HTSQL Tutorial¶
Table of Contents
A query language for the accidental programmer
HTSQL makes accessing data as easy as browsing the web. An HTSQL processor translates web requests into relational database queries and returns the results in a form ready for display or processing. Information in a database can then be directly accessed from a browser, a mobile application, statistics tool, or a rich Internet application. Like any web resource, an HTSQL service can be secured via encryption and standard authentication mechanisms – either on your own private intranet or publicly on the Internet.
HTSQL users are data experts. They may be business users, but they can also be technical users who value data transparency and direct access. Business users can use HTSQL to quickly search the database and create reports without the help of IT. Programmers can use it as data access layer for web applications. HTSQL can be installed by DBAs to provide easy, safe database access for power users.
HTSQL is a schema-driven URI-to-SQL translator that takes a request over HTTP, converts it to a set of SQL queries, executes these queries in a single transaction, and returns the results in a format (CSV, HTML, JSON, etc.) requested by the user agent:

The HTSQL query processor does heavy lifting for you. Using relationships between tables as permitted links, the HTSQL processor translates graph-oriented web requests into corresponding relational queries. This translation can be especially involved for sophisticated requests having projections and aggregates. For complex cases, an equivalent hand-written SQL query is tedious to write and non-obvious without extensive training. By doing graph to relational mapping on your behalf, HTSQL permits your time to be spent exploring information instead of debugging.
The HTSQL language is easy to use. We’ve designed HTSQL to be broadly usable by semi-technical domain experts, or what we call accidental programmers. We’ve field tested the toolset with business analysts, medical researchers, statisticians, and web application developers. By using a formalized directed graph as the underpinning of the query algebra and by using a URI-inspired syntax over HTTP, we’ve obtained a careful balance between clarity and functionality.
We hope you like it.
Getting Started¶
The following examples show output from the HTSQL command-line system, which is plain text. HTSQL can output HTML, CSV, XML and many other formats. This makes it suitable not only for direct queries, but as a data access layer for application development.
We’ll use a fictional university that maintains a database for its student enrollment system. There are four tables that describe the business units of the university and their relationship to the courses offered:

The university consists of schools, which administer one or more degree-granting programs. Departments are associated with a school and offer courses. Further on in the tutorial we will introduce other tables such as student, instructor and enrollment.
Basic Expressions¶
HTSQL requests start with a forward-slash /
. To return all rows and
columns from the school table, sorted by primary key, write:
code | name | campus |
---|---|---|
art | School of Art & Design | old |
bus | School of Business | south |
edu | College of Education | old |
Hint
In this tutorial query results are shown truncated. Click on any example request to open it at demo.htsql.org and see the complete output.
Scalar expressions, including arithmetic and boolean operations, can be written directly:
42 |
HTSQL has many built-in functions. For instance you could use the
function count()
to get the number of rows in a table:
HTSQL uses a regular and intuitive syntax for expressions ranging from table selection to complex calculation.
Choosing Columns¶
Use a selector, marked with {
curley braces }
, to specify
more than one output column:
count(school) | count(program) | count(department) |
---|---|---|
9 | 40 | 27 |
When returning data from a table, use a selector to choose columns for display:
school_code | code | title |
---|---|---|
art | gart | Post Baccalaureate in Art History |
art | uhist | Bachelor of Arts in Art History |
art | ustudio | Bachelor of Arts in Studio Art |
bus | gecon | Master of Arts in Economics |
In addition to table attributes, you could select arbitrary expressions. The following example displays, for each school record, the school’s name and the number of associated departments:
name | count(department) |
---|---|
School of Art & Design | 1 |
School of Business | 3 |
College of Education | 2 |
School of Engineering | 4 |
To title an output column, use the :as
decorator:
name | # of Dept. |
---|---|
School of Art & Design | 1 |
School of Business | 3 |
College of Education | 2 |
Since HTSQL is a web query language, there are two characters that have
special meaning: %
is used to encode reserved and unprintable
characters as hexadecimal UTF-8 octets; #
represents query fragments
that can be truncated by your browser. Hence, these characters must be
percent-encoded in HTSQL queries: %
is written %25
; #
is
written %23
. Depending upon the browser, other characters may be
percent-encoded, for example, the space `` `` may show up as %20
.
