# HTSQL Reference

This document describes the use of the HTSQL language. We start with describing the syntax of HTSQL, then we discuss the HTSQL data model and the query semantics and conclude with the list of built-in data types, functions and operators.

## Syntax

A valid input of an HTSQL processor is called an HTSQL query.

In a regular mode of operation, an HTSQL processor is started as a web service and accepts queries as HTTP GET requests. However, an HTSQL query can also be executed using a command-line utility htsql-ctl or via internal Python API.

### Encoding

An HTSQL query is a string of characters in UTF-8 encoding. Octets composing the string could be written literally or percent-encoded. A percent-encoded octet is serialized as three characters: % followed by two hexdecimal digits encoding the octet value.

Percent-encoding is useful for transmitting an HTSQL query via channels that forbid certain characters in literal form. The list of characters that should be encoded depends on the channel type, but the percent (%) character itself must always be percent-encoded.

A NUL character cannot appear in an HTSQL query, neither in literal nor in percent-encoded form.

The HTSQL processor decodes percent-encoded octets before parsing the query. As a consequence, a percent-encoded punctuation or operator character still plays its syntax role.

### Lexical Structure

An HTSQL query is parsed into a sequence of tokens. The following tokens are recognized.

NAME
A sequence of alphanumeric characters that does not start with a digit.
NUMBER
A numeric literal: integer, decimal and exponential notations are recognized.
STRING
A string literal enclosed in single quotes; any single quote character should be doubled.
SYMBOL
A valid symbol in the HTSQL grammar; that includes operators and punctuation characters. Some symbols are represented by more than one character (e.g. <=, !~).

Individual tokens may be separated by whitespace characters.

See :class:htsql.tr.scan.Scanner for detailed description of HTSQL tokens.

### Syntax Structure

A sequence of HTSQL tokens must obey the HTSQL grammar.

An HTSQL query starts with / followed by a valid HTSQL expression and is concluded with an optional query decorator.

The following table lists HTSQL operations in the order of precedence.

Operation Description Example Input Output
x :name infix function call 'HTSQL':length 5
x :name y 1/3 :round 2 0.33
x :name (y,z,...) 'HTSQL':slice(1,-1) 'TSQ'
x +, x - sorting decorator program{degree+}
p | q logical OR operator true()|false() true()
p & q logical AND operator true()&false() false()
! p logical NOT operator !true() false()
x = y, x != y, x == y, x !== y comparison operators 2+2=4 true()
x ~ y, x !~ y 'HTSQL'~'SQL' true()
x < y, x <= y, x > y, x >= y 12>7 true()
x + y, x - y addition, subtraction 'HT'+'SQL' 'HTSQL'
x * y, x / y multiplication, division 12*7 84
- x negation -42
T ^ x projection operator program^degree
T ? p sieve operator program?degree='ms'
T := x assignment student := student?is_active
S . T traversal operator school.program
{x,y,...} selection operator {count(school)}
T {x,y,...} school{code,name}
name (...) function call round(1/3,2) 0.33
(...) grouping (7+4)*2 22
* wildcard selection school.*
* number school.*1
^ projection complement count(^)
name   school
number   60, 2.125, 271828e-5
string   'HTSQL'

An optional query decorator starts with / followed : and the decorator name.

See :class:htsql.tr.parse.QueryParser for a formal description of the HTSQL grammar.

Below we describe individual syntax elements.

### Atomic Expressions

An atomic expression is a basic syntax unit. HTSQL recognizes the following atoms:

Identifier

An identifier is a sequence of characters which contains Latin letters, underscores (_), decimal digits and those Unicode characters that are classified as alphanumeric. An identifier must not start with a digit.

In HTSQL, identifiers are case-insensitive.

Identifiers are used to refer to database entities such as tables and attributes, to define calculated attributes, and to call functions.

In this example, four identifiers school, name, count and department represent respectively a table, a table attribute, a built-in function and a table link.

Literal

HTSQL supports two types of literal values: quoted and unquoted.

An unquoted (or numeric) literal is a number written in integer, decimal or exponential notation.

The range of allowed numeric values depends on the database backend. The type of a numeric literal is determined from notation: literals written in integer, decimal and exponential notation are assigned to integer, decimal and float data type respectively.

A quoted literal is a (possibly empty) sequence of arbitrary characters enclosed in single quotes. Any single quote in the value must be doubled.

