Queries

In this section, all the examples assume that you are using a dataverse called TinySocial.

You can use the USE statement to set the default dataverse for the statement immediately following.

Example
USE TinySocial;

SELECT Statements

The following shows the (rich) grammar for the SELECT statement in the query language.

SelectStatement    ::= ( WithClause )?
                       SelectSetOperation (OrderbyClause )? ( LimitClause )?
SelectSetOperation ::= SelectBlock (<UNION> <ALL> ( SelectBlock | Subquery ) )*
Subquery           ::= "(" SelectStatement ")"

SelectBlock        ::= SelectClause
                       ( FromClause ( LetClause )?)?
                       ( WhereClause )?
                       ( GroupbyClause ( LetClause )? ( HavingClause )? )?
                       |
                       FromClause ( LetClause )?
                       ( WhereClause )?
                       ( GroupbyClause ( LetClause )? ( HavingClause )? )?
                       SelectClause

SelectClause       ::= <SELECT> ( <ALL> | <DISTINCT> )? ( SelectRegular | SelectValue )
SelectRegular      ::= Projection ( "," Projection )*
SelectValue        ::= ( <VALUE> | <ELEMENT> | <RAW> ) Expression
Projection         ::= ( Expression ( <AS> )? Identifier | "*" | Identifier "." "*" )

FromClause         ::= <FROM> FromTerm ( "," FromTerm )*
FromTerm           ::= Expression (( <AS> )? Variable)?
                       ( ( JoinType )? ( JoinClause | UnnestClause ) )*

JoinClause         ::= <JOIN> Expression (( <AS> )? Variable)? <ON> Expression
UnnestClause       ::= ( <UNNEST> ) Expression
                       ( <AS> )? Variable ( <AT> Variable )?
JoinType           ::= ( <INNER> | <LEFT> ( <OUTER> )? )

WithClause         ::= <WITH> WithElement ( "," WithElement )*
LetClause          ::= (<LET> | <LETTING>) LetElement ( "," LetElement )*
LetElement         ::= Variable "=" Expression
WithElement        ::= Variable <AS> Expression

WhereClause        ::= <WHERE> Expression

GroupbyClause      ::= <GROUP> <BY> Expression ( ( (<AS>)? Variable )?
                       ( "," Expression ( (<AS>)? Variable )? )* )
                       ( <GROUP> <AS> Variable
                         ("(" VariableReference <AS> Identifier
                         ("," VariableReference <AS> Identifier )* ")")?
                       )?
HavingClause       ::= <HAVING> Expression

OrderbyClause      ::= <ORDER> <BY> Expression ( <ASC> | <DESC> )?
                       ( "," Expression ( <ASC> | <DESC> )? )*
LimitClause        ::= <LIMIT> Expression ( <OFFSET> Expression )?

In this section, we will make use of two stored collections of objects (datasets), GleambookUsers and GleambookMessages, in a series of running examples to explain SELECT queries. The contents of the example collections are as follows:

GleambookUsers collection (or, dataset):

[ {
  "id":1,
  "alias":"Margarita",
  "name":"MargaritaStoddard",
  "nickname":"Mags",
  "userSince":"2012-08-20T10:10:00",
  "friendIds":[2,3,6,10],
  "employment":[{
                  "organizationName":"Codetechno",
                  "start-date":"2006-08-06"
                },
                {
                  "organizationName":"geomedia",
                  "start-date":"2010-06-17",
                  "end-date":"2010-01-26"
                }],
  "gender":"F"
},
{
  "id":2,
  "alias":"Isbel",
  "name":"IsbelDull",
  "nickname":"Izzy",
  "userSince":"2011-01-22T10:10:00",
  "friendIds":[1,4],
  "employment":[{
                  "organizationName":"Hexviafind",
                  "startDate":"2010-04-27"
               }]
},
{
  "id":3,
  "alias":"Emory",
  "name":"EmoryUnk",
  "userSince":"2012-07-10T10:10:00",
  "friendIds":[1,5,8,9],
  "employment":[{
                  "organizationName":"geomedia",
                  "startDate":"2010-06-17",
                  "endDate":"2010-01-26"
               }]
} ]

GleambookMessages collection (or, dataset):

[ {
  "messageId":2,
  "authorId":1,
  "inResponseTo":4,
  "senderLocation":[41.66,80.87],
  "message":" dislike x-phone its touch-screen is horrible"
},
{
  "messageId":3,
  "authorId":2,
  "inResponseTo":4,
  "senderLocation":[48.09,81.01],
  "message":" like product-y the plan is amazing"
},
{
  "messageId":4,
  "authorId":1,
  "inResponseTo":2,
  "senderLocation":[37.73,97.04],
  "message":" can't stand acast the network is horrible:("
},
{
  "messageId":6,
  "authorId":2,
  "inResponseTo":1,
  "senderLocation":[31.5,75.56],
  "message":" like product-z its platform is mind-blowing"
}
{
  "messageId":8,
  "authorId":1,
  "inResponseTo":11,
  "senderLocation":[40.33,80.87],
  "message":" like ccast the 3G is awesome:)"
},
{
  "messageId":10,
  "authorId":1,
  "inResponseTo":12,
  "senderLocation":[42.5,70.01],
  "message":" can't stand product-w the touch-screen is terrible"
},
{
  "messageId":11,
  "authorId":1,
  "inResponseTo":1,
  "senderLocation":[38.97,77.49],
  "message":" can't stand acast its plan is terrible"
} ]

SELECT Clause

The SELECT clause always returns a collection value as its result (even if the result is empty or a singleton).

Select Element/Value/Raw

The SELECT VALUE clause returns an array or multiset that contains the results of evaluating the VALUE expression, with one evaluation being performed per "binding tuple" (i.e., per FROM clause item) satisfying the statement’s selection criteria. For historical reasons the query language also allows the keywords ELEMENT or RAW to be used in place of VALUE (not recommended).

If there is no FROM clause, the expression after VALUE is evaluated once with no binding tuples (except those inherited from an outer environment).

Example
SELECT VALUE 1;

This query returns:

[
  1
]

The following example shows a query that selects one user from the GleambookUsers collection.