Linking Data¶
In our example schema, each program
is administered by a school
.
Since the HTSQL processor knows about this relationship, it is possible
to link data accordingly:
name | title |
---|---|
School of Art & Design | Post Baccalaureate in Art History |
School of Art & Design | Bachelor of Arts in Art History |
School of Art & Design | Bachelor of Arts in Studio Art |
It is possible to link data through several relationships. Since course
is offered by a department
which belongs to a school
, we can write:
name | name | title |
---|---|---|
School of Business | Accounting | Practical Bookkeeping |
School of Business | Accounting | Introduction to Accounting |
School of Business | Accounting | Accounting Information Systems |
School of Business | Accounting | Financial Accounting |
This request can be shortened a bit by collapsing the duplicate mention
of department
; the resulting request is equivalent:
For cases where you don’t wish to specify each column explicitly, use
the wildcard *
selector. The request below returns all columns from
department
and all columns from its correlated school
:
code | name | school_code | code | name | campus |
---|---|---|---|---|---|
acc | Accounting | bus | bus | School of Business | south |
arthis | Art History | la | la | School of Arts and Humanities | old |
astro | Astronomy | ns | ns | School of Natural Sciences | old |
be | Bioengineering | eng | eng | School of Engineering | north |
Since the HTSQL processor knows about relationships between tables in your relational database, linking tables in your reports is trivial.
Filtering Data¶
Use the filter operator ?
to show only data that satisfies some
criteria. For example, to return departments in the School of
Engineering we can write:
code | name | school_code |
---|---|---|
be | Bioengineering | eng |
comp | Computer Science | eng |
ee | Electrical Engineering | eng |
me | Mechanical Engineering | eng |
This request returns all records in the department
table where the
column school_code
is equal to 'eng'
. In HTSQL, literal
values are single quoted so that 'eng'
isn’t confused with a column
name.
For a case-insensitive substring match, use the ~
operator:
school_code | code | title | degree | part_of_code |
---|---|---|---|---|
edu | glited | Master of Arts in Literacy Education | ma | |
la | upolisci | Bachelor of Arts in Political Science | ba |
Often times we want to compare a column against values from a list. The
next example returns rows from the program
table for the “Bachelors
of Arts” ('ba'
) or “Bachelors of Science” ('bs'
) degrees:
school_code | code | title | degree | part_of_code |
---|---|---|---|---|
art | uhist | Bachelor of Arts in Art History | ba | |
art | ustudio | Bachelor of Arts in Studio Art | ba | |
bus | uacct | B.S. in Accounting | bs | |
Complex filters can be created using boolean connectors, such as the
conjunction (&
), alternation (|
), and negation (!
)
operators. The following request returns programs in the “School of
Business” that do not grant a “Bachelor of Science” degree:
school_code | code | title | degree | part_of_code |
---|---|---|---|---|
bus | gecon | Master of Arts in Economics | ma | |
bus | pacc | Graduate Certificate in Accounting | ct | |
bus | pbusad | Certificate in Business Administration | ct | |
Filters can be combined with selectors and links. The following request returns courses, listing only department number and title, having less than 3 credits in the “School of Natural Science”:
department_code | no | title |
---|---|---|
astro | 106 | General Astronomy I Lab |
astro | 211 | General Astronomy II Lab |
astro | 241 | Telescope Workshop |
astro | 329 | Radio Astronomy Lab |
Sorting & Truncating¶
By default, with a simple table expression such as /school
, all rows
are returned in the order of the primary key columns. To override the
sort order, you can use sort()
function:
code | name | campus |
---|---|---|
edu | College of Education | old |
ph | Public Honorariums | |
art | School of Art & Design | old |
la | School of Arts and Humanities | old |
Sort direction can be specified explicitly using +
for ascending and
-
for descending order. Also, you can sort by multiple columns. The
following example sorts courses in ascending order by department and
then in descending order by number of credits:
department_code | no | title | credits | description |
---|---|---|---|---|
acc | 620 | Accounting Internship | 6 | Supervised internship in a business or nonprofit organization on an accounting position. |
acc | 315 | Financial Accounting | 5 | Integration of the conceptual and computational aspects of asset, liability and stockholders equity accounting. |
