string


DatasetSpecification ::= "internal"? "dataset" QualifiedName "(" Identifier ")" IfNotExists
                         PrimaryKey ( "on" Identifier )? ( "hints" Properties )?
                         ( "using" "compaction" "policy" CompactionPolicy ( Configuration )? )?
                         ( "with filter on" Identifier )?
                       | "external" "dataset" QualifiedName "(" Identifier ")" IfNotExists
                         "using" AdapterName Configuration ( "hints" Properties )?
                         ( "using" "compaction" "policy" CompactionPolicy ( Configuration )? )?
AdapterName          ::= Identifier
Configuration        ::= "(" ( KeyValuePair ( "," KeyValuePair )* )? ")"
KeyValuePair         ::= "(" StringLiteral "=" StringLiteral ")"
Properties           ::= ( "(" Property ( "," Property )* ")" )?
Property             ::= Identifier "=" ( StringLiteral | IntegerLiteral )
FunctionSignature    ::= FunctionOrTypeName "@" IntegerLiteral
PrimaryKey           ::= "primary" "key" NestedField ( "," NestedField )* ( "autogenerated ")?
CompactionPolicy     ::= Identifier
PrimaryKey           ::= "primary" "key" Identifier ( "," Identifier )* ( "autogenerated ")?

The create dataset statement is used to create a new dataset. Datasets are named, unordered collections of ADM record instances; they are where data lives persistently and are the targets for queries in AsterixDB. Datasets are typed, and AsterixDB will ensure that their contents conform to their type definitions. An Internal dataset (the default) is a dataset that is stored in and managed by AsterixDB. It must have a specified unique primary key that can be used to partition data across nodes of an AsterixDB cluster. The primary key is also used in secondary indexes to uniquely identify the indexed primary data records. Random primary key (UUID) values can be auto-generated by declaring the field to be UUID and putting “autogenerated” after the “primary key” identifier. In this case, values for the auto-generated PK field should not be provided by the user since it will be auto-generated by AsterixDB. Optionally, a filter can be created on a field to further optimize range queries with predicates on the filter’s field. (Refer to Filter-Based LSM Index Acceleration for more information about filters.)

An External dataset is stored outside of AsterixDB (currently datasets in HDFS or on the local filesystem(s) of the cluster’s nodes are supported). External dataset support allows AQL queries to treat external data as though it were stored in AsterixDB, making it possible to query “legacy” file data (e.g., Hive data) without having to physically import it into AsterixDB. For an external dataset, an appropriate adapter must be selected to handle the nature of the desired external data. (See the guide to external data for more information on the available adapters.)

When creating a dataset, it is possible to choose a merge policy that controls which of the underlaying LSM storage components to be merged. Currently, AsterixDB provides four different merge policies that can be configured per dataset: no-merge, constant, prefix, and correlated-prefix. The no-merge policy simply never merges disk components. While the constant policy merges disk components when the number of components reaches some constant number k, which can be configured by the user. The prefix policy relies on component sizes and the number of components to decide which components to merge. Specifically, it works by first trying to identify the smallest ordered (oldest to newest) sequence of components such that the sequence does not contain a single component that exceeds some threshold size M and that either the sum of the component’s sizes exceeds M or the number of components in the sequence exceeds another threshold C. If such a sequence of components exists, then each of the components in the sequence are merged together to form a single component. Finally, the correlated-prefix is similar to the prefix policy but it delegates the decision of merging the disk components of all the indexes in a dataset to the primary index. When the policy decides that the primary index needs to be merged (using the same decision criteria as for the prefix policy), then it will issue successive merge requests on behalf of all other indexes associated with the same dataset. The default policy for AsterixDB is the prefix policy except when there is a filter on a dataset, where the preferred policy for filters is the correlated-prefix.

The following example creates an internal dataset for storing FacefookUserType records. It specifies that their id field is their primary key.

create internal dataset FacebookUsers(FacebookUserType) primary key id;

The following example creates an internal dataset for storing FbUserType records. It specifies that their id field is their primary key. It also specifies that the id field is an auto-generated field, meaning that a randomly generated UUID value will be assigned to each record by the system. (A user should therefore not proivde a value for this field.) Note that the id field should be UUID.

create internal dataset FbMsgs(FbUserType) primary key id autogenerated;

The next example creates an external dataset for storing LineitemType records. The choice of the hdfs adapter means that its data will reside in HDFS. The create statement provides parameters used by the hdfs adapter: the URL and path needed to locate the data in HDFS and a description of the data format.

create external dataset Lineitem('LineitemType) using hdfs (


IndexSpecification ::= "index" Identifier IfNotExists "on" QualifiedName
                       "(" ( IndexField ) ( "," IndexField )* ")" ( "type" IndexType )? ( "enforced" )?
IndexType          ::= "btree"
                     | "rtree"
                     | "keyword"
                     | "ngram" "(" IntegerLiteral ")"

The create index statement creates a secondary index on one or more fields of a specified dataset. Supported index types include btree for totally ordered datatypes, rtree for spatial data, and keyword and ngram for textual (string) data. An index can be created on a nested field (or fields) by providing a valid path expression as an index field identifier. An index field is not required to be part of the datatype associated with a dataset if that datatype is declared as open and the field’s type is provided along with its type and the enforced keyword is specified in the end of index definition. Enforcing an open field will introduce a check that will make sure that the actual type of an indexed field (if the field exists in the record) always matches this specified (open) field type.

