A SQL++ query can be any legal SQL++ expression or SELECT statement. A SQL++ query always ends with a semicolon.
Query ::= (Expression | SelectStatement) ";"
The following shows the (rich) grammar for the SELECT statement in SQL++.
SelectStatement ::= ( WithClause )?
SelectSetOperation (OrderbyClause )? ( LimitClause )?
SelectSetOperation ::= SelectBlock (<UNION> <ALL> ( SelectBlock | Subquery ) )*
Subquery ::= "(" SelectStatement ")"
SelectBlock ::= SelectClause
( FromClause ( WithClause )?)?
( WhereClause )?
( GroupbyClause ( LetClause )? ( HavingClause )? )?
|
FromClause ( WithClause )?
( WhereClause )?
( GroupbyClause ( WithClause )? ( HavingClause )? )?
SelectClause
SelectClause ::= <SELECT> ( <ALL> | <DISTINCT> )? ( SelectRegular | SelectValue )
SelectRegular ::= Projection ( "," Projection )*
SelectValue ::= ( <VALUE> | <ELEMENT> | <RAW> ) Expression
Projection ::= ( Expression ( <AS> )? Identifier | "*" )
FromClause ::= <FROM> FromTerm ( "," FromTerm )*
FromTerm ::= Expression (( <AS> )? Variable)? ( <AT> Variable )?
( ( JoinType )? ( JoinClause | UnnestClause ) )*
JoinClause ::= <JOIN> Expression (( <AS> )? Variable)? (<AT> Variable)? <ON> Expression
UnnestClause ::= ( <UNNEST> | <CORRELATE> | <FLATTEN> ) 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
("(" Variable <AS> VariableReference ("," Variable <AS> VariableReference )* ")")?
)?
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 records (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:
{"id":1,"alias":"Margarita","name":"MargaritaStoddard","nickname":"Mags","userSince":datetime("2012-08-20T10:10:00"),"friendIds":{{2,3,6,10}},"employment":[{"organizationName":"Codetechno","start-date":date("2006-08-06")},{"organizationName":"geomedia","start-date":date("2010-06-17"),"end-date":date("2010-01-26")}],"gender":"F"}
{"id":2,"alias":"Isbel","name":"IsbelDull","nickname":"Izzy","userSince":datetime("2011-01-22T10:10:00"),"friendIds":{{1,4}},"employment":[{"organizationName":"Hexviafind","startDate":date("2010-04-27")}]}
{"id":3,"alias":"Emory","name":"EmoryUnk","userSince":datetime("2012-07-10T10:10:00"),"friendIds":{{1,5,8,9}},"employment":[{"organizationName":"geomedia","startDate":date("2010-06-17"),"endDate":date("2010-01-26")}]}
GleambookMessages collection:
{"messageId":2,"authorId":1,"inResponseTo":4,"senderLocation":point("41.66,80.87"),"message":" dislike iphone its touch-screen is horrible"}
{"messageId":3,"authorId":2,"inResponseTo":4,"senderLocation":point("48.09,81.01"),"message":" like samsung the plan is amazing"}
{"messageId":4,"authorId":1,"inResponseTo":2,"senderLocation":point("37.73,97.04"),"message":" can't stand at&t the network is horrible:("}
{"messageId":6,"authorId":2,"inResponseTo":1,"senderLocation":point("31.5,75.56"),"message":" like t-mobile its platform is mind-blowing"}
{"messageId":8,"authorId":1,"inResponseTo":11,"senderLocation":point("40.33,80.87"),"message":" like verizon the 3G is awesome:)"}
{"messageId":10,"authorId":1,"inResponseTo":12,"senderLocation":point("42.5,70.01"),"message":" can't stand motorola the touch-screen is terrible"}
{"messageId":11,"authorId":1,"inResponseTo":1,"senderLocation":point("38.97,77.49"),"message":" can't stand at&t its plan is terrible"}
The SQL++ SELECT clause always returns a collection value as its result (even if the result is empty or a singleton).
The SELECT VALUE clause in SQL++ returns a collection 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 SQL++ also allows the keywords ELEMENT or RAW to be used in place of VALUE (not recommended). The following example shows a query that selects one user from the GleambookUsers collection.