When sorting by a selected output column, you could use a shortcut syntax which combines column selection and sorting:
department_code | no | credits | title |
---|---|---|---|
acc | 620 | 6 | Accounting Internship |
acc | 315 | 5 | Financial Accounting |
acc | 511 | 5 | Audit |
acc | 200 | 3 | Introduction to Accounting |
To list a range of rows, the limit()
function takes one or two
arguments. The first argument is the number of rows to return, the
optional second argument is the starting offset. The next example
returns 5 records from the program table, skipping first 10 rows:
school_code | code | title | degree | part_of_code |
---|---|---|---|---|
edu | gedu | M.S. in Education | ms | |
edu | glited | Master of Arts in Literacy Education | ma | |
edu | gtch | Master of Arts in Teaching | ma | |
edu | psci | Certificate in Science Teaching | ct | |
edu | umath | Bachelor of Arts in Math Education | ba |
Formatting Output¶
By default, HTSQL tries to guess the desired output format depending
upon the browser or the tool used to make the request. This can be
overridden with a format decorator, such as /:json
. For example,
results in JSON format can be requested as follows:
{
"school": [
{
"code": "art",
"name": "School of Art & Design",
"campus": "old"
},
{
"code": "bus",
"name": "School of Business",
"campus": "south"
},
{
"code": "edu",
"name": "College of Education",
"campus": "old"
},
{
"code": "eng",
"name": "School of Engineering",
"campus": "north"
},
{
"code": "la",
"name": "School of Arts and Humanities",
"campus": "old"
},
{
"code": "mus",
"name": "School of Music & Dance",
"campus": "south"
},
{
"code": "ns",
"name": "School of Natural Sciences",
"campus": "old"
},
{
"code": "ph",
"name": "Public Honorariums"
},
{
"code": "sc",
"name": "School of Continuing Studies"
}
]
}
Other formats include /:txt
for plain-text formatting, /:html
for display in web browsers, and /:csv
for data exchange.
Putting it Together¶
HTSQL is a composable language where individual query fragments can be
combined into more complex expressions. For example, a selection on the
course table such as /course{department, no, title}
and a filter on
the course table, /course?credits<3
can be combined in either of the
following two forms:
department_code | no | title |
---|---|---|
acc | 100 | Practical Bookkeeping |
astro | 106 | General Astronomy I Lab |
astro | 211 | General Astronomy II Lab |
department_code | no | title |
---|---|---|
acc | 100 | Practical Bookkeeping |
astro | 106 | General Astronomy I Lab |
astro | 211 | General Astronomy II Lab |
Note that the order in which selection and filter operators are applied doesn’t affect the output. You could also use a functional form:
For the following two equivalent examples, we combine 3 operators – sorting, truncating, and selection:
department_code | no | credits |
---|---|---|
be | 415 | 8 |
acc | 620 | 6 |
arthis | 209 | 6 |
department_code | no | credits |
---|---|---|
be | 415 | 8 |
acc | 620 | 6 |
arthis | 209 | 6 |
The relative position of sort and limit matter, switching the positions will change the output:
department_code | no | credits |
---|---|---|
acc | 315 | 5 |
acc | 511 | 5 |
acc | 200 | 3 |
The following example requests the top 5 departments from schools with
'art'
in their name, sorted in descending order by the number of
courses. The output columns include the corresponding school name, the
name of the department itself, and the number of courses. The output
format is “Comma-Separated Values” suitable for consumption by
spreadsheet or statistical analysis packages:
name,name,count(course)
School of Arts and Humanities,English,21
School of Arts and Humanities,Foreign Languages,21
School of Arts and Humanities,Art History,20
School of Arts and Humanities,Political Science,19
School of Art & Design,Studio Art,19
HTSQL requests are powerful without being complex. They are easy to read and modify. They adapt to changes in the database. These qualities increase the usability of databases by all types of users and reduce the likelihood of costly errors.
Relating and Aggregating Data¶
HTSQL distinguishes between singular and plural relationships to
simplify query construction. By a singular relationship we mean for
every record in one table, there is at most one record in a linked
table; by plural we mean there is perhaps more than one correlated
record. To select a plural expression in a result set, an aggregate
function, such as sum
, count
, or exists
must be used. In
this way, what would be many values are converted into a single data cell
and integrated into a coherent result set.
