IQL is an SQL-inspired query language for processing Internet resources. The IQL uses common data formats as input tables and allows users to run SQL-like queries over the tables. The currently supported data formats are comma-separated values (CSV), JavaScript Object Notation (JSON), and HTML. The data sources can be retrieved from HTTP and HTTPS URLs, local files, and data URIs.
The iql command accepts the following command line arguments:
-eiqlcode: execute iqlcode. The remaining command line arguments are provided to the script via theARGSglobal variable.-ofile: save output to file file-tstyle: set the table formatting style to style-cpuprofilefile: write Go CPU profile to file-htmlstring: filter argument files with HTML selector string-jsonstring: filter argument files with JSON selector string
$ iql -e 'select time,mag,place from ARGS limit 10' https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/2.5_day.csv
┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ time ┃ mag ┃ place ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ 2021-01-26T18:40:20.930Z │ 2.92 │ 5 km SW of Guánica, Puerto Rico │
│ 2021-01-26T17:29:56.580Z │ 4.3 │ 14 km W of Foxton, New Zealand │
│ 2021-01-26T16:59:35.240Z │ 2.47 │ 4 km SSE of Guánica, Puerto Rico │
│ 2021-01-26T16:46:39.097Z │ 4 │ 96 km NNW of Villa General Roca, Argentina │
│ 2021-01-26T16:45:12.923Z │ 4 │ 48 km NE of Iquique, Chile │
│ 2021-01-26T16:13:50.750Z │ 2.78 │ 10 km SSE of Indios, Puerto Rico │
│ 2021-01-26T15:42:21.236Z │ 4.9 │ 71 km SSE of Panguna, Papua New Guinea │
│ 2021-01-26T15:28:41.243Z │ 4.5 │ Pagan region, Northern Mariana Islands │
│ 2021-01-26T14:56:59.874Z │ 4.6 │ Kuril Islands │
│ 2021-01-26T14:32:42.636Z │ 4.5 │ 19 km WSW of Mamurras, Albania │
└──────────────────────────┴──────┴────────────────────────────────────────────┘
The examples directory contains sample data files and queries. The data files are also hosted at my web site and we use that location for these examples. Please, check also Appendix B for additional data visualization examples.
The store.html file contains 2 data sources, encoded has HTML tables. The "customers" table contain information about store customers:
SELECT customers.'.id' AS ID,
customers.'.name' AS Name,
customers.'.address' AS Address
FROM 'https://markkurossi.com/iql/examples/store.html'
FILTER 'table:nth-of-type(1) tr' AS customers
WHERE '.id' <> null;┏━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ID ┃ Name ┃ Address ┃
┡━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ 1 │ Alyssa P. Hacker │ 77 Massachusetts Ave Cambridge, MA 02139 │
│ 2 │ Ben Bitdiddle │ 2200 Mission College Blvd. Santa Clara, CA 95052 │
│ 3 │ Cy D. Fect │ 208 S. Akard St. Dallas, TX 75202 │
│ 4 │ Eva Lu Ator │ 353 Jane Stanford Way Stanford, CA 94305 │
│ 5 │ Lem E. Tweakit │ 1 Hacker Way Menlo Park, CA 94025 │
│ 6 │ Louis Reasoner │ Princeton NJ 08544, United States │
└────┴──────────────────┴───────────────────────────────────────────────────┘
The "products" table defines the store products:
SELECT products.'.id' AS ID,
products.'.name' AS Name,
products.'.price' AS Price
FROM 'https://markkurossi.com/iql/examples/store.html'
FILTER 'table:nth-of-type(2) tr' AS products
WHERE '.id' <> null;┏━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┓
┃ ID ┃ Name ┃ Price ┃
┡━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━┩
│ 1 │ Structure and Interpretation of Computer Programs │ 14.95 │
│ 2 │ GNU Emacs Manual, For Version 21, 15th Edition │ 9.95 │
│ 3 │ ISO/IEC 9075-1:2016(en) SQL — Part 1 Framework │ │
└────┴───────────────────────────────────────────────────┴───────┘
The orders.csv file
contains order information, encoded as comma-separted values
(CSV). The data file does not have CSV headers at its first line so we
use the noheaders filter flag:
SELECT orders.'0' AS ID,
orders.'1' AS Customer,
orders.'2' AS Product,
orders.'3' AS Count
FROM 'https://markkurossi.com/iql/examples/orders.csv'
FILTER 'noheaders' AS orders;┏━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┓
┃ ID ┃ Customer ┃ Product ┃ Count ┃
┡━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━┩
│ 1 │ 1 │ 2 │ 1 │
│ 2 │ 4 │ 1 │ 2 │
│ 3 │ 5 │ 3 │ 5 │
└────┴──────────┴─────────┴───────┘
In addition of listing individual tables, you can join tables and compute values over the columns:
DECLARE storeurl VARCHAR;
SET storeurl = 'https://markkurossi.com/iql/examples/store.html';
DECLARE ordersurl VARCHAR;
SET ordersurl = 'https://markkurossi.com/iql/examples/orders.csv';
SELECT customers.Name AS Name,
customers.Address AS Address,
products.Name AS Product,
orders.Count AS Count,
products.Price * orders.Count AS Price
FROM (
SELECT c.'.id' AS ID,
c.'.name' AS Name,
c.'.address' AS Address