Example
SELECT VALUE user
FROM GleambookUsers user
WHERE user.id = 1;

This query returns:

[{
    "userSince": "2012-08-20T10:10:00.000Z",
    "friendIds": [
        2,
        3,
        6,
        10
    ],
    "gender": "F",
    "name": "MargaritaStoddard",
    "nickname": "Mags",
    "alias": "Margarita",
    "id": 1,
    "employment": [
        {
            "organizationName": "Codetechno",
            "start-date": "2006-08-06"
        },
        {
            "end-date": "2010-01-26",
            "organizationName": "geomedia",
            "start-date": "2010-06-17"
        }
    ]
} ]

SQL-style SELECT

The traditional SQL-style SELECT syntax is also supported in the query language. This syntax can also be reformulated in a SELECT VALUE based manner. (E.g., SELECT expA AS fldA, expB AS fldB is syntactic sugar for SELECT VALUE { 'fldA': expA, 'fldB': expB }.) Unlike in SQL, the result of a query does not preserve the order of expressions in the SELECT clause.

Example
SELECT user.alias user_alias, user.name user_name
FROM GleambookUsers user
WHERE user.id = 1;

Returns:

[ {
    "user_name": "MargaritaStoddard",
    "user_alias": "Margarita"
} ]

SELECT *

SELECT * returns an object with a nested field for each input tuple. Each field has as its field name the name of a binding variable generated by either the FROM clause or GROUP BY clause in the current enclosing SELECT statement, and its field value is the value of that binding variable.

Note that the result of SELECT * is different from the result of query that selects all the fields of an object.

Example
SELECT *
FROM GleambookUsers user;

Since user is the only binding variable generated in the FROM clause, this query returns:

[ {
    "user": {
        "userSince": "2012-08-20T10:10:00.000Z",
        "friendIds": [
            2,
            3,
            6,
            10
        ],
        "gender": "F",
        "name": "MargaritaStoddard",
        "nickname": "Mags",
        "alias": "Margarita",
        "id": 1,
        "employment": [
            {
                "organizationName": "Codetechno",
                "start-date": "2006-08-06"
            },
            {
                "end-date": "2010-01-26",
                "organizationName": "geomedia",
                "start-date": "2010-06-17"
            }
        ]
    }
}, {
    "user": {
        "userSince": "2011-01-22T10:10:00.000Z",
        "friendIds": [
            1,
            4
        ],
        "name": "IsbelDull",
        "nickname": "Izzy",
        "alias": "Isbel",
        "id": 2,
        "employment": [
            {
                "organizationName": "Hexviafind",
                "startDate": "2010-04-27"
            }
        ]
    }
}, {
    "user": {
        "userSince": "2012-07-10T10:10:00.000Z",
        "friendIds": [
            1,
            5,
            8,
            9
        ],
        "name": "EmoryUnk",
        "alias": "Emory",
        "id": 3,
        "employment": [
            {
                "organizationName": "geomedia",
                "endDate": "2010-01-26",
                "startDate": "2010-06-17"
            }
        ]
    }
} ]
Example
SELECT *
FROM GleambookUsers u, GleambookMessages m
WHERE m.authorId = u.id and u.id = 2;

This query does an inner join that we will discuss in multiple from terms. Since both u and m are binding variables generated in the FROM clause, this query returns:

[ {
    "u": {
        "userSince": "2011-01-22T10:10:00",
        "friendIds": [
            1,
            4
        ],
        "name": "IsbelDull",
        "nickname": "Izzy",
        "alias": "Isbel",
        "id": 2,
        "employment": [
            {
                "organizationName": "Hexviafind",
                "startDate": "2010-04-27"
            }
        ]
    },
    "m": {
        "senderLocation": [
            31.5,
            75.56
        ],
        "inResponseTo": 1,
        "messageId": 6,
        "authorId": 2,
        "message": " like product-z its platform is mind-blowing"
    }
}, {
    "u": {
        "userSince": "2011-01-22T10:10:00",
        "friendIds": [
            1,
            4
        ],
        "name": "IsbelDull",
        "nickname": "Izzy",
        "alias": "Isbel",
        "id": 2,
        "employment": [
            {
                "organizationName": "Hexviafind",
                "startDate": "2010-04-27"
            }
        ]
    },
    "m": {
        "senderLocation": [
            48.09,
            81.01
        ],
        "inResponseTo": 4,
        "messageId": 3,
        "authorId": 2,
        "message": " like product-y the plan is amazing"
    }
} ]

SELECT variable.*

Whereas SELECT returns all the fields bound to all the variables which are currently defined, the notation SELECT c. returns all the fields of the object bound to variable c. The variable c must be bound to an object for this to work.

Example
SELECT user.*
FROM GleambookUsers user;

Compare this query with the first example given under SELECT *. This query returns all users from the GleambookUsers dataset, but the user variable name is omitted from the results:

[
  {
    "id": 1,
    "alias": "Margarita",
    "name": "MargaritaStoddard",
    "nickname": "Mags",
    "userSince": "2012-08-20T10:10:00",
    "friendIds": [
      2,
      3,
      6,
      10
    ],
    "employment": [
      {
        "organizationName": "Codetechno",
        "start-date": "2006-08-06"
      },
      {
        "organizationName": "geomedia",
        "start-date": "2010-06-17",
        "end-date": "2010-01-26"
      }
    ],
    "gender": "F"
  },
  {
    "id": 2,
    "alias": "Isbel",
    "name": "IsbelDull",
    "nickname": "Izzy",
    "userSince": "2011-01-22T10:10:00",
    "friendIds": [
      1,
      4
    ],
    "employment": [
      {
        "organizationName": "Hexviafind",
        "startDate": "2010-04-27"
      }
    ]
  },
  {
    "id": 3,
    "alias": "Emory",
    "name": "EmoryUnk",
    "userSince": "2012-07-10T10:10:00",
    "friendIds": [
      1,
      5,
      8,
      9
    ],
    "employment": [
      {
        "organizationName": "geomedia",
        "startDate": "2010-06-17",
        "endDate": "2010-01-26"
      }
    ]
  }
]

SELECT DISTINCT

The DISTINCT keyword is used to eliminate duplicate items in results. The following example shows how it works.