By requiring aggregates for plural expressions, HTSQL reduces query construction time and reduces errors. When a query starts with a table, rows returned are directly correlated to records in this table. Since cross products or projections cannot be created accidentally, the combined result set is always consistent and understandable.
Basic Linking¶
One-to-many relationships are the primary building block of relational
structures. In our schema, each course
is offered by a
department
with a mandatory foreign key. For each course, there is
exactly one corresponding department. In this case, the relationship is
singular in one direction and plural in the other.
If each row in your result set represents a course
, it is easy to
get correlated information for each course’s department:
name | title |
---|---|
Accounting | Practical Bookkeeping |
Accounting | Introduction to Accounting |
Accounting | Accounting Information Systems |
It’s possible to join up a hierarchy in this way, but not down. If
each row in your result set is a department
, then it is an error to
request course
‘s credits
since there could be many courses in a
given department:
<!DOCTYPE html>
<html>
<head>
<title>Bad Request</title>
<link rel="stylesheet" href="/error_pages/normalize.css">
<link rel="stylesheet" href="/error_pages/style.css">
</head>
<body>
<div id="container-left">
<h1>The server did not understand your request.</h1>
<p>Please try going back to the previous page.</p>
<p>If you would like to report this problem to your site administrator, please make note of the steps you took before arriving here.</p>
<p class="error-type">[400 Bad Request]</p>
</div>
<div id="container-right">
<img id="logo" src="/error_pages/RexDB Text.png">
</div>
</body>
</html>
In cases like this, an aggregate function, such as max
is needed to
convert a plural expression into a singular value. The following
example shows the maximum course credits by department:
name | max(course.credits) |
---|---|
Accounting | 6 |
Art History | 6 |
Astronomy | 5 |
Conversely, you cannot use aggregates with singular expressions. For
example, since school
is singular relative to department
, it is
an error to count them:
<!DOCTYPE html>
<html>
<head>
<title>Bad Request</title>
<link rel="stylesheet" href="/error_pages/normalize.css">
<link rel="stylesheet" href="/error_pages/style.css">
</head>
<body>
<div id="container-left">
<h1>The server did not understand your request.</h1>
<p>Please try going back to the previous page.</p>
<p>If you would like to report this problem to your site administrator, please make note of the steps you took before arriving here.</p>
<p class="error-type">[400 Bad Request]</p>
</div>
<div id="container-right">
<img id="logo" src="/error_pages/RexDB Text.png">
</div>
</body>
</html>
For single row or scalar expressions, an aggregate is always needed when referencing a table. For example, the query below returns maximum number of course credits across all departments:
Aggregate Expressions¶
Since school
table has a plural (one to many) relationship
with program
and department
, we can count them:
name | count(program) | count(department) |
---|---|---|
School of Art & Design | 3 | 1 |
School of Business | 6 | 3 |
College of Education | 7 | 2 |
School of Engineering | 8 | 4 |
Filters may be used within an aggregate expression. For example, the following returns the number of courses, by department, that are at the 400 level or above:
name | count(course?no>=400) |
---|---|
Accounting | 7 |
Art History | 9 |
Astronomy | 5 |
Bioengineering | 3 |
It’s possible to nest aggregate expressions. This request returns the average number of courses each department offers:
name | avg(department.count(course)) |
---|---|
School of Art & Design | 19.0 |
School of Business | 14.6666666667 |
College of Education | 17.5 |
Filters and nested aggregates can be combined. Here we count, for each school, departments offering 4 or more credits:
name | count(department?exists(course?credits>3)) |
---|---|
School of Art & Design | 1 |
School of Business | 3 |
College of Education | 1 |
Filtering can be done on one column, with aggregation on another. This example shows average credits from only high-level courses:
name | avg((course?no>400).credits) |
---|---|
Accounting | 3.71428571429 |
Art History | 3.55555555556 |
Astronomy | 2.8 |
Bioengineering | 4.33333333333 |
Numerical aggregates are supported. These requests compute some useful
course.credit
statistics:
code | min(course.credits) | max(course.credits) |
---|---|---|
acc | 2 | 6 |
arthis | 3 | 6 |
astro | 1 | 5 |
be | 2 | 8 |
code | sum(course.credits) | avg(course.credits) |
---|---|---|
acc | 42 | 3.5 |
arthis | 70 | 3.5 |
astro | 66 | 3.0 |
be | 55 | 3.23529411765 |
The every
aggregate tests that a predicate is true for every row in
the correlated set. This example returns department
records that
either lack correlated course
records or where every one of those
course
records have exactly 3
credits:
name | avg(course.credits) |
---|---|
Bursar’s Office | |
Career Development | |
Educational Policy | 3.0 |
Arbitrary Linking¶
The unlink operator (@
) permits arbitrary, non-relative links; it
relates the current entity with all other records of another table.