SELECT VALUE user FROM GleambookUsers user WHERE user.id = 1;
This query returns:
[
{ "id": 1, "alias": "Margarita", "name": "MargaritaStoddard", "userSince": datetime("2012-08-20T10:10:00.000Z"), "friendIds": {{ 2, 3, 6, 10 }}, "employment": [ { "organizationName": "Codetechno", "startDate": date("2006-08-06") }, { "organizationName": "geomedia", "startDate": date("2010-06-17"), "endDate": date("2010-01-26") } ], "nickname": "Mags", "gender": "F" }
]
In SQL++, the traditional SQL-style SELECT syntax is also supported. This syntax can also be reformulated in a SELECT VALUE based manner in SQL++. (E.g., SELECT expA AS fldA, expB AS fldB is syntactic sugar for SELECT VALUE { 'fldA': expA, 'fldB': expB }.)
SELECT user.alias user_alias, user.name user_name FROM GleambookUsers user WHERE user.id = 1;
Returns:
[
{"user_alias":"Margarita","user_name":"MargaritaStoddard"}
]
In SQL++, SELECT * returns a record 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 is the value of that binding variable.
SELECT * FROM GleambookUsers user;
Since user is the only binding variable generated in the FROM clause, this query returns:
[
{ "user": { "id": 1, "alias": "Margarita", "name": "MargaritaStoddard", "userSince": datetime("2012-08-20T10:10:00.000Z"), "friendIds": {{ 2, 3, 6, 10 }}, "employment": [ { "organizationName": "Codetechno", "startDate": date("2006-08-06") }, { "organizationName": "geomedia", "startDate": date("2010-06-17"), "endDate": date("2010-01-26") } ], "nickname": "Mags", "gender": "F" } },
{ "user": { "id": 2, "alias": "Isbel", "name": "IsbelDull", "userSince": datetime("2011-01-22T10:10:00.000Z"), "friendIds": {{ 1, 4 }}, "employment": [ { "organizationName": "Hexviafind", "startDate": date("2010-04-27") } ], "nickname": "Izzy" } },
{ "user": { "id": 3, "alias": "Emory", "name": "EmoryUnk", "userSince": datetime("2012-07-10T10:10:00.000Z"), "friendIds": {{ 1, 5, 8, 9 }}, "employment": [ { "organizationName": "geomedia", "startDate": date("2010-06-17"), "endDate": date("2010-01-26") } ] } }
]
SQL++'s DISTINCT keyword is used to eliminate duplicate items in results. The following example shows how it works.
SELECT DISTINCT * FROM [1, 2, 2, 3] AS foo;
This query returns:
[
{ "foo": 1 },
{ "foo": 2 },
{ "foo": 3 }
]
SELECT DISTINCT VALUE foo FROM [1, 2, 2, 3] AS foo;
This version of the query returns:
[ 1, 2, 3 ]
Similar to standard SQL, SQL++ supports unnamed projections (a.k.a, unnamed SELECT clause items), for which names are generated. Name generation has three cases:
SELECT substr(user.name, 10), user.alias FROM GleambookUsers user WHERE user.id = 1;
This query outputs:
[
{ "$1": "Stoddard", "alias": "Margarita" }
]
In the result, $1 is the generated name for substr(user.name, 1), while alias is the generated name for user.alias.
As in standard SQL, 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.
SELECT substr(name, 10) AS lname, alias FROM GleambookUsers user WHERE id = 1;
Outputs:
[
{ "lname": "Stoddard", "alias": "Margarita" }
]
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.
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 record.
SELECT u.id AS userId, e.organizationName AS orgName FROM GleambookUsers u UNNEST u.employment e WHERE u.id = 1;
This query returns:
[
{ "userId": 1, "orgName": "Codetechno" },
{ "userId": 1, "orgName": "geomedia" }
]
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.
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 record for the user whose id is 1, but the following query’s result still includes user 1.
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.
The SQL++ 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 record per pair, with result records 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”.