Example
SELECT DISTINCT * FROM [1, 2, 2, 3] AS foo;

This query returns:

[ {
    "foo": 1
}, {
    "foo": 2
}, {
    "foo": 3
} ]
Example
SELECT DISTINCT VALUE foo FROM [1, 2, 2, 3] AS foo;

This version of the query returns:

[ 1
, 2
, 3
 ]

Unnamed Projections

Similar to standard SQL, the query language supports unnamed projections (a.k.a, unnamed SELECT clause items), for which names are generated. Name generation has three cases:

  • If a projection expression is a variable reference expression, its generated name is the name of the variable.

  • If a projection expression is a field access expression, its generated name is the last identifier in the expression.

  • For all other cases, the query processor will generate a unique name.

Example
SELECT substr(user.name, 10), user.alias
FROM GleambookUsers user
WHERE user.id = 1;

This query outputs:

[ {
    "alias": "Margarita",
    "$1": "Stoddard"
} ]

In the result, $1 is the generated name for substr(user.name, 1), while alias is the generated name for user.alias.

Abbreviated Field Access Expressions

As in standard SQL, field access expressions can be abbreviated (not recommended!) when there is no ambiguity. In the next example, the variable user is the only possible variable reference for fields id, name and alias and thus could be omitted in the query. More information on abbbreviated field access can be found in the appendix section on Variable Resolution.

Example
SELECT substr(name, 10) AS lname, alias
FROM GleambookUsers user
WHERE id = 1;

Outputs:

[ {
    "lname": "Stoddard",
    "alias": "Margarita"
} ]

UNNEST Clause

For each of its input tuples, the UNNEST clause flattens a collection-valued expression into individual items, producing multiple tuples, each of which is one of the expression’s original input tuples augmented with a flattened item from its collection.

Inner UNNEST

The following example is a query that retrieves the names of the organizations that a selected user has worked for. It uses the UNNEST clause to unnest the nested collection employment in the user’s object.

Example
SELECT u.id AS userId, e.organizationName AS orgName
FROM GleambookUsers u
UNNEST u.employment e
WHERE u.id = 1;

This query returns:

[ {
    "orgName": "Codetechno",
    "userId": 1
}, {
    "orgName": "geomedia",
    "userId": 1
} ]

Note that UNNEST has SQL’s inner join semantics — that is, if a user has no employment history, no tuple corresponding to that user will be emitted in the result.

Left Outer UNNEST

As an alternative, the LEFT OUTER UNNEST clause offers SQL’s left outer join semantics. For example, no collection-valued field named hobbies exists in the object for the user whose id is 1, but the following query’s result still includes user 1.

Example
SELECT u.id AS userId, h.hobbyName AS hobby
FROM GleambookUsers u
LEFT OUTER UNNEST u.hobbies h
WHERE u.id = 1;

Returns:

[ {
    "userId": 1
} ]

Note that if u.hobbies is an empty collection or leads to a MISSING (as above) or NULL value for a given input tuple, there is no corresponding binding value for variable h for an input tuple. A MISSING value will be generated for h so that the input tuple can still be propagated.

Expressing Joins Using UNNEST

The UNNEST clause is similar to SQL’s JOIN clause except that it allows its right argument to be correlated to its left argument, as in the examples above — i.e., think "correlated cross-product". The next example shows this via a query that joins two data sets, GleambookUsers and GleambookMessages, returning user/message pairs. The results contain one object per pair, with result objects containing the user’s name and an entire message. The query can be thought of as saying "for each Gleambook user, unnest the GleambookMessages collection and filter the output with the condition message.authorId = user.id".

Example
SELECT u.name AS uname, m.message AS message
FROM GleambookUsers u
UNNEST GleambookMessages m
WHERE m.authorId = u.id;

This returns:

[ {
    "uname": "MargaritaStoddard",
    "message": " can't stand acast its plan is terrible"
}, {
    "uname": "MargaritaStoddard",
    "message": " dislike x-phone its touch-screen is horrible"
}, {
    "uname": "MargaritaStoddard",
    "message": " can't stand acast the network is horrible:("
}, {
    "uname": "MargaritaStoddard",
    "message": " like ccast the 3G is awesome:)"
}, {
    "uname": "MargaritaStoddard",
    "message": " can't stand product-w the touch-screen is terrible"
}, {
    "uname": "IsbelDull",
    "message": " like product-z its platform is mind-blowing"
}, {
    "uname": "IsbelDull",
    "message": " like product-y the plan is amazing"
} ]

Similarly, the above query can also be expressed as the UNNESTing of a correlated subquery:

Example
SELECT u.name AS uname, m.message AS message
FROM GleambookUsers u
UNNEST (
    SELECT VALUE msg
    FROM GleambookMessages msg
    WHERE msg.authorId = u.id
) AS m;

FROM clauses

A FROM clause is used for enumerating (i.e., conceptually iterating over) the contents of collections, as in SQL.

Binding expressions

In addition to stored collections, a FROM clause can iterate over any intermediate collection returned by a valid query expression. In the tuple stream generated by a FROM clause, the ordering of the input tuples are not guaranteed to be preserved.

Example
SELECT VALUE foo
FROM [1, 2, 2, 3] AS foo
WHERE foo > 2;

Returns:

[
  3
]

Multiple FROM Terms

The query language permits correlations among FROM terms. Specifically, a FROM binding expression can refer to variables defined to its left in the given FROM clause. Thus, the first unnesting example above could also be expressed as follows:

Example
SELECT u.id AS userId, e.organizationName AS orgName
FROM GleambookUsers u, u.employment e
WHERE u.id = 1;

Expressing Joins Using FROM Terms

Similarly, the join intentions of the other UNNEST-based join examples above could be expressed as:

Example
SELECT u.name AS uname, m.message AS message
FROM GleambookUsers u, GleambookMessages m
WHERE m.authorId = u.id;
Example
SELECT u.name AS uname, m.message AS message
FROM GleambookUsers u,
  (
    SELECT VALUE msg
    FROM GleambookMessages msg
    WHERE msg.authorId = u.id
  ) AS m;

Note that the first alternative is one of the SQL-92 approaches to expressing a join.

Implicit Binding Variables

Similar to standard SQL, the query language supports implicit FROM binding variables (i.e., aliases), for which a binding variable is generated. Variable generation falls into three cases:

  • If the binding expression is a variable reference expression, the generated variable’s name will be the name of the referenced variable itself.