code | name | campus |
---|---|---|
bus | School of Business | south |
eng | School of Engineering | north |
la | School of Arts and Humanities | old |
The query above returns schools that have above average number of
departments. This could also be written with the link function
fork()
which generates the current graph node to itself:
code | name | campus |
---|---|---|
bus | School of Business | south |
eng | School of Engineering | north |
la | School of Arts and Humanities | old |
Calculations & References¶
Calculated Attributes¶
Suppose that you’re returning schools along with the number of associated departments, and we want to list only schools with more than 3 departments.
name | count(department) |
---|---|
School of Engineering | 4 |
School of Arts and Humanities | 6 |
School of Music & Dance | 4 |
In this query we have to repeat the expression count(department)
twice; once to select the value for output, and the other as part of
filter criteria. It is possible to avoid this duplication by defining a
calculated attribute num_dept
.
As syntax sugar, you could combine definition and selection.
All three of these examples return the same result.
Calculated Links¶
In the prior example num_dept
was a scalar value with respect to each
school. It’s possible to define links as well. Suppose we’d like to
calculate a set of statistics by department on 200 level courses typically
taken by sophomores.
name | count(course?no>=200&no<300) | max((course?no>=200&no<300).credits) | min((course?no>=200&no<300).credits) | avg((course?no>=200&no<300).credits) |
---|---|---|---|---|
Accounting | 2 | 3 | 3 | 3.0 |
Art History | 5 | 6 | 3 | 3.8 |
Astronomy | 6 | 5 | 1 | 3.0 |
Here the link expression (course?no>=200&no<300)
is duplicated. We can
define a sophomore
link to these courses as follows.
For readability, it is helpful to put definitions at the end of an
expression where it is used. In the following example the usage of
sophomore
precedes its definition.
In this example we use infix notation to call the given()
function.
Generally, any function call f(x,y)
could be written x :f y
.
Parameterized Calculations¶
Suppose we want to expand the previous example, by calculating the same set of statistics over 4 sets of courses: 100’s, 200’s, 300’s and 400’s.
/department.define(freshman := course?no>=100&no<200,
sophomore := course?no>=200&no<300,
junior := course?no>=300&no<400,
senior := course?no>=400&no<500)
{name, count(freshman),
max(freshman.credits),
min(freshman.credits),
avg(freshman.credits),
count(sophomore),
max(sophomore.credits),
min(sophomore.credits),
avg(sophomore.credits),
count(junior),
max(junior.credits),
min(junior.credits),
avg(junior.credits),
count(senior),
max(senior.credits),
min(senior.credits),
avg(senior.credits)}
name | count(freshman) | max(freshman.credits) | min(freshman.credits) | avg(freshman.credits) | count(sophomore) | max(sophomore.credits) | min(sophomore.credits) | avg(sophomore.credits) | count(junior) | max(junior.credits) | min(junior.credits) | avg(junior.credits) | count(senior) | max(senior.credits) | min(senior.credits) | avg(senior.credits) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Accounting | 1 | 2 | 2 | 2.0 | 2 | 3 | 3 | 3.0 | 2 | 5 | 3 | 4.0 | 3 | 3 | 3 | 3.0 |
Art History | 2 | 4 | 3 | 3.5 | 5 | 6 | 3 | 3.8 | 4 | 3 | 3 | 3.0 | 3 | 5 | 3 | 3.66666666667 |
Astronomy | 5 | 5 | 2 | 3.2 | 6 | 5 | 1 | 3.0 | 6 | 4 | 2 | 3.0 | 5 | 4 | 2 | 2.8 |
In the above examples, we repeat the same group of aggregates four times, but each time with different set of courses. We could write this more concisely defining a calculation with a parameter.