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 at&t its plan is terrible" },
{ "uname": "MargaritaStoddard", "message": " dislike iphone its touch-screen is horrible" },
{ "uname": "MargaritaStoddard", "message": " can't stand at&t the network is horrible:(" },
{ "uname": "MargaritaStoddard", "message": " like verizon the 3G is awesome:)" },
{ "uname": "MargaritaStoddard", "message": " can't stand motorola the touch-screen is terrible" },
{ "uname": "IsbelDull", "message": " like t-mobile its platform is mind-blowing" },
{ "uname": "IsbelDull", "message": " like samsung the plan is amazing" }
]
Similarly, the above query can also be expressed as the UNNESTing of a correlated SQL++ subquery:
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;
A FROM clause is used for enumerating (i.e., conceptually iterating over) the contents of collections, as in SQL.
In SQL++, in addition to stored collections, a FROM clause can iterate over any intermediate collection returned by a valid SQL++ expression.
SELECT VALUE foo FROM [1, 2, 2, 3] AS foo WHERE foo > 2;
Returns:
[ 3 ]
SQL++ 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:
SELECT u.id AS userId, e.organizationName AS orgName FROM GleambookUsers u, u.employment e WHERE u.id = 1;
Similarly, the join intentions of the other UNNEST-based join examples above could be expressed as:
SELECT u.name AS uname, m.message AS message FROM GleambookUsers u, GleambookMessages m WHERE m.authorId = u.id;
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.
Similar to standard SQL, SQL++ supports implicit FROM binding variables (i.e., aliases), for which a binding variable is generated. SQL++ variable generation falls into three cases:
The next two examples show queries that do not provide binding variables in their FROM clauses.
SELECT GleambookUsers.name, GleambookMessages.message FROM GleambookUsers, GleambookMessages WHERE GleambookMessages.authorId = GleambookUsers.id;
Returns:
[
{ "name": "MargaritaStoddard", "message": " can't stand at&t its plan is terrible" },
{ "name": "MargaritaStoddard", "message": " dislike iphone its touch-screen is horrible" },
{ "name": "MargaritaStoddard", "message": " can't stand at&t the network is horrible:(" },
{ "name": "MargaritaStoddard", "message": " like verizon the 3G is awesome:)" },
{ "name": "MargaritaStoddard", "message": " can't stand motorola the touch-screen is terrible" },
{ "name": "IsbelDull", "message": " like t-mobile its platform is mind-blowing" },
{ "name": "IsbelDull", "message": " like samsung the plan is amazing" }
]
SELECT GleambookUsers.name, GleambookMessages.message
FROM GleambookUsers,
(
SELECT VALUE GleambookMessages
FROM GleambookMessages
WHERE GleambookMessages.authorId = GleambookUsers.id
);
Returns:
Error: Need an alias for the enclosed expression:
(select element $GleambookMessages
from $GleambookMessages as $GleambookMessages
where ($GleambookMessages.authorId = $GleambookUsers.id)
)
The join clause in SQL++ supports both inner joins and left outer joins from standard SQL.
Using a JOIN clause, the inner join intent from the preceeding examples can also be expressed as follows:
SELECT u.name AS uname, m.message AS message FROM GleambookUsers u JOIN GleambookMessages m ON m.authorId = u.id;
SQL++ 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": " can't stand at&t its plan is terrible" },
{ "uname": "MargaritaStoddard", "message": " dislike iphone its touch-screen is horrible" },
{ "uname": "MargaritaStoddard", "message": " can't stand at&t the network is horrible:(" },
{ "uname": "MargaritaStoddard", "message": " like verizon the 3G is awesome:)" },
{ "uname": "MargaritaStoddard", "message": " can't stand motorola the touch-screen is terrible" },
{ "uname": "IsbelDull", "message": " like t-mobile its platform is mind-blowing" },
{ "uname": "IsbelDull", "message": " like samsung the plan is amazing" },
{ "uname": "EmoryUnk" }
]
For non-matching left-side tuples, SQL++ produces MISSING values for the right-side binding variables; that is why the last record 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, SQL++ 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, in SQL++, SQL-style join queries can also be expressed by UNNEST clauses and left outer join queries can be expressed by LEFT OUTER UNNESTs.