  • If the binding expression is a field access expression (or a fully qualified name for a dataset), the generated variable’s name will be the last identifier (or the dataset name) in the expression.

  • For all other cases, a compilation error will be raised.

The next two examples show queries that do not provide binding variables in their FROM clauses.

Example
SELECT GleambookUsers.name, GleambookMessages.message
FROM GleambookUsers, GleambookMessages
WHERE GleambookMessages.authorId = GleambookUsers.id;

Returns:

[ {
    "name": "MargaritaStoddard",
    "message": " like ccast the 3G is awesome:)"
}, {
    "name": "MargaritaStoddard",
    "message": " can't stand product-w the touch-screen is terrible"
}, {
    "name": "MargaritaStoddard",
    "message": " can't stand acast its plan is terrible"
}, {
    "name": "MargaritaStoddard",
    "message": " dislike x-phone its touch-screen is horrible"
}, {
    "name": "MargaritaStoddard",
    "message": " can't stand acast the network is horrible:("
}, {
    "name": "IsbelDull",
    "message": " like product-y the plan is amazing"
}, {
    "name": "IsbelDull",
    "message": " like product-z its platform is mind-blowing"
} ]
Example
SELECT GleambookUsers.name, GleambookMessages.message
FROM GleambookUsers,
  (
    SELECT VALUE GleambookMessages
    FROM GleambookMessages
    WHERE GleambookMessages.authorId = GleambookUsers.id
  );

Returns:

Error: "Syntax error: Need an alias for the enclosed expression:\n(select element GleambookMessages\n    from GleambookMessages as GleambookMessages\n    where (GleambookMessages.authorId = GleambookUsers.id)\n )",
    "query_from_user": "use TinySocial;\n\nSELECT GleambookUsers.name, GleambookMessages.message\n    FROM GleambookUsers,\n      (\n        SELECT VALUE GleambookMessages\n        FROM GleambookMessages\n        WHERE GleambookMessages.authorId = GleambookUsers.id\n      );"

More information on implicit binding variables can be found in the appendix section on Variable Resolution.

JOIN Clauses

The join clause in the query language supports both inner joins and left outer joins from standard SQL.

Inner joins

Using a JOIN clause, the inner join intent from the preceding examples can also be expressed as follows:

Example
SELECT u.name AS uname, m.message AS message
FROM GleambookUsers u JOIN GleambookMessages m ON m.authorId = u.id;

Left Outer Joins

The query language supports SQL’s notion of left outer join. The following query is an example:

SELECT u.name AS uname, m.message AS message
FROM GleambookUsers u LEFT OUTER JOIN GleambookMessages m ON m.authorId = u.id;

Returns:

[ {
    "uname": "MargaritaStoddard",
    "message": " like ccast the 3G is awesome:)"
}, {
    "uname": "MargaritaStoddard",
    "message": " can't stand product-w the touch-screen is terrible"
}, {
    "uname": "MargaritaStoddard",
    "message": " can't stand acast its plan is terrible"
}, {
    "uname": "MargaritaStoddard",
    "message": " dislike x-phone its touch-screen is horrible"
}, {
    "uname": "MargaritaStoddard",
    "message": " can't stand acast the network is horrible:("
}, {
    "uname": "IsbelDull",
    "message": " like product-y the plan is amazing"
}, {
    "uname": "IsbelDull",
    "message": " like product-z its platform is mind-blowing"
}, {
    "uname": "EmoryUnk"
} ]

For non-matching left-side tuples, the query language produces MISSING values for the right-side binding variables; that is why the last object in the above result doesn’t have a message field. Note that this is slightly different from standard SQL, which instead would fill in NULL values for the right-side fields. The reason for this difference is that, for non-matches in its join results, the query language views fields from the right-side as being "not there" (a.k.a. MISSING) instead of as being "there but unknown" (i.e., NULL).

The left-outer join query can also be expressed using LEFT OUTER UNNEST:

SELECT u.name AS uname, m.message AS message
FROM GleambookUsers u
LEFT OUTER UNNEST (
    SELECT VALUE message
    FROM GleambookMessages message
    WHERE message.authorId = u.id
  ) m;

In general, SQL-style join queries can also be expressed by UNNEST clauses and left outer join queries can be expressed by LEFT OUTER UNNESTs.

Variable scope in JOIN clauses

Variables defined by JOIN subclauses are not visible to other subclauses in the same FROM clause. This also applies to the FROM variable that starts the JOIN subclause.

Example
SELECT * FROM GleambookUsers u
JOIN (SELECT VALUE m
      FROM GleambookMessages m
      WHERE m.authorId = u.id) m
ON u.id = m.authorId;

The variable u defined by the FROM clause is not visible inside the JOIN subclause, so this query returns no results.

GROUP BY Clauses

The GROUP BY clause generalizes standard SQL’s grouping and aggregation semantics, but it also retains backward compatibility with the standard (relational) SQL GROUP BY and aggregation features.

Group variables

In a GROUP BY clause, in addition to the binding variable(s) defined for the grouping key(s), the query language allows a user to define a group variable by using the clause’s GROUP AS extension to denote the resulting group. After grouping, then, the query’s in-scope variables include the grouping key’s binding variables as well as this group variable which will be bound to one collection value for each group. This per-group collection (i.e., multiset) value will be a set of nested objects in which each field of the object is the result of a renamed variable defined in parentheses following the group variable’s name. The GROUP AS syntax is as follows:

<GROUP> <AS> Variable ("(" VariableReference <AS> Identifier ("," VariableReference <AS> Identifier )* ")")?
Example
SELECT *
FROM GleambookMessages message
GROUP BY message.authorId AS uid GROUP AS msgs(message AS msg);

This first example query returns:

[ {
    "msgs": [
        {
            "msg": {
                "senderLocation": [
                    38.97,
                    77.49
                ],
                "inResponseTo": 1,
                "messageId": 11,
                "authorId": 1,
                "message": " can't stand acast its plan is terrible"
            }
        },
        {
            "msg": {
                "senderLocation": [
                    41.66,
                    80.87
                ],
                "inResponseTo": 4,
                "messageId": 2,
                "authorId": 1,
                "message": " dislike x-phone its touch-screen is horrible"
            }
        },
        {
            "msg": {
                "senderLocation": [
                    37.73,
                    97.04
                ],
                "inResponseTo": 2,
                "messageId": 4,
                "authorId": 1,
                "message": " can't stand acast the network is horrible:("
            }
        },
        {
            "msg": {
                "senderLocation": [
                    40.33,
                    80.87
                ],
                "inResponseTo": 11,
                "messageId": 8,
                "authorId": 1,
                "message": " like ccast the 3G is awesome:)"
            }
        },
        {
            "msg": {
                "senderLocation": [
                    42.5,
                    70.01
                ],
                "inResponseTo": 12,
                "messageId": 10,
                "authorId": 1,
                "message": " can't stand product-w the touch-screen is terrible"
            }
        }
    ],
    "uid": 1
}, {
    "msgs": [
        {
            "msg": {
                "senderLocation": [
                    31.5,
                    75.56
                ],
                "inResponseTo": 1,
                "messageId": 6,
                "authorId": 2,
                "message": " like product-z its platform is mind-blowing"
            }
        },
        {
            "msg": {
                "senderLocation": [
                    48.09,
                    81.01
                ],
                "inResponseTo": 4,
                "messageId": 3,
                "authorId": 2,
                "message": " like product-y the plan is amazing"
            }
        }
    ],
    "uid": 2
} ]

As we can see from the above query result, each group in the example query’s output has an associated group variable value called msgs that appears in the SELECT *'s result. This variable contains a collection of objects associated with the group; each of the group’s message values appears in the msg field of the objects in the msgs collection.

The group variable in the query language makes more complex, composable, nested subqueries over a group possible, which is important given the language’s more complex data model (relative to SQL). As a simple example of this, as we really just want the messages associated with each user, we might wish to avoid the "extra wrapping" of each message as the msg field of an object. (That wrapping is useful in more complex cases, but is essentially just in the way here.) We can use a subquery in the SELECT clause to tunnel through the extra nesting and produce the desired result.

Example
SELECT uid, (SELECT VALUE g.msg FROM g) AS msgs
FROM GleambookMessages gbm
GROUP BY gbm.authorId AS uid
GROUP AS g(gbm as msg);

This variant of the example query returns:

[ {
    "msgs": [
        {
            "senderLocation": [
                38.97,
                77.49
            ],
            "inResponseTo": 1,
            "messageId": 11,
            "authorId": 1,
            "message": " can't stand acast its plan is terrible"
        },
        {
            "senderLocation": [
                41.66,
                80.87
            ],
            "inResponseTo": 4,
            "messageId": 2,
            "authorId": 1,
            "message": " dislike x-phone its touch-screen is horrible"
        },
        {
            "senderLocation": [
                37.73,
                97.04
            ],
            "inResponseTo": 2,
            "messageId": 4,
            "authorId": 1,
            "message": " can't stand acast the network is horrible:("
        },
        {
            "senderLocation": [
                40.33,
                80.87
            ],
            "inResponseTo": 11,
            "messageId": 8,
            "authorId": 1,
            "message": " like ccast the 3G is awesome:)"
        },
        {
            "senderLocation": [
                42.5,
                70.01
            ],
            "inResponseTo": 12,
            "messageId": 10,
            "authorId": 1,
            "message": " can't stand product-w the touch-screen is terrible"
        }
    ],
    "uid": 1
}, {
    "msgs": [
        {
            "senderLocation": [
                31.5,
                75.56
            ],
            "inResponseTo": 1,
            "messageId": 6,
            "authorId": 2,
            "message": " like product-z its platform is mind-blowing"
        },
        {
            "senderLocation": [
                48.09,
                81.01
            ],
            "inResponseTo": 4,
            "messageId": 3,
            "authorId": 2,
            "message": " like product-y the plan is amazing"
        }
    ],
    "uid": 2
} ]

The next example shows a more interesting case involving the use of a subquery in the SELECT list. Here the subquery further processes the groups. There is no renaming in the declaration of the group variable g such that g only has one field gbm which comes from the FROM clause.

Example
SELECT uid,
       (SELECT VALUE g.gbm
        FROM g
        WHERE g.gbm.message LIKE '% like%'
        ORDER BY g.gbm.messageId
        LIMIT 2) AS msgs
FROM GleambookMessages gbm
GROUP BY gbm.authorId AS uid
GROUP AS g;

This example query returns:

[ {
    "msgs": [
        {
            "senderLocation": [
                40.33,
                80.87
            ],
            "inResponseTo": 11,
            "messageId": 8,
            "authorId": 1,
            "message": " like ccast the 3G is awesome:)"
        }
    ],
    "uid": 1
}, {
    "msgs": [
        {
            "senderLocation": [
                48.09,
                81.01
            ],
            "inResponseTo": 4,
            "messageId": 3,
            "authorId": 2,
            "message": " like product-y the plan is amazing"
        },
        {
            "senderLocation": [
                31.5,
                75.56
            ],
            "inResponseTo": 1,
            "messageId": 6,
            "authorId": 2,
            "message": " like product-z its platform is mind-blowing"
        }
    ],
    "uid": 2
} ]

Implicit Grouping Key Variables

In the query language syntax, providing named binding variables for GROUP BY key expressions is optional. If a grouping key is missing a user-provided binding variable, the underlying compiler will generate one. Automatic grouping key variable naming falls into three cases, much like the treatment of unnamed projections:

  • If the grouping key expression is a variable reference expression, the generated variable gets the same name as the referred variable;

  • If the grouping key expression is a field access expression, the generated variable gets the same name as the last identifier in the expression;

  • For all other cases, the compiler generates a unique variable (but the user query is unable to refer to this generated variable).

The next example illustrates a query that doesn’t provide binding variables for its grouping key expressions.