/department.define(freshman := course?no>=100&no<200,
sophomore := course?no>=200&no<300,
junior := course?no>=300&no<400,
senior := course?no>=400&no<500,
stats(set) := {count(set),
max(set.credits),
min(set.credits),
avg(set.credits)})
{name, stats(freshman),
stats(sophomore),
stats(junior),
stats(senior)}
Here the parameter set
is bound to a subset of courses for each grade
level. The calculation returns a set of columns that appear in the output.
Argument References¶
Instead of defining four different subsets of courses, we may want to define a parameterized calculation which takes a the course level and produces courses of this level. Naively, we could write:
<!DOCTYPE html>
<html>
<head>
<title>Bad Request</title>
<link rel="stylesheet" href="/error_pages/normalize.css">
<link rel="stylesheet" href="/error_pages/style.css">
</head>
<body>
<div id="container-left">
<h1>The server did not understand your request.</h1>
<p>Please try going back to the previous page.</p>
<p>If you would like to report this problem to your site administrator, please make note of the steps you took before arriving here.</p>
<p class="error-type">[400 Bad Request]</p>
</div>
<div id="container-right">
<img id="logo" src="/error_pages/RexDB Text.png">
</div>
</body>
</html>
Here we have a problem with the definition of course(level)
. In the
body of the calculation, course
introduces a new naming scope with
attributes from the course table, such as the course no
. Names from the
previous scope, such as level
, are not available. To overcome this
deliberate limitation, we mark level
with a dollar sign to indicate that
it can be referenced from nested scopes.
name | count(course(1)) | count(course(2)) | count(course(3)) | count(course(4)) |
---|---|---|---|---|
Accounting | 1 | 2 | 2 | 3 |
Art History | 2 | 5 | 4 | 3 |
Astronomy | 5 | 6 | 6 | 5 |
Using this technique, we could rewrite the last example from the previous section as:
Defined References¶
References are not limited to parameters of calculations, they could be
defined separately. In the following example $avg_credits
defines the
average number of credits per course. This reference is then used to return
courses with more credits than average.
department_code | no | title | credits | description |
---|---|---|---|---|
acc | 315 | Financial Accounting | 5 | Integration of the conceptual and computational aspects of asset, liability and stockholders equity accounting. |
acc | 511 | Audit | 5 | This course considers legal, procedural and ethical aspects of audit in accounting. |
acc | 620 | Accounting Internship | 6 | Supervised internship in a business or nonprofit organization on an accounting position. |
This same request can be written using given
.
Suppose that we’d like to return courses that have more than average credits for their given department. We could write this as follows.
department_code | no | title | credits | description |
---|---|---|---|---|
acc | 315 | Financial Accounting | 5 | Integration of the conceptual and computational aspects of asset, liability and stockholders equity accounting. |
acc | 511 | Audit | 5 | This course considers legal, procedural and ethical aspects of audit in accounting. |
acc | 620 | Accounting Internship | 6 | Supervised internship in a business or nonprofit organization on an accounting position. |
Projections¶
So far we have shown queries that produce either scalar values or rows
that correspond to records from a table. Occasionally, you may want to
return all unique values of some expression. For example, to return
distinct values of degree
from the program
table, write:
degree |
---|
ba |
bs |
ct |
ma |
ms |
pb |
ph |
In HTSQL, we call this a projection. This construct creates a virtual table of all unique records from a set of expressions.
Distinct Expressions¶
The following example lists values from the degree column for each record of the program table. Observe that you get duplicate rows corresponding to different records from the program table that share the same degree:
degree |
---|
pb |
ba |
ba |
ma |
To get unique rows from the example above, the distinct()
function
can be used:
degree |
---|
ba |
bs |
ct |
Equivalently, this could be written using the ^
operator:
degree |
---|
ba |
bs |
ct |
Note that the projection operator skips rows containing a NULL.
Hence, even though there are rows in the program without a degree,
program^degree
doesn’t contain a NULL.