The SQL++ 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.
In a GROUP BY clause, in addition to the binding variable(s) defined for the grouping key(s), SQL++ 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 value will be a set of nested records in which each field of the record 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 ("(" Variable <AS> VariableReference ("," Variable <AS> VariableReference )* ")")?
SELECT * FROM GleambookMessages message GROUP BY message.authorId AS uid GROUP AS msgs(message AS msg);
This first example query returns:
[
{ "uid": 1, "msgs": [ { "msg": { "messageId": 8, "authorId": 1, "inResponseTo": 11, "senderLocation": point("40.33,80.87"), "message": " like verizon the 3G is awesome:)" } },
{ "msg": { "messageId": 10, "authorId": 1, "inResponseTo": 12, "senderLocation": point("42.5,70.01"), "message": " can't stand motorola the touch-screen is terrible" } },
{ "msg": { "messageId": 11, "authorId": 1, "inResponseTo": 1, "senderLocation": point("38.97,77.49"), "message": " can't stand at&t its plan is terrible" } },
{ "msg": { "messageId": 2, "authorId": 1, "inResponseTo": 4, "senderLocation": point("41.66,80.87"), "message": " dislike iphone its touch-screen is horrible" } },
{ "msg": { "messageId": 4, "authorId": 1, "inResponseTo": 2, "senderLocation": point("37.73,97.04"), "message": " can't stand at&t the network is horrible:(" } } ] },
{ "uid": 2, "msgs": [ { "msg": { "messageId": 6, "authorId": 2, "inResponseTo": 1, "senderLocation": point("31.5,75.56"), "message": " like t-mobile its platform is mind-blowing" } },
{ "msg": { "messageId": 3, "authorId": 2, "inResponseTo": 4, "senderLocation": point("48.09,81.01"), "message": " like samsung the plan is amazing" } } ] }
]
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 records associated with the group; each of the group’s message values appears in the msg field of the records in the msgs collection.
The group variable in SQL++ makes more complex, composable, nested subqueries over a group possible, which is important given the more complex data model of SQL++ (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 a record. (That wrapping is useful in more complex cases, but is essentially just in the way here.) We can use a subquery in the SELECT clase to tunnel through the extra nesting and produce the desired result.
SELECT uid, (SELECT VALUE m.msg FROM msgs m) AS msgs FROM GleambookMessages message GROUP BY message.authorId AS uid GROUP AS msgs(message AS msg);
This variant of the example query returns:
{ "uid": 1, "msgs": [ { "messageId": 8, "authorId": 1, "inResponseTo": 11, "senderLocation": point("40.33,80.87"), "message": " like verizon the 3G is awesome:)" },
{ "messageId": 10, "authorId": 1, "inResponseTo": 12, "senderLocation": point("42.5,70.01"), "message": " can't stand motorola the touch-screen is terrible" },
{ "messageId": 11, "authorId": 1, "inResponseTo": 1, "senderLocation": point("38.97,77.49"), "message": " can't stand at&t its plan is terrible" },
{ "messageId": 2, "authorId": 1, "inResponseTo": 4, "senderLocation": point("41.66,80.87"), "message": " dislike iphone its touch-screen is horrible" },
{ "messageId": 4, "authorId": 1, "inResponseTo": 2, "senderLocation": point("37.73,97.04"), "message": " can't stand at&t the network is horrible:(" } ] },
{ "uid": 2, "msgs": [ { "messageId": 6, "authorId": 2, "inResponseTo": 1, "senderLocation": point("31.5,75.56"), "message": " like t-mobile its platform is mind-blowing" },
{ "messageId": 3, "authorId": 2, "inResponseTo": 4, "senderLocation": point("48.09,81.01"), "message": " like samsung the plan is amazing" } ] }
Because this is a fairly common case, a third variant with output identical to the second variant is also possible:
SELECT uid, msg AS msgs FROM GleambookMessages message GROUP BY message.authorId AS uid GROUP AS msgs(message AS msg);
This variant of the query exploits a bit of SQL-style “syntactic sugar” that SQL++ offers to shorten some user queries. In particular, in the SELECT list, the reference to the GROUP variable field msg -- because it references a field of the group variable -- is allowed but is “pluralized”. As a result, the msg reference in the SELECT list is implicitly rewritten into the second variant's SELECT VALUE subquery.