The data type of a quoted literal is inferred from the context in which the literal is used; the default data type is string.

Wildcard

A wildcard selection (*) selects all output columns of the table.

When followed by an integer literal N, a wildcard selects N-th output column of the table. N starts from 1 and should not exceed the number of output columns.

Complement

A projection complement (^) represents a complement link from a projection to the projected table.

Do not confuse a projection complement with a binary projection operator, which is also represented with the ^ character.

In this example, the first and the second occurrences of ^ indicate a projection operator and a projection complement respectively.

Grouping

Any expression enclosed in parentheses ((...)) is treated syntactically as a single atom. Use grouping to override the default operator precedence.

Do not confuse a grouping operation with a function call, which also uses parentheses.

### Function Calls

HTSQL has a large library of built-in functions and can be extended with user-defined functions.

A function call is represented as a function name followed by (, a comma-separated list of arguments, and ).

A function may accept no arguments, but the parentheses are still required.

For functions with at least one argument, HTSQL supports an alternative infix call notation. In this notation, the expression starts with the first argument followed by : and a function name, and then the rest of the arguments. The trailing arguments must be enclosed in parentheses if their number is more than one.

This example could be equivalently expressed as

Infix function calls are composable and have the lowest precedence among the operators.

For a list and description of built-in functions, see the respective section of the reference.

### Operators

An HTSQL operator is denoted by a special character or a sequence of characters (e.g. +, <=). HTSQL has infix, prefix and postfix operators, and some operators admit all three forms.

The current version of HTSQL does not support user-defined operators; future versions may add this ability.

In HTSQL, the order in which the operators are applied is determined by operator precedence. For example, multiplication and division operators have a higher precedence than addition and subtraction.

Some HTSQL operators are composable (e.g. arithmetic operators) and some are not (e.g. equality operators). We call the former associative and the latter non-associative.

Logical Operators

HTSQL supports the following logical operators:

• p | q --- logical OR;
• p & q --- logical AND;
• ! p --- logical NOT.

In this list, the operators are sorted by the order of precedence, from lowest to highest. All logical operators are left-associative.

Comparison Operators

HTSQL supports the following comparison operators:

• x = y, x != y, x == y, x !== y --- equality operators;
• x ~ y, x !~ y --- containing operators;
• x < y, x <= y, x > y, x >= y --- ordering operators.

All comparison operators have the same precedence and are not associative.

Future versions of HTSQL may make ordering operators left-associative to express between operation (e.g. a <= x <= b).

Arithmetic Operators

HTSQL supports the usual set of arithmetic operators:

• x + y --- addition;
• x - y --- subtraction;
• x * y --- multiplication;
• x / y --- division;
• - x --- negation.

Arithmetic operators have standard precedence and associativity.

Table Operators

HTSQL supports specialized operators to work with table expressions:

• T ^ x --- projection operator;
• T ? p --- sieve operator;
• S . T --- traversal operator;
• T {x,y,...} --- selection operator.

The projection operator (T ^ x) produces a row set containing all unique values of x as it ranges over T. Do not confuse the projection operator with a projection complement.

The sieve operator (T ? p) produces rows of T satisfying condition p.

The traversal operator (S . T) evaluates T in the context of S.

The selection operator specifies output columns. The operator admits two forms: with and without the selection base.

Table operators have irregular precedence; for more details, see the HTSQL grammar. For a comprehensive description of the semantics of table operators, see the respective section of the reference.

Sorting Decorators

x + and x - are two sorting decorators indicating ascending and descending order respectively.

Sorting decorators have the same precedence as infix function call.

Sorting decorators are only meaningful when used as arguments of the sort() function and in a selector expression.

Assignment

T := x is an assignment expression.

The left side of an assignment must be an identifier, a function call or a traversal operator, and it indicates the name and formal parameters (if any) of a calculated attribute. The right side of an assignment is an arbitrary expression.

An assignment expression could be used only as an argument of functions define() and where().

For a comprehensive description built-in operators see the respective section.

## Data Model and Query Semantics

In this section, we describe how HTSQL represents information in the database and how the HTSQL translator interprets the queries.

### Data Model

HTSQL is not a full-fledged database system. As opposed to regular data stores, it does not include a storage layer, but relies on a relational database server to physically store and retrieve data.

HTSQL is designed to work on top of existing relational databases and does not impose any restrictions on how information is modeled and stored there. At the same time, HTSQL works best when the data in the database is highly normalized.