Example
SELECT authorId,
       (SELECT VALUE g.gbm
        FROM g
        WHERE g.gbm.message LIKE '% like%'
        ORDER BY g.gbm.messageId
        LIMIT 2) AS msgs
FROM GleambookMessages gbm
GROUP BY gbm.authorId
GROUP AS g;

This query returns:

    [ {
    "msgs": [
        {
            "senderLocation": [
                40.33,
                80.87
            ],
            "inResponseTo": 11,
            "messageId": 8,
            "authorId": 1,
            "message": " like ccast the 3G is awesome:)"
        }
    ],
    "authorId": 1
}, {
    "msgs": [
        {
            "senderLocation": [
                48.09,
                81.01
            ],
            "inResponseTo": 4,
            "messageId": 3,
            "authorId": 2,
            "message": " like product-y the plan is amazing"
        },
        {
            "senderLocation": [
                31.5,
                75.56
            ],
            "inResponseTo": 1,
            "messageId": 6,
            "authorId": 2,
            "message": " like product-z its platform is mind-blowing"
        }
    ],
    "authorId": 2
} ]

Based on the three variable generation rules, the generated variable for the grouping key expression message.authorId is authorId (which is how it is referred to in the example’s SELECT clause).

Implicit Group Variables

The group variable itself is also optional in the GROUP BY syntax. If a user’s query does not declare the name and structure of the group variable using GROUP AS, the query compiler will generate a unique group variable whose fields include all of the binding variables defined in the FROM clause of the current enclosing SELECT statement. In this case the user’s query will not be able to refer to the generated group variable, but is able to call SQL-92 aggregation functions as in SQL-92.

Aggregation Functions

In the traditional SQL, which doesn’t support nested data, grouping always also involves the use of aggregation to compute properties of the groups (for example, the average number of messages per user rather than the actual set of messages per user). Each aggregation function in the query language takes a collection (for example, the group of messages) as its input and produces a scalar value as its output. These aggregation functions, being truly functional in nature (unlike in SQL), can be used anywhere in a query where an expression is allowed. The following table catalogs the built-in aggregation functions of the query language and also indicates how each one handles NULL/MISSING values in the input collection or a completely empty input collection:

Function NULL MISSING Empty Collection

STRICT_COUNT

counted

counted

0

STRICT_SUM

returns NULL

returns NULL

returns NULL

STRICT_MAX

returns NULL

returns NULL

returns NULL

STRICT_MIN

returns NULL

returns NULL

returns NULL

STRICT_AVG

returns NULL

returns NULL

returns NULL

ARRAY_COUNT

not counted

not counted

0

ARRAY_SUM

ignores NULL

ignores NULL

returns NULL

ARRAY_MAX

ignores NULL

ignores NULL

returns NULL

ARRAY_MIN

ignores NULL

ignores NULL

returns NULL

ARRAY_AVG

ignores NULL

ignores NULL

returns NULL

Notice that the query language has twice as many functions listed above as there are aggregate functions in SQL-92. This is because the language offers two versions of each — one that handles UNKNOWN values in a semantically strict fashion, where unknown values in the input result in unknown values in the output — and one that handles them in the ad hoc "just ignore the unknown values" fashion that the SQL standard chose to adopt.

Example
ARRAY_AVG(
    (
      SELECT VALUE ARRAY_COUNT(friendIds) FROM GleambookUsers
    )
);

This example returns:

3.3333333333333335
Example
SELECT uid AS uid, ARRAY_COUNT(grp) AS msgCnt
FROM GleambookMessages message
GROUP BY message.authorId AS uid
GROUP AS grp(message AS msg);

This query returns:

[ {
    "uid": 1,
    "msgCnt": 5
}, {
    "uid": 2,
    "msgCnt": 2
} ]

Notice how the query forms groups where each group involves a message author and their messages. (SQL cannot do this because the grouped intermediate result is non-1NF in nature.) The query then uses the collection aggregate function ARRAY_COUNT to get the cardinality of each group of messages.

Each aggregation function in the query language supports DISTINCT modifier that removes duplicate values from the input collection.

Example
ARRAY_SUM(DISTINCT [1, 1, 2, 2, 3])

This query returns:

6

SQL-92 Aggregation Functions

For compatibility with the traditional SQL aggregation functions, the query language also offers SQL-92’s aggregation function symbols (COUNT, SUM, MAX, MIN, and AVG) as supported syntactic sugar. The query compiler rewrites queries that utilize these function symbols into queries that only use the collection aggregate functions of the query language. The following example uses the SQL-92 syntax approach to compute a result that is identical to that of the more explicit example above:

Example
SELECT uid, COUNT(*) AS msgCnt
FROM GleambookMessages msg
GROUP BY msg.authorId AS uid;

It is important to realize that COUNT is actually not a built-in aggregation function. Rather, the COUNT query above is using a special "sugared" function symbol that the query compiler will rewrite as follows:

SELECT uid AS uid, ARRAY_COUNT( (SELECT VALUE 1 FROM `$1` as g) ) AS msgCnt
FROM GleambookMessages msg
GROUP BY msg.authorId AS uid
GROUP AS `$1`(msg AS msg);

The same sort of rewritings apply to the function symbols SUM, MAX, MIN, and AVG. In contrast to the collection aggregate functions of the query language, these special SQL-92 function symbols can only be used in the same way they are in standard SQL (i.e., with the same restrictions).

DISTINCT modifier is also supported for these aggregate functions.

SQL-92 Compliant GROUP BY Aggregations

The query language provides full support for SQL-92 GROUP BY aggregation queries. The following query is such an example:

Example
SELECT msg.authorId, COUNT(*)
FROM GleambookMessages msg
GROUP BY msg.authorId;

This query outputs:

[ {
    "authorId": 1,
    "$1": 5
}, {
    "authorId": 2,
    "$1": 2
} ]

In principle, a msg reference in the query’s SELECT clause would be "sugarized" as a collection (as described in Implicit Group Variables). However, since the SELECT expression msg.authorId is syntactically identical to a GROUP BY key expression, it will be internally replaced by the generated group key variable. The following is the equivalent rewritten query that will be generated by the compiler for the query above:

SELECT authorId AS authorId, ARRAY_COUNT( (SELECT g.msg FROM `$1` AS g) )
FROM GleambookMessages msg
GROUP BY msg.authorId AS authorId
GROUP AS `$1`(msg AS msg);

Column Aliases

The query language also allows column aliases to be used as ORDER BY keys.