The following example creates a btree index called fbAuthorIdx on the author-id field of the FacebookMessages dataset. This index can be useful for accelerating exact-match queries, range search queries, and joins involving the author-id field.

create index fbAuthorIdx on FacebookMessages(author-id) type btree;

The following example creates an open btree index called fbSendTimeIdx on the open send-time field of the FacebookMessages dataset having datetime type. This index can be useful for accelerating exact-match queries, range search queries, and joins involving the send-time field.

create index fbSendTimeIdx on FacebookMessages(send-time:datetime) type btree enforced;

The following example creates a btree index called twUserScrNameIdx on the screen-name field, which is a nested field of the user field in the TweetMessages dataset. This index can be useful for accelerating exact-match queries, range search queries, and joins involving the screen-name field.

create index twUserScrNameIdx on TweetMessages(user.screen-name) type btree;

The following example creates an rtree index called fbSenderLocIdx on the sender-location field of the FacebookMessages dataset. This index can be useful for accelerating queries that use the spatial-intersect function in a predicate involving the sender-location field.

create index fbSenderLocIndex on FacebookMessages(sender-location) type rtree;

The following example creates a 3-gram index called fbUserIdx on the name field of the FacebookUsers dataset. This index can be used to accelerate some similarity or substring maching queries on the name field. For details refer to the document on similarity queries.

create index fbUserIdx on FacebookUsers(name) type ngram(3);

The following example creates a keyword index called fbMessageIdx on the message field of the FacebookMessages dataset. This keyword index can be used to optimize queries with token-based similarity predicates on the message field. For details refer to the document on similarity queries.

create index fbMessageIdx on FacebookMessages(message) type keyword;


The create function statement creates a named function that can then be used and reused in AQL queries. The body of a function can be any AQL expression involving the function’s parameters.

FunctionSpecification ::= "function" FunctionOrTypeName IfNotExists ParameterList "{" Expression "}"

The following is a very simple example of a create function statement. It differs from the declare function example shown previously in that it results in a function that is persistently registered by name in the specified dataverse.

create function add($a, $b) {
  $a + $b


DropStatement       ::= "drop" ( "dataverse" Identifier IfExists
                               | "type" FunctionOrTypeName IfExists
                               | "dataset" QualifiedName IfExists
                               | "index" DoubleQualifiedName IfExists
                               | "function" FunctionSignature IfExists )
IfExists            ::= ( "if" "exists" )?

The drop statement in AQL is the inverse of the create statement. It can be used to drop dataverses, datatypes, datasets, indexes, and functions.

The following examples illustrate uses of the drop statement.

drop dataset FacebookUsers if exists;

drop index FacebookUsers.fbSenderLocIndex;

drop type FacebookUserType;

drop dataverse TinySocial;

drop function add;

Import/Export Statements

LoadStatement  ::= "load" "dataset" QualifiedName "using" AdapterName Configuration ( "pre-sorted" )?

The load statement is used to initially populate a dataset via bulk loading of data from an external file. An appropriate adapter must be selected to handle the nature of the desired external data. The load statement accepts the same adapters and the same parameters as external datasets. (See the guide to external data for more information on the available adapters.) If a dataset has an auto-generated primary key field, a file to be imported should not include that field in it.

The following example shows how to bulk load the FacebookUsers dataset from an external file containing data that has been prepared in ADM format.

load dataset FacebookUsers using localfs

Modification Statements


InsertStatement ::= "insert" "into" "dataset" QualifiedName Query

The AQL insert statement is used to insert data into a dataset. The data to be inserted comes from an AQL query expression. The expression can be as simple as a constant expression, or in general it can be any legal AQL query. Inserts in AsterixDB are processed transactionally, with the scope of each insert transaction being the insertion of a single object plus its affiliated secondary index entries (if any). If the query part of an insert returns a single object, then the insert statement itself will be a single, atomic transaction. If the query part returns multiple objects, then each object inserted will be handled independently as a tranaction. If a dataset has an auto-generated primary key field, an insert statement should not include a value for that field in it. (The system will automatically extend the provided record with this additional field and a corresponding value.)

The following example illustrates a query-based insertion.

insert into dataset UsersCopy (for $user in dataset FacebookUsers return $user)


DeleteStatement ::= "delete" Variable "from" "dataset" QualifiedName ( "where" Expression )?

The AQL delete statement is used to delete data from a target dataset. The data to be deleted is identified by a boolean expression involving the variable bound to the target dataset in the delete statement. Deletes in AsterixDB are processed transactionally, with the scope of each delete transaction being the deletion of a single object plus its affiliated secondary index entries (if any). If the boolean expression for a delete identifies a single object, then the delete statement itself will be a single, atomic transaction. If the expression identifies multiple objects, then each object deleted will be handled independently as a transaction.

The following example illustrates a single-object deletion.

delete $user from dataset FacebookUsers where $ = 8;

We close this guide to AQL with one final example of a query expression.

for $praise in {{ "great", "brilliant", "awesome" }}
   string-concat(["AsterixDB is ", $praise])