Even though HTSQL wraps a relational database, it does not expose the relational model directly to the users. Instead it derives HTSQL data model from the underlying database and uses this model when presenting data to the users and interpreting user queries. HTSQL data model is very close to traditional network data model utilized by CODASYL, and various OODBMS and ORM systems.

In the next sections, we describe HTSQL data model and how it is inferred from the relational model of the underlying database.

### Model and Instances

When describing how information is represented by HTSQL, we differentiate between a database model and a database instance.

A database model specifies the structure of the database: what types of business entities are represented and how the entities may relate to each other. A database instance is the actual data in the database and must satisfy the constraints imposed by the model. The difference between a model and an instance is the difference between the shape of data and data itself.

Let's consider the model of a student enrollment system in a fictional university. This model may contain schools, programs administered by a school, departments associated with a school, and courses offered by a department. A concrete instance of this model may contain a school of Engineering with associated departments of Computer Science, Electrical Engineering, etc.:

### Records and Relations

As we focus from the database model to a specific instance, classes are populated with values and records, and links are expanded to relations between individual items.

On the instance level, a domain class is transformed into a set of all values of the respective type. Thus, boolean class contains two values: true and false, integer class contains all integer numbers, and so on.

A record class becomes a set of records representing business entities of this class.

It is convenient to depict an entity as a collection of attribute values, hence the word "record". Even though it is permitted for two different records to have the same set of attribute values, in practice, there often exists an attribute or a group of attributes which could uniquely identify a record. We use the value of such an attribute enclosed in brackets to denote records in writing. Thus, an instance of class school may contain records [eng], [la], [ns] representing respectively schools of Engineering, of Arts and Humanities, and of Natural Sciences, assuming that we use attribute school.code to uniquely identify records.

A link between two classes is unwound into connections between elements of these classes. If in the database model a link represents an entity attribute, in a specific instance a link connects records to attribute values. A link between two record classes would connect records of these classes.

For example, link school.name connects a school record [eng] to a string value 'School of Engineering'. The record [eng] is also connected to department records [comp] and [ee] indicating that Department of Computer Science and Department of Electrical Engineering belong to School of Engineering.

Some links may enforce constraints on connections between elements. We classify these constraints as follows:

A link is called singular if any element of the origin class is connected to no more than one element of the target class. Otherwise, the link is called plural.

For example, all links representing attributes are singular; link department.school is also singular because each department may be associated with just one school, but the reverse link school.department is plural since a school may contain more than one department.

A link is called total if any element of the origin class is connected to at least one element of the target class. Otherwise, the link is called partial.

For example, we require that every school entity has a code, therefore attribute school.code is total. We also permit a department to lack an associated school, which means link department.school is partial.

A link is called unique if any element of the target class is connected to no more than one element of the origin class. Otherwise, the link is non-unique.

Attribute school.name is unique since different school entities must have different names, but link department.school is non-unique as different departments are allowed to be associated with the same school.

Note that links constraints are defined on the database model and applied to all instances of the model.

### Correspondence to the relational model

In this section, we explain how underlying relation database model is translated to HTSQL data model.

For the most part, translation of relational structure to HTSQL model is straightforward. SQL data types become domain classes, SQL tables become record classes, table columns are class attributes.

Column constraints are trivially translated to properties of the respective attributes. NOT NULL constraint on a table column means, in HTSQL terms, that the respective class attribute is total. UNIQUE constraint on a column indicates that the respective attribute link is unique. PRIMARY KEY constraint indicates that the attribute link is both total and unique.

The link structure of the model graph is provided by foreign key constraints. Specifically, a foreign key creates a singular link from the referring class to the referred class.

Consider, for example, the following fragment of an SQL schema:

CREATE TABLE ad.school (
code                VARCHAR(16) NOT NULL,
name                VARCHAR(64) NOT NULL,
campus              VARCHAR(5),
CONSTRAINT school_pk
PRIMARY KEY (code),
CONSTRAINT name_uk
UNIQUE (name),
CONSTRAINT school_campus_ck
CHECK (campus IN ('old', 'north', 'south'))
);

code                VARCHAR(16) NOT NULL,
name                VARCHAR(64) NOT NULL,
school_code         VARCHAR(16),
CONSTRAINT department_pk
PRIMARY KEY (code),
CONSTRAINT department_name_uk
UNIQUE (name),
CONSTRAINT department_school_fk
FOREIGN KEY (school_code)
);


HTSQL model of this schema consists of two classes, school and department, each with three attributes: code, name, campus and code, name, school_code respectively. Additionally, the foreign key constraint department_school_fk generates a singular link from class department to class school and a reverse plural link from class school to class department.