Example
SELECT msg.authorId AS aid, COUNT(*)
FROM GleambookMessages msg
GROUP BY msg.authorId;
ORDER BY aid;

This query returns:

[ {
    "$1": 5,
    "aid": 1
}, {
    "$1": 2,
    "aid": 2
} ]

WHERE Clauses and HAVING Clauses

Both WHERE clauses and HAVING clauses are used to filter input data based on a condition expression. Only tuples for which the condition expression evaluates to TRUE are propagated. Note that if the condition expression evaluates to NULL or MISSING the input tuple will be disgarded.

ORDER BY Clauses

The ORDER BY clause is used to globally sort data in either ascending order (i.e., ASC) or descending order (i.e., DESC). During ordering, MISSING and NULL are treated as being smaller than any other value if they are encountered in the ordering key(s). MISSING is treated as smaller than NULL if both occur in the data being sorted. The ordering of values of a given type is consistent with its type’s <= ordering; the ordering of values across types is implementation-defined but stable. The following example returns all GleambookUsers in descending order by their number of friends.

Example
SELECT VALUE user
FROM GleambookUsers AS user
ORDER BY ARRAY_COUNT(user.friendIds) DESC;

This query returns:

[ {
    "userSince": "2012-08-20T10:10:00.000Z",
    "friendIds": [
        2,
        3,
        6,
        10
    ],
    "gender": "F",
    "name": "MargaritaStoddard",
    "nickname": "Mags",
    "alias": "Margarita",
    "id": 1,
    "employment": [
        {
            "organizationName": "Codetechno",
            "start-date": "2006-08-06"
        },
        {
            "end-date": "2010-01-26",
            "organizationName": "geomedia",
            "start-date": "2010-06-17"
        }
    ]
}, {
    "userSince": "2012-07-10T10:10:00.000Z",
    "friendIds": [
        1,
        5,
        8,
        9
    ],
    "name": "EmoryUnk",
    "alias": "Emory",
    "id": 3,
    "employment": [
        {
            "organizationName": "geomedia",
            "endDate": "2010-01-26",
            "startDate": "2010-06-17"
        }
    ]
}, {
    "userSince": "2011-01-22T10:10:00.000Z",
    "friendIds": [
        1,
        4
    ],
    "name": "IsbelDull",
    "nickname": "Izzy",
    "alias": "Isbel",
    "id": 2,
    "employment": [
        {
            "organizationName": "Hexviafind",
            "startDate": "2010-04-27"
        }
    ]
} ]

LIMIT Clauses

The LIMIT clause is used to limit the result set to a specified constant size. The use of the LIMIT clause is illustrated in the next example.

Example
SELECT VALUE user
FROM GleambookUsers AS user
ORDER BY len(user.friendIds) DESC
LIMIT 1;

This query returns:

[ {
    "userSince": "2012-08-20T10:10:00.000Z",
    "friendIds": [
        2,
        3,
        6,
        10
    ],
    "gender": "F",
    "name": "MargaritaStoddard",
    "nickname": "Mags",
    "alias": "Margarita",
    "id": 1,
    "employment": [
        {
            "organizationName": "Codetechno",
            "start-date": "2006-08-06"
        },
        {
            "end-date": "2010-01-26",
            "organizationName": "geomedia",
            "start-date": "2010-06-17"
        }
    ]
} ]

WITH Clauses

As in standard SQL, WITH clauses are available to improve the modularity of a query. The next query shows an example.

Example
WITH avgFriendCount AS (
  SELECT VALUE AVG(ARRAY_COUNT(user.friendIds))
  FROM GleambookUsers AS user
)[0]
SELECT VALUE user
FROM GleambookUsers user
WHERE ARRAY_COUNT(user.friendIds) > avgFriendCount;

This query returns:

[ {
    "userSince": "2012-08-20T10:10:00.000Z",
    "friendIds": [
        2,
        3,
        6,
        10
    ],
    "gender": "F",
    "name": "MargaritaStoddard",
    "nickname": "Mags",
    "alias": "Margarita",
    "id": 1,
    "employment": [
        {
            "organizationName": "Codetechno",
            "start-date": "2006-08-06"
        },
        {
            "end-date": "2010-01-26",
            "organizationName": "geomedia",
            "start-date": "2010-06-17"
        }
    ]
}, {
    "userSince": "2012-07-10T10:10:00.000Z",
    "friendIds": [
        1,
        5,
        8,
        9
    ],
    "name": "EmoryUnk",
    "alias": "Emory",
    "id": 3,
    "employment": [
        {
            "organizationName": "geomedia",
            "endDate": "2010-01-26",
            "startDate": "2010-06-17"
        }
    ]
} ]

The query is equivalent to the following, more complex, inlined form of the query:

SELECT *
FROM GleambookUsers user
WHERE ARRAY_COUNT(user.friendIds) >
    ( SELECT VALUE AVG(ARRAY_COUNT(user.friendIds))
      FROM GleambookUsers AS user
    ) [0];

WITH can be particularly useful when a value needs to be used several times in a query.

Before proceeding further, notice that both the WITH query and its equivalent inlined variant include the syntax "[0]" — this is due to a noteworthy difference between the query language and SQL-92. In SQL-92, whenever a scalar value is expected and it is being produced by a query expression, the SQL-92 query processor will evaluate the expression, check that there is only one row and column in the result at runtime, and then coerce the one-row/one-column tabular result into a scalar value. A JSON query language, being designed to deal with nested data and schema-less data, should not do this. Collection-valued data is perfectly legal in most contexts, and its data is schema-less, so the query processor rarely knows exactly what to expect where and such automatic conversion would often not be desirable. Thus, in the queries above, the use of "[0]" extracts the first (i.e., 0th) element of an array-valued query expression’s result; this is needed above, even though the result is an array of one element, to extract the only element in the singleton array and obtain the desired scalar for the comparison.

LET Clauses

Similar to WITH clauses, LET clauses can be useful when a (complex) expression is used several times within a query, allowing it to be written once to make the query more concise. The next query shows an example.