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.
SELECT uid,
(SELECT VALUE m.msg
FROM msgs m
WHERE m.msg.message LIKE '% like%'
ORDER BY m.msg.messageId
LIMIT 2) AS msgs
FROM GleambookMessages message
GROUP BY message.authorId AS uid GROUP AS msgs(message AS msg);
This example query returns:
[
{ "uid": 1, "msgs": [ { "messageId": 8, "authorId": 1, "inResponseTo": 11, "senderLocation": point("40.33,80.87"), "message": " like verizon the 3G is awesome:)" } ] },
{ "uid": 2, "msgs": [ { "messageId": 3, "authorId": 2, "inResponseTo": 4, "senderLocation": point("48.09,81.01"), "message": " like samsung the plan is amazing" },
{ "messageId": 6, "authorId": 2, "inResponseTo": 1, "senderLocation": point("31.5,75.56"), "message": " like t-mobile its platform is mind-blowing" } ] }
]
In the SQL++ 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 in SQL++, much like the treatment of unnamed projections:
The next example illustrates a query that doesn't provide binding variables for its grouping key expressions.
SELECT authorId,
(SELECT VALUE m.msg
FROM msgs m
WHERE m.msg.message LIKE '% like%'
ORDER BY m.msg.messageId
LIMIT 2) AS msgs
FROM GleambookMessages message
GROUP BY message.authorId GROUP AS msgs(message AS msg);
This query returns:
[
{ "authorId": 1, "msgs": [ { "messageId": 8, "authorId": 1, "inResponseTo": 11, "senderLocation": point("40.33,80.87"), "message": " like verizon the 3G is awesome:)" } ] },
{ "authorId": 2, "msgs": [ { "messageId": 3, "authorId": 2, "inResponseTo": 4, "senderLocation": point("48.09,81.01"), "message": " like samsung the plan is amazing" },
{ "messageId": 6, "authorId": 2, "inResponseTo": 1, "senderLocation": point("31.5,75.56"), "message": " like t-mobile its platform is mind-blowing" } ] }
]
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).
The group variable itself is also optional in SQL++‘s 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.)
SELECT uid,
(SELECT m.message
FROM message m
WHERE m.message LIKE '% like%'
ORDER BY m.messageId
LIMIT 2) AS msgs
FROM GleambookMessages message
GROUP BY message.authorId AS uid;
This query returns:
[
{ "uid": 1, "msgs": [ { "message": " like verizon the 3G is awesome:)" } ] },
{ "uid": 2, "msgs": [ { "message": " like samsung the plan is amazing" },
{ "message": " like t-mobile its platform is mind-blowing" } ] }
]
Note that in the query above, in principle, message is not an in-scope variable in the SELECT clause. However, the query above is a syntactically-sugared simplification of the following query and it is thus legal, executable, and returns the same result:
SELECT uid,
(SELECT m.message
FROM (SELECT VALUE grp.message FROM `$1` AS grp) AS m
WHERE m.message LIKE '% like%'
ORDER BY m.messageId
LIMIT 2) AS msgs
FROM GleambookMessages message
GROUP BY message.authorId AS uid GROUP AS `$1` (message AS message);
In traditional SQL, which doesn't support nested data, grouping always also involves the use of aggregation compute properties of the groups (e.g., the average number of messages per user rather than the actual set of messages per user). Each aggregation function in SQL++ takes a collection (e.g., 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 SQL++ built-in aggregation functions 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 |
|---|---|---|---|
| COLL_COUNT | counted | counted | 0 |
| COLL_SUM | returns NULL | returns NULL | returns NULL |
| COLL_MAX | returns NULL | returns NULL | returns NULL |
| COLL_MIN | returns NULL | returns NULL | returns NULL |
| COLL_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 SQL++ has twice as many functions listed above as there are aggregate functions in SQL-92. This is because SQL++ 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.