### Data Flow

A central concept in HTSQL is data flow, a sequence of homogeneous values.

HTSQL is a data flow transformation language. Every HTSQL expression operates on flows; that is, it accepts an input flow and transforms it into an output flow.

(diagram: input flow -> expression -> output flow)

The initial input flow consists of a single empty record. HTSQL processor interprets the given HTSQL query as a sequence (or rather a directed graph) of flow operations, which it applies one by one. The resulting flow is then displayed to the user in a tabular form.

Different operations affect the flow in various ways: multiply it, or remove elements from it, apply a scalar function to each element, etc. In the next sections, we discuss different types flow operations.

### Scalar Expressions

A simplest example of a flow operation is an application of some expression to each value in a flow. The output flow consists of a results of the expression.

That kind of expression does not change the number of elements in the flow; we call such expressions scalar.

In this example, a scalar expression (3+4)*6 is applied to the initial flow; the value of this expression forms the resulting flow.

(diagram: [] -> (3+4)*6 -> 42)

In this example, two scalar expressions code and count(department) are applied to the flow consisting of school records. For each school entity, they extract the value of the attribute code and the number of associated departments.

(diagram: [art], [bus], [edu], [eng], ...
-> {'art',2}, {'bus',3}, {'edu',2}, {'eng',4}, ...)

A scalar expression is an example of a singular expression; one which does not increase the number of elements in the flow, as opposed to a plural expression, which may produce more output elements than in the input flow.

Navigation is an operation of selecting the initial record class or traversing a link.

When used in the root scope, a class name produces a flow of all records from the class.

(diagram: [] -> (school) -> [art], ...)

In a class scope, the name of a link produces the flow consisting of associated records from the target class.

(diagram: [] -> (school) -> [art], ... -> (department) -> [arthis], ...)

As in the previous example, school generates a flow of school records. Then we traverse a link school.department. That each, for each school record in the input flow, we find the associated department records, the output flow consists of all department records combined.

A traversal operation is singular or plural depending on whether the respective link is singular or plural.

### Filtering

A sieve expression filters the input flow leaving only those elements which satisfy the given condition.

A sieve expression takes one argument: a scalar logical expression called the filter. It applies the filter to each element of the input flow. The output flow consists of those elements of the input flow for which the filter is evaluated to TRUE value.

(diagram)

In this example, the sieve expression evaluates a filter condition count(department)>3 for each record from the school class; those records for which this condition is valid generate the output of the query.

A sort expression reorders elements in the flow according to a given argument.

(diagram)

In this example, the school records are ordered in the ascending order with respect to the value of name attribute.

A truncation operation makes a slice of the input flow.

(diagram)

In this case, we take the top 3 records from the school class.

### Aggregates

An aggregate function converts a plural expression into a scalar.

The argument of an aggregate function must be a plural expression. Then for each element of the input flow, the aggregate evaluates the respective sub-flow and applies a set function to the result to generate a scalar value.

(diagram)

In this example, count() aggregates produces the number of elements in the flow generated by expression school.

(diagram)

In this example, max(course.credits) starts with evaluating the flow department.course.credits. Then for each department record of the input flow, max() finds the maximum value in the respective sub-flow.

### Projection

A projection expression takes a scalar argument called the kernel. The output flow of projection consists of all unique values of the kernel as it runs over the input flow.

(diagram)

The output of this query consists of all distinct values of school.campus attribute.

### Naming Scope

In HTSQL, identifiers are used to refer to class names, attributes, links as so on. A collection of available names and associated objects is called a naming scope.

### Root Scope

The root scope is the top level scope in the scope stack -- it is the scope where the query is evaluated. This scope contains the names of all classes (tables) in the database.

In this example, identifiers school and department belong to the root scope and are associated with the respective classes.

### Class Scope

The class scope is associated with some class (table) of the database. The scope contains names of all class attributes and links to other classes.

In this example, school belongs to the root scope while identifiers code, department and program belong to the scope of school class. school.code is the attribute of school, school.department and school.program are links to the respective classes.

### Projection Scope

The projection scope is associated with a projection expression.