Example
SELECT u.name AS uname, messages AS messages
FROM GleambookUsers u
LET messages = (SELECT VALUE m
                FROM GleambookMessages m
                WHERE m.authorId = u.id)
WHERE EXISTS messages;

This query lists GleambookUsers that have posted GleambookMessages and shows all authored messages for each listed user. It returns:

[ {
    "uname": "MargaritaStoddard",
    "messages": [
        {
            "senderLocation": [
                38.97,
                77.49
            ],
            "inResponseTo": 1,
            "messageId": 11,
            "authorId": 1,
            "message": " can't stand acast its plan is terrible"
        },
        {
            "senderLocation": [
                41.66,
                80.87
            ],
            "inResponseTo": 4,
            "messageId": 2,
            "authorId": 1,
            "message": " dislike x-phone its touch-screen is horrible"
        },
        {
            "senderLocation": [
                37.73,
                97.04
            ],
            "inResponseTo": 2,
            "messageId": 4,
            "authorId": 1,
            "message": " can't stand acast the network is horrible:("
        },
        {
            "senderLocation": [
                40.33,
                80.87
            ],
            "inResponseTo": 11,
            "messageId": 8,
            "authorId": 1,
            "message": " like ccast the 3G is awesome:)"
        },
        {
            "senderLocation": [
                42.5,
                70.01
            ],
            "inResponseTo": 12,
            "messageId": 10,
            "authorId": 1,
            "message": " can't stand product-w the touch-screen is terrible"
        }
    ]
}, {
    "uname": "IsbelDull",
    "messages": [
        {
            "senderLocation": [
                31.5,
                75.56
            ],
            "inResponseTo": 1,
            "messageId": 6,
            "authorId": 2,
            "message": " like product-z its platform is mind-blowing"
        },
        {
            "senderLocation": [
                48.09,
                81.01
            ],
            "inResponseTo": 4,
            "messageId": 3,
            "authorId": 2,
            "message": " like product-y the plan is amazing"
        }
    ]
} ]

This query is equivalent to the following query that does not use the LET clause:

SELECT u.name AS uname, ( SELECT VALUE m
                          FROM GleambookMessages m
                          WHERE m.authorId = u.id
                        ) AS messages
FROM GleambookUsers u
WHERE EXISTS ( SELECT VALUE m
               FROM GleambookMessages m
               WHERE m.authorId = u.id
             );

UNION ALL

UNION ALL can be used to combine two input arrays or multisets into one. As in SQL, there is no ordering guarantee on the contents of the output stream. However, unlike SQL, the query language does not constrain what the data looks like on the input streams; in particular, it allows heterogenity on the input and output streams. A type error will be raised if one of the inputs is not a collection. The following odd but legal query is an example:

Example
SELECT u.name AS uname
FROM GleambookUsers u
WHERE u.id = 2
  UNION ALL
SELECT VALUE m.message
FROM GleambookMessages m
WHERE authorId=2;

This query returns:

[
  " like product-z its platform is mind-blowing"
  , {
    "uname": "IsbelDull"
}, " like product-y the plan is amazing"
 ]

Subqueries

In the query language, an arbitrary subquery can appear anywhere that an expression can appear. Unlike SQL-92, as was just alluded to, the subqueries in a SELECT list or a boolean predicate need not return singleton, single-column relations. Instead, they may return arbitrary collections. For example, the following query is a variant of the prior group-by query examples; it retrieves an array of up to two "dislike" messages per user.

Example
SELECT uid,
       (SELECT VALUE m.msg
        FROM msgs m
        WHERE m.msg.message LIKE '%dislike%'
        ORDER BY m.msg.messageId
        LIMIT 2) AS msgs
FROM GleambookMessages message
GROUP BY message.authorId AS uid GROUP AS msgs(message AS msg);

For our sample data set, this query returns:

[ {
    "msgs": [
        {
            "senderLocation": [
                41.66,
                80.87
            ],
            "inResponseTo": 4,
            "messageId": 2,
            "authorId": 1,
            "message": " dislike x-phone its touch-screen is horrible"
        }
    ],
    "uid": 1
}, {
    "msgs": [

    ],
    "uid": 2
} ]

Note that a subquery, like a top-level SELECT statment, always returns a collection — regardless of where within a query the subquery occurs — and again, its result is never automatically cast into a scalar.

Differences from SQL-92

The query language offers the following additional features beyond SQL-92:

  • Fully composable and functional: A subquery can iterate over any intermediate collection and can appear anywhere in a query.

  • Schema-free: The query language does not assume the existence of a static schema for any data that it processes.

  • Correlated FROM terms: A right-side FROM term expression can refer to variables defined by FROM terms on its left.

  • Powerful GROUP BY: In addition to a set of aggregate functions as in standard SQL, the groups created by the GROUP BY clause are directly usable in nested queries and/or to obtain nested results.

  • Generalized SELECT clause: A SELECT clause can return any type of collection, while in SQL-92, a SELECT clause has to return a (homogeneous) collection of objects.

The following matrix is a quick "SQL-92 compatibility cheat sheet" for the query language.

Feature The query language SQL-92 Why different?

SELECT *

Returns nested objects

Returns flattened concatenated objects

Nested collections are 1st class citizens

SELECT list

order not preserved

order preserved

Fields in a JSON object are not ordered

Subquery

Returns a collection

The returned collection is cast into a scalar value if the subquery appears in a SELECT list or on one side of a comparison or as input to a function

Nested collections are 1st class citizens

LEFT OUTER JOIN

Fills in MISSING(s) for non-matches

Fills in NULL(s) for non-matches

"Absence" is more appropriate than "unknown" here

UNION ALL

Allows heterogeneous inputs and output

Input streams must be UNION-compatible and output field names are drawn from the first input stream

Heterogenity and nested collections are common

IN constant_expr

The constant expression has to be an array or multiset, i.e., [..,..,…​]

The constant collection can be represented as comma-separated items in a paren pair

Nested collections are 1st class citizens

String literal

Double quotes or single quotes

Single quotes only

Double quoted strings are pervasive

Delimited identifiers

Backticks

Double quotes

Double quoted strings are pervasive

The following SQL-92 features are not implemented yet. However, the query language does not conflict with these features:

  • CROSS JOIN, NATURAL JOIN, UNION JOIN

  • RIGHT and FULL OUTER JOIN

  • INTERSECT, EXCEPT, UNION with set semantics

  • CAST expression

  • COALESCE expression

  • ALL and SOME predicates for linking to subqueries

  • UNIQUE predicate (tests a collection for duplicates)

  • MATCH predicate (tests for referential integrity)

  • Row and Table constructors

  • Preserved order for expressions in a SELECT list