You could use projections anywhere a table expression is permitted. For instance, to get the number of distinct degrees offered at the university, write:
Or, one could count distinct degrees by school:
name | count(program^degree) |
---|---|
School of Art & Design | 2 |
School of Business | 4 |
College of Education | 4 |
Projections aren’t limited to table attributes. Let’s assume course level as the first digit of the course number. Then, hence following expression returns distinct course levels:
trunc(no/100) |
---|
1 |
2 |
3 |
If you wish to project by more than one expression, use a selector
{}
to group the expressions. In this example we return distinct
combinations of course level and credits.
trunc(no/100) | credits |
---|---|
1 | 2 |
1 | 3 |
1 | 4 |
1 | 5 |
Just as tables are sorted by default using the table’s primary key, projected expressions are also sorted using the distinct columns.
Working with Projections¶
Each projection is a virtual table with its own attributes and links to
other tables. For instance, program^degree
has two attributes, a
column degree
and a plural link program
to records of the
program table having that degree. In the query below, we return
distinct degrees with the number of corresponding programs.
degree | count(program) |
---|---|
ba | 10 |
bs | 10 |
ct | 4 |
ma | 7 |
We may want to filter the base table before projecting. For example, listing only distinct degrees in the School of Engineering.
degree |
---|
bs |
ms |
Or, we could filter the expression after the projection has happened. In the next query we return only degrees having more than 5 corresponding programs.
degree |
---|
ba |
bs |
ma |
ms |
Usually HTSQL automatically assigns names to projected columns, however, in cases where you have an expression, you have to name them. In the following example, we return distinct course level and credits combinations sorted in descending order by level and credits.
level | credits |
---|---|
8 | 3 |
7 | 4 |
7 | 3 |
7 | 0 |
Sometimes HTSQL cannot assign a name linking to the base of the
projection. In these cases, you may use ^
to refer to it.
Additionally *
can be used to return all columns of the projection.
Thus, the first example of this section could be written:
degree | count(^) |
---|---|
ba | 10 |
bs | 10 |
ct | 4 |
ma | 7 |
Logical Expressions¶
A filter refines results by including or excluding data by specific
criteria. This section reviews comparison operators, boolean
expressions, and NULL
handling.
Comparison Operators¶
The equality operator (=
) is overloaded to support various types.
For character strings, this depends upon the underlying database’s
collation rules but typically is case-sensitive. For example, to return
a department
by name
:
code | name | school_code |
---|---|---|
econ | Economics | bus |
If you’re not sure of the exact department name, use the case-insensitive
contains operator (~
). The example below returns all department
records that contain the substring 'engineering'
:
code | name | school_code |
---|---|---|
be | Bioengineering | eng |
ee | Electrical Engineering | eng |
me | Mechanical Engineering | eng |
Use the not-contains operator (!~
) to exclude all courses with
science in the name:
To exclude a specific department, use the not-equals operator:
The equality (=
) and inequality (!=
) operators are
straightforward when used with numbers:
code | name | school_code |
---|---|---|
acc | Accounting | bus |
arthis | Art History | la |
The in operator (={}
) can be thought of as equality over a set.
This example, we return departments that don’t belong to either the
School of Engineering or the School of Natural Sciences:
Use the greater-than (>
) operator to request departments with
more than 20 offered courses:
code | name | school_code |
---|---|---|
astro | Astronomy | ns |
comp | Computer Science | eng |
eng | English | la |
lang | Foreign Languages | la |
Use the greater-than-or-equal-to (>=
) operator to request
departments with 20 courses or more:
Using comparison operators with strings tells HTSQL to compare them
alphabetically (once again, dependent upon database’s collation). For
example, the greater-than (>
) operator can be used to request
departments whose code
follows 'me'
in the alphabet:
code | name | school_code |
---|---|---|
mm | Management & Marketing | bus |
mth | Mathematics | ns |
parent | Parents & Alumni | |
phys | Physics | ns |
Boolean Expressions¶
HTSQL uses function notation for constants such as true()
, false()
and null()
. For the text formatter, a NULL
is shown as a blank,
while the empty string is presented as a double-quoted pair:
true() | false() | null() | ‘’ |
---|---|---|---|
true | false |
The is_null()
function returns true()
if it’s operand is
null()
. In our schema, non-academic department
records with
a NULL
school_code
can be listed:
code | name |
---|---|
bursar | Bursar’s Office |
career | Career Development |
parent | Parents & Alumni |
The negation operator (!