Projection is an example of a derived class: its records are composed from unique values of the kernel as it runs over the base class. A projection class has a natural link back to the base class: it relates the value of the kernel to every record of the base class that produced this value.

(diagram)

This link is called a complement link. HTSQL assigns the name for the link that coincides with the name of the base class. In cases when HTSQL is unable to deduce a link name, one may use a special complement expression: ^.

Attributes of the projection class are values of the kernel expression. When possible, HTSQL automatically assigns names for attributes, otherwise, the user may define custom attribute names.

In this example, the projection scope (school^campus) has two names: the attribute name campus and the kernel link school.

In this example, we assign the name num_dept to the projection attribute.

### Modifying Scope

HTSQL allows adding new attributes to an existing scope, see functions define() and where().

### References

Traversing a link changes the scope; any names defined in the previous scope are no longer available. To pass values between different scopes, use references.

## Data Types

Every HTSQL expression has an associated type. The type defines the set of valid values produced by the expression and permitted operations over the expression. The type also indicates how returned values are formatted in the output.

Since HTSQL wraps an SQL database, HTSQL data types are related to SQL data type. Although HTSQL does not expose SQL data types directly to the user, each SQL data type corresponds to some HTSQL data type and vice versa.

In this section we describe what data types HTSQL supports, how HTSQL types are mapped to SQL types, the format of input literals for each data type, etc.

### Regular and Special Types

HTSQL demands that every expression has an associated type. For example, in the query:

the expressions 2+2=4, count(school), date('2010-04-15')-6813 have the types boolean, integer and date respectively. These are regular types.

The following table lists the default set of supported regular data types in HTSQL; more data types could be added by HTSQL extensions.

Type Description Example Input
boolean logical data type, with two values: TRUE and FALSE true()
false()
integer binary integer type 4096
decimal arbitrary-precision exact numeric type 124.49
float IEEE 754 floating-point inexact numeric type 271828e-5
string text data type string('HTSQL')
enum enumeration data type, with predefined set of valid string values
date calendar date date('2010-04-15')
time time of day time('20:13:04.5')
datetime date and time combined datetime('2010-04-15 20:13:04.5')
opaque unrecognized data type

Some HTSQL expressions do not produce a proper value and therefore cannot be assigned a regular data type. In this case, the expression is assigned one of the special data types: record, untyped or void.

Record entities are assigned the record type. This type is special since values of this type are never displayed directly and it has no corresponding SQL data type.

Quoted HTSQL literals have no intrinsic data type; their actual type is determined from the context. Until it is determined, HTSQL translator assign them a temporary untyped type.

Some expressions, such as assignments, produce no values and therefore have no meaningful data type. In this case, the assigned type is void.

The following table lists supported special data types.

Type Description Example Input
record type of record entities school
untyped initial type of quoted literals 'HTSQL'
void type without any valid values

### Literal Expressions

A literal expression is an atomic expression that represents a fixed value. HTSQL supports two types of literals: numeric (or unquoted) and quoted.

An unquoted literal is a number written in one of the following forms:

• an integer number
• a number with a decimal point
• a number in exponential notation

Literals in these forms are assigned integer, decimal and float types respectively.

A quoted literal is an arbitrary string value enclosed in single quotes.

In this example, three literal expressions represent an empty string, HTSQL and O'Reilly respectively. Note that to represent a single quote in the value, we must duplicate it.

As opposed to numeric literals, quoted literals have no intrinsic type, their type is determined from the context. Specifically, the type of a quoted literal is inferred from the innermost expression that contains the literal. Until the actual data type of a quoted literal is determined, the literal is assigned an untyped type.

Consider a query:

Here, a quoted literal '4' is a right operand of an equality expression, and its left counterpart 2+2 has the type integer. Therefore, HTSQL processor is able to infer integer for the literal '4'.

There is no generic rule how to determine the type of a quoted literal; every operator and function have different rules how to treat untyped values. However the content of the literal is never examined when determining its data type. It is possible to explicitly specify the type of an unquoted literal by applying a cast operator.

Here, the same quoted literal is converted to string and date data types respectively. Each data type has a set of quoted literals it accepts; it is an error when the quoted literal does not obey the format expected by a particular type.

Note the error generated because 'HTSQL' is not a valid format for an integer literal.

### Type Conversion

Expressions of one type could be explicitly converted to another type using a cast function. A cast function is a regular function with one argument; the name of the function coincides with the name of the target type.