ARRAY_AVG(
(
SELECT VALUE len(friendIds) FROM GleambookUsers
)
);
This example returns:
3.3333333333333335
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.
For compatibility with the traditional SQL aggregation functions, SQL++ also offers SQL-92's aggregation function symbols (COUNT, SUM, MAX, MIN, and AVG) as supported syntactic sugar. The SQL++ compiler rewrites queries that utilize these function symbols into SQL++ queries that only use the SQL++ collection aggregate functions. The following example uses the SQL-92 syntax approach to compute a result that is identical to that of the more explicit SQL++ example above:
SELECT uid, COUNT(msg) AS msgCnt FROM GleambookMessages msg GROUP BY msg.authorId AS uid;
It is important to realize that COUNT is actually not a SQL++ built-in aggregation function. Rather, the COUNT query above is using a special “sugared” function symbol that the SQL++ compiler will rewrite as follows:
SELECT uid AS uid, ARRAY_COUNT( (SELECT g.msg 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 SQL++ collection aggregate functions, 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).
SQL++ provides full support for SQL-92 GROUP BY aggregation queries. The following query is such an example:
SELECT msg.authorId, COUNT(msg) 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);
SQL++ also allows column aliases to be used as GROUP BY keys or ORDER BY keys.
SELECT msg.authorId AS aid, COUNT(msg) FROM GleambookMessages msg GROUP BY aid;
This query returns:
[
{ "aid": 1, "$1": 5 },
{ "aid": 2, "$1": 2 }
]
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.
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 following example returns all GleambookUsers ordered by their friend numbers.
SELECT VALUE user FROM GleambookUsers AS user ORDER BY len(user.friendIds) DESC;
This query returns:
[
{ "id": 1, "alias": "Margarita", "name": "MargaritaStoddard", "userSince": datetime("2012-08-20T10:10:00.000Z"), "friendIds": {{ 2, 3, 6, 10 }}, "employment": [ { "organizationName": "Codetechno", "startDate": date("2006-08-06") }, { "organizationName": "geomedia", "startDate": date("2010-06-17"), "endDate": date("2010-01-26") } ], "nickname": "Mags", "gender": "F" },
{ "id": 3, "alias": "Emory", "name": "EmoryUnk", "userSince": datetime("2012-07-10T10:10:00.000Z"), "friendIds": {{ 1, 5, 8, 9 }}, "employment": [ { "organizationName": "geomedia", "startDate": date("2010-06-17"), "endDate": date("2010-01-26") } ] }
{ "id": 2, "alias": "Isbel", "name": "IsbelDull", "userSince": datetime("2011-01-22T10:10:00.000Z"), "friendIds": {{ 1, 4 }}, "employment": [ { "organizationName": "Hexviafind", "startDate": date("2010-04-27") } ], "nickname": "Izzy" }
]
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.
SELECT VALUE user FROM GleambookUsers AS user ORDER BY len(user.friendIds) DESC LIMIT 1;
This query returns:
[
{ "id": 1, "alias": "Margarita", "name": "MargaritaStoddard", "userSince": datetime("2012-08-20T10:10:00.000Z"), "friendIds": {{ 2, 3, 6, 10 }}, "employment": [ { "organizationName": "Codetechno", "startDate": date("2006-08-06") }, { "organizationName": "geomedia", "startDate": date("2010-06-17"), "endDate": date("2010-01-26") } ], "nickname": "Mags", "gender": "F" }
]
As in standard SQL, WITH clauses are available to improve the modularity of a query. The next query shows an example.