) is true()
when it’s operand is
false()
. To skip non-academic department
records:
code | name |
---|---|
acc | Accounting |
arthis | Art History |
astro | Astronomy |
be | Bioengineering |
The conjunction (&
) operator is true()
only if both of its
operands are true()
. This example asks for courses in the
'Accounting'
department having less than 3 credits:
department_code | no | title | credits | description |
---|---|---|---|---|
acc | 100 | Practical Bookkeeping | 2 |
The alternation (|
) operator is true()
if either of its
operands is true()
. For example, we could list courses having
anomalous number of credits:
department_code | no | title | credits | description |
---|---|---|---|---|
acc | 100 | Practical Bookkeeping | 2 | |
acc | 620 | Accounting Internship | 6 | Supervised internship in a business or nonprofit organization on an accounting position. |
arthis | 209 | Antique Art: Greece and Rome | 6 | Visual art of antique period with emphasis on Greek and Roman art. |
astro | 106 | General Astronomy I Lab | 2 | Laboratory studies that complement the lecture course. |
The precedence rules for boolean operators follow typical programming convention; negation binds more tightly than conjunction, which binds more tightly than alternation. Parenthesis can be used to override this default grouping rule or to better clarify intent. The next example returns courses that are in “Art History” or “Studio Art” departments that have more than three credits:
department_code | no | title | credits | description |
---|---|---|---|---|
arthis | 102 | History of Art Criticism I | 4 | An introductory survey course of world art from Prehistoric through late-Medieval art history. |
arthis | 209 | Antique Art: Greece and Rome | 6 | Visual art of antique period with emphasis on Greek and Roman art. |
arthis | 210 | Antique Art: The Middle East | 4 | Visual art of ancient world civilizations: Egypt, Mesopotamia, Persia. |
arthis | 476 | Islamic Art | 5 | History of Islamic visual arts and their religious value. |
Without the parenthesis, the expression above would show all courses
from 'arthis'
regardless of credits:
department_code | no | title | credits | description |
---|---|---|---|---|
arthis | 102 | History of Art Criticism I | 4 | An introductory survey course of world art from Prehistoric through late-Medieval art history. |
arthis | 103 | History of Art Criticism II | 3 | An introductory survey course of world art from 15th century through the present. |
arthis | 204 | North American Native Art | 3 | Visual art of North American natives from prehistoric times to the present day. |
When a non-boolean is used in a logical expression, it is implicitly
cast as a boolean. As part of this cast, tri-value logic is
flattened, null()
is converted into false()
. For strings, the
empty string (''
) is also treated as false()
. This conversion
rule shortens URLs and makes them more readable.
For example, this query returns only course
records having a
description
:
The predicate ?description
is treated as a short-hand for
?(!is_null(description)&description!='')
. The negated variant of
this shortcut is more illustrative:
department_code | no | description |
---|---|---|
acc | 100 | |
tched | 630 |
Types and Functions¶
HTSQL supports boolean, date, numeric, and string data types, as well as variants. The pluggable type system can be used to augment the core types provided.
Working with NULLs¶
HTSQL provides a rich function set for handling NULL
expressions;
however, careful attention must be paid. For starters, the standard
equality operator (=
) is null-regular, that is, if either operand is
null()
the result is null()
. The following request always
returns 0 rows:
code | name | school_code |
---|
While you wouldn’t directly write that query, it could be the final
result after parameter substitution for a templatized query such as
/department?school=$var
. For cases like this, use total equality
operator (==
) which treats NULL
values as equivalent:
code | name | school_code |
---|---|---|
bursar | Bursar’s Office | |
career | Career Development | |
parent | Parents & Alumni |
The !==
operator lists distinct values, including records with
a NULL
for the field tested:
code | name | school_code |
---|---|---|
acc | Accounting | bus |
arthis | Art History | la |
astro | Astronomy | ns |
be | Bioengineering | eng |
bursar | Bursar’s Office | |