Not every conversion is permitted; for instance, an integer value could be converted to a string, but not to a date:

Implicit type conversion is called coercion. In an arithmetic formulas and other expressions that require homogeneous arguments, when the operands are of different types, values of less generic types are converted to the most generic type. The order of conversion is as follows:

• integer
• decimal
• float

### Boolean

Type boolean is a logical data type with two values: TRUE and FALSE.

The following table maps the boolean type to respective native data types.

Backend Native types
sqlite BOOL, BOOLEAN or any type containing BOOL in its name
pgsql BOOLEAN
mysql BOOL aka BOOLEAN aka TINYINT(1)
oracle NUMBER(1) CHECK (X IN (0, 1))
mssql BIT

## Function Syntax

A few observations about HTSQL's function and operator usage:

• For any function, "f(x,y)" can be written "x :f(y)" and depending upon grammatical context, abbreviated to "x :f y".
• Unless annotated, functions are null-regular, that is, if any of their arguments is null() then the result is null().
• HTSQL uses zero-based indexes, e.g. the 1st item in a collection is indexed by 0, the 2nd character indexed by 1, and so on.
• A single quoted string in an HTSQL request is an untyped literal, and is automatically cast depending upon the context -- it is not necessarily a string value.

## Logical Operators

Function Description Example Input Output
boolean(x) cast x to Boolean boolean(true()) true()
boolean(false()) false()
boolean(1) true()
boolean(string('HTSQL')) true()
boolean(string('')) false()
boolean(date('2010-04-15')) true()
boolean(null()) null()
boolean(string(null())) false()
true() logical TRUE value true()
false() logical FALSE value false()
p & q logical AND operator; treats nulls as UNKNOWN true()&true() true()
true()&false() false()
false()&false() false()
true()&null() null()
false()&null() false()
p | q logical OR operator; treats nulls as UNKNOWN true()|true() true()
true()|false() true()
false()|false() false()
true()|null() true()
false()|null() null()
!p logical NOT operator; treats nulls as UNKNOWN !true() false()
!false() true()
!null() null()
is_null(x) x is null is_null(null()) true()
x = y x is equal to y 'HTSQL'='QUEL' false()
2=null() null()
x != y x is not equal to y 'HTSQL'!='QUEL' true()
2!=null() null()
x == y x is equal to y; treats nulls as regular values 'HTSQL'=='QUEL' false()
2==null() false()
x !== y x is not equal to y; treats nulls as regular values 'HTSQL'!=='QUEL' true()
2!==null() true()
x = {a,b,c,...} x is among a, b, c, ... 5={2,3,5,7}' true()
x != {a,b,c,...} x is not among a, b, c, ... 5!={2,3,5,7}' false()
x < y x is less than y 1<10 true()
'omega'<'alpha' false()
x <= y x is less than or equal to y 1<=10 true()
'omega'<='alpha' false()
x > y x is greater than y 1>10 false()
'omega'>'alpha' true()
x >= y x is greater than or equal to y 1>=10 false()
'omega'>='alpha' true()
if_null(x,y) x if x is not null; y otherwise if_null(1,0) 1
if_null(null(),0) 0
null_if(x,y) x if x is not equal to y; null otherwise null_if(1,0) 1
null_if(0,0) null()
if(p1,c1,...,pn,cn) first ck such that pk is TRUE; o or null otherwise if(true(),'up','down') 'up'
if(p1,c1,...,pn,cn,o) if(false(),'up','down') 'down'
switch(x,y1,c1,...,yn,cn) first ck such that x is equal to yk; o or null otherwise switch(1,1,'up',0,'down') 'up'
switch(x,y1,c1,...,yn,cn,o) switch(0,1,'up',0,'down') 'down'

## Numeric Functions

Function Description Example Input Output
integer(x) cast x to integer integer(60) 60
integer(17.25) 17
integer(223607e-5) 2
integer(string('60')) 60
decimal(x) cast x to decimal decimal(60) 60.0
decimal(17.25) 17.25
decimal(223607e-5) 2.23607
decimal(string('17.25')) 17.25
float(x) cast x to float float(60) 6e1
float(17.25) 1725e-2
float(223607e-5) 223607e-5
float(string('223607e-5')) 223607e-5
-x negate x -7
x + y add x to y 13+7 20
x - y subtract y from x 13-7 6
x * y multiply x by y 13*7 91
x / y divide x by y 13/7 1.85714285714286
round(x) round x to the nearest integer round(17.25) 17
round(x,n) round x to n decimal places round(17.25,1) 17.3

## String Functions

By convention, string functions take a string as its first parameter. When an untyped literal, such as 'value' is used and a string is expected, it is automatically cast. Hence, for convenience, we write string typed values using single quotes in the output column.