WITH avgFriendCount AS ( SELECT VALUE AVG(LEN(user.friendIds)) FROM GleambookUsers AS user )[0] SELECT VALUE user FROM GleambookUsers user WHERE LEN(user.friendIds) > avgFriendCount;
This query returns:
[
{ "id": 1, "alias": "Margarita", "name": "MargaritaStoddard", "userSince": datetime("2012-08-20T10:10:00.000Z"), "friendIds": {{ 2, 3, 6, 10 }}, "employment": [ { "organizationName": "Codetechno", "startDate": date("2006-08-06") }, { "organizationName": "geomedia", "startDate": date("2010-06-17"), "endDate": date("2010-01-26") } ], "nickname": "Mags", "gender": "F" },
{ "id": 3, "alias": "Emory", "name": "EmoryUnk", "userSince": datetime("2012-07-10T10:10:00.000Z"), "friendIds": {{ 1, 5, 8, 9 }}, "employment": [ { "organizationName": "geomedia", "startDate": date("2010-06-17"), "endDate": date("2010-01-26") } ] }
]
The query is equivalent to the following, more complex, inlined form of the query:
SELECT *
FROM GleambookUsers user
WHERE LEN(user.friendIds) >
( SELECT VALUE AVG(LEN(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 SQL++ 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. SQL++, being designed to deal with nested data and schema-less data, does not (and should not) do this. Collection-valued data is perfectly legal in most SQL++ contexts, and its data is schema-less, so a query processor rarely knows exactly what to expect where and such automatic conversion is often not desirable. Thus, in the queries above, the use of “[0]” extracts the first (i.e., 0th) element of a list-valued query expression's result; this is needed above, even though the result is a list of one element, to “de-listify” the list and obtain the desired scalar for the comparison.
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.
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:
[
{ "messages": [ { "messageId": 8, "authorId": 1, "inResponseTo": 11, "senderLocation": point("40.33,80.87"), "message": " like verizon the 3G is awesome:)" }, { "messageId": 10, "authorId": 1, "inResponseTo": 12, "senderLocation": point("42.5,70.01"), "message": " can't stand motorola the touch-screen is terrible" }, { "messageId": 11, "authorId": 1, "inResponseTo": 1, "senderLocation": point("38.97,77.49"), "message": " can't stand at&t its plan is terrible" }, { "messageId": 2, "authorId": 1, "inResponseTo": 4, "senderLocation": point("41.66,80.87"), "message": " dislike iphone its touch-screen is horrible" }, { "messageId": 4, "authorId": 1, "inResponseTo": 2, "senderLocation": point("37.73,97.04"), "message": " can't stand at&t the network is horrible:(" } ], "uname": "MargaritaStoddard" },
{ "messages": [ { "messageId": 6, "authorId": 2, "inResponseTo": 1, "senderLocation": point("31.5,75.56"), "message": " like t-mobile its platform is mind-blowing" }, { "messageId": 3, "authorId": 2, "inResponseTo": 4, "senderLocation": point("48.09,81.01"), "message": " like samsung the plan is amazing" } ], "uname": "IsbelDull" }
]
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 can be used to combine two input streams into one. As in SQL, there is no ordering guarantee on the contents of the output stream. However, unlike SQL, SQL++ does not constrain what the data looks like on the input streams; in particular, it allows heterogenity on the input and output streams. The following odd but legal query is an 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 t-mobile its platform is mind-blowing",
" like samsung the plan is amazing",
{ "uname": "IsbelDull" }
]
In SQL++, 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 a list of up to two “dislike” messages per user.
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:
[
{ "uid": 1, "msgs": [ { "messageId": 2, "authorId": 1, "inResponseTo": 4, "senderLocation": point("41.66,80.87"), "message": " dislike iphone its touch-screen is horrible" } ] },
{ "uid": 2, "msgs": [ ] }
]
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.
The following matrix is a quick “key differences cheat sheet” for SQL++ and SQL-92.
| Feature | SQL++ | SQL-92 |
|---|---|---|
| SELECT * | Returns nested records | Returns flattened concatenated records |
| 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 |
| LEFT OUTER JOIN | Fills in MISSING for non-matches | Fills in NULL(s) for non-matches |
| UNION ALL | Allows heterogenous inputs and output | Input streams must be UNION-compatible and output field names are drawn from the first input stream |
| String literal | Double quotes or single quotes | Single quotes only |
| Delimited identifiers | Backticks | Double quotes |
For things beyond this cheat sheet, SQL++ is SQL-92 compliant. Morever, SQL++ offers the following additional features beyond SQL-92 (hence the “++” in its name):
GROUP BY clause are directly usable in nested queries and/or to obtain nested results.SELECT clause has to return a (homogeneous) collection of records.