Function Description Example Input Output
string(x) cast x to string string('Hello') 'Hello'
string(1.0) '1.0'
string(null()) null()
string(true()) 'true'
string(false()) 'false'
string(date('2010-04-15')) '2010-04-15'
length(s) number of characters in s length('HTSQL') 5
x + y concatenate x and y; treats nulls as empty strings 'Hello' + ' World' 'Hello World'
'Hello' + null() 'Hello'
x ~ y x contains y; case-insensitive 'HTSQL' ~ 'sql' true()
x !~ y x does not contain y; case-insensitive 'HTSQL' !~ 'sql' false()
tail(s) last character of s tail('HTSQL') 'L'
tail(s,n) last n characters of s tail('HTSQL',3) 'SQL'
tail('HTSQL',-2) 'SQL'
slice(s,i,j) i-th to j-th characters of s; null or missing index means the beginning or the end of the string slice('HTSQL',1,4) 'TSQ'
slice('HTSQL',-4,-1) 'TSQ'
slice('HTSQL',null(),2) 'HT'
slice('HTSQL',2,null()) 'SQL'
at(s,k) k-th character of s at('HTSQL',2) 'S'
at(s,k,n) n characters of s starting with k-th character at('HTSQL',1,3) 'TSQ'
at('HTSQL,-4,3) 'TSQ'
at('HTSQL,4,-3) 'TSQ'
upper(s) upper case of s upper('htsql') 'HTSQL'
lower(s) lower case of s lower('HTSQL') 'htsql'
trim(s) strip leading and trailing spaces from s trim('  HTSQL  ') 'HTSQL'
ltrim(s) strip leading spaces from s ltrim('  HTSQL  ') 'HTSQL  '
rtrim(s) strips trailing spaces from s rtrim('  HTSQL  ') '  HTSQL'
replace(s,x,y) replace all occurences of x in s with y; in s with y; null x is treated as an empty string replace('HTSQL','SQL','RAF') 'HTRAF'
replace('HTSQL',null(),'RAF') 'HTSQL'

## Date Functions

Function Description Example Input Output
date(x) cast x to date date('2010-04-15')
date(yyyy,mm,dd) date yyyy-mm-dd date(2010,4,15) date('2010-04-15')
date(2010,3,46) date('2010-04-15')
date(2011,-8,15) date('2010-04-15')
today() current date today()
year(d) year of d year(date('2010-04-15')) 2010
month(d) month of d month(date('2010-04-15')) 4
day(d) day of d day(date('2010-04-15')) 15
d + n increment d by n days date('1991-08-20')+6813 date('2010-04-15')
d - n decrement d by n days date('2028-12-09')-6813 date('2010-04-15')
d1 - d2 number of days between d1 and d2 date('2028-12-09')-date('1991-08-20') 13626

## Aggregate Functions

Function Description Example Input Output
exists(ps) TRUE if ps contains at least one TRUE value; FALSE otherwise exists(course.credits>5)
every(ps) TRUE if ps contains only TRUE values; FALSE otherwise every(course.credits>5)
count(ps) number of TRUE values in ps count(course.credits>5)
min(xs) smallest x in sx min(course.credits)
max(xs) largest x in sx max(course.credits)
sum(xs) sum of x in xs sum(course.credits)
avg(xs) average value of x in xs avg(course.credits)

Function Description Example Input Output
chain . attr extract attribute value school.name
chain . * extract all attributes school.*
chain ? p records from chain satisfying condition p school?code='edu'
chain.sort(x,...) records from chain sorted by x, ... course.sort(credits-)
chain.limit(n) first n records from chain course.limit(10)
chain.limit(n,k) n records from chain starting from k-th course.limit(10,20)
chain {x,...} select x, ... from chain school{code,name}
root() scalar class
this() current chain

## Decorators

Function Description Example Input Output
as(x,title) set the column title number :as 'No.'
x+ sort by x in ascending order credits+
x- sort by x in descending order credits-

## Formatters

Function Description Example Input Output
/:html HTML tabular output
/:txt plain text tabular output
/:csv CSV (comma-separated values) output
/:json JSON-serialized output