FROM storeurl FILTER 'table:nth-of-type(1) tr' AS c
WHERE '.id' <> null
) AS customers,
(
SELECT p.'.id' AS ID,
p.'.name' AS Name,
p.'.price' AS Price
FROM storeurl FILTER 'table:nth-of-type(2) tr' AS p
WHERE '.id' <> null
) AS products,
(
SELECT o.'0' AS ID,
o.'1' AS Customer,
o.'2' AS Product,
o.'3' AS Count
FROM ordersurl FILTER 'noheaders' AS o
) AS orders
WHERE orders.Product = products.ID AND orders.Customer = customers.ID;┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ Name ┃ Address ┃ Product ┃ Count ┃ Price ┃
┡━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━┩
│ Alyssa P. Hacker │ 77 Massachusetts Ave Cambridge, MA 02139 │ GNU Emacs Manual, For Version 21, 15th Edition │ 1 │ 9.95 │
│ Eva Lu Ator │ 353 Jane Stanford Way Stanford, CA 94305 │ Structure and Interpretation of Computer Programs │ 2 │ 29.9 │
│ Lem E. Tweakit │ 1 Hacker Way Menlo Park, CA 94025 │ ISO/IEC 9075-1:2016(en) SQL — Part 1 Framework │ 5 │ │
└──────────────────┴──────────────────────────────────────────┴───────────────────────────────────────────────────┴───────┴───────┘
The "here strings" allow embedding data in the IQL scripts:
SET REALFMT = '%.2f';
SELECT * FROM ```datauri:text/csv
Ints,Floats,Strings
1,42,foo
2,3.14,bar
```;┏━━━━━━┳━━━━━━━━┳━━━━━━━━━┓
┃ Ints ┃ Floats ┃ Strings ┃
┡━━━━━━╇━━━━━━━━╇━━━━━━━━━┩
│ 1 │ 42.00 │ foo │
│ 2 │ 3.14 │ bar │
└──────┴────────┴─────────┘
The IQL follows SQL in all constructs where possible. The full syntax is defined in the iql.iso-ebnf file and it is also available as SVG and HTML versions.
The HTML data source extracts input from HTML documents. The data source uses the goquery package for the HTML processing. This means that the filter and column selectors are CSS selectors, implemented by the cascadia library. The input document processing is done as follows:
- the
FILTERselector selects input rows - the
SELECTselectors select columns from input rows
The CSV data source extracts input from comma-separated values (CSV)
data. The data source uses Go's CSV encoding package for decoding the
data. The FILTER parameter can be used to specify CSV processing
options:
skip=count: skip the first count input linescomma=rune: use rune to separate columnscomment=rune: skip lines starting with runekeep-leading-space: keep leading space from columnsnoheaders: the first line of the CSV data is not a header line. You must use column indices to select columns from the data.prepend-headers=header[,...]: prepend the headers to the CSV file's header line. This option can be used to fix malformed CSV files which contain an invalid header line.
For example, if your input file is as follows:
Year; Value; Delta
# lines beginning with # character are ignored
1970; 100; 0
1971; 101; 1
1972; 200; 99
The fields can be processed with the following IQL code:
SELECT data.'0' AS Year,
data.'1' AS Value,
data.'2' AS Delta
FROM 'test_options.csv'
FILTER 'noheaders skip=1 comma=; comment=#'
AS data;┏━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ Year ┃ Value ┃ Delta ┃
┡━━━━━━╇━━━━━━━╇━━━━━━━┩
│ 1970 │ 100 │ 0 │
│ 1971 │ 101 │ 1 │
│ 1972 │ 200 │ 99 │
└──────┴───────┴───────┘
Since our sample CSV file did have a header row, we can also use it to name the data columns:
SELECT Year, Value, Delta
FROM 'test_options.csv'
FILTER 'comma=; comment=#';This query gives the same result as the previous example:
┏━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ Year ┃ Value ┃ Delta ┃
┡━━━━━━╇━━━━━━━╇━━━━━━━┩
│ 1970 │ 100 │ 0 │
│ 1971 │ 101 │ 1 │
│ 1972 │ 200 │ 99 │
└──────┴───────┴───────┘
The JSON data source extracts input from JSON documents. The data source uses the jsonq package for the JSON processing. This means that the filter and column selectors are JSONQ selectors which emulate XPath expressions. The input document processing is done as follows:
- the
FILTERselector selects input rows - the
SELECTselectors select columns from input rows
For example, if your input file is as follows:
{
"colors": [
{
"name": "Black",
"red": 0,
"green": 0,
"blue": 0
},
{
"name": "Red",
"red": 205,
"green": 0,
"blue": 0
},
"... objects omitted ...",
{
"name": "Bright White",
"red": 255,
"green": 255,
"blue": 255
}
]
}The color values can be processed with the following IQL code:
SELECT src.name AS Name,
src.red AS Red,
src.green AS Green,
src.blue AS Blue
from 'ansi.json' FILTER 'colors' AS src;┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━┳━━━━━━━┳━━━━━━┓
┃ Name ┃ Red ┃ Green ┃ Blue ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━╇━━━━━━━╇━━━━━━┩
│ Black │ 0 │ 0 │ 0 │
│ Red │ 205 │ 0 │ 0 │
│ Green │ 0 │ 205 │ 0 │
│ Yellow │ 205 │ 205 │ 0 │
│ Blue │ 0 │ 0 │ 238 │
│ Magenta │ 205 │ 0 │ 205 │
│ Cyan │ 0 │ 205 │ 205 │
│ White │ 229 │ 229 │ 229 │
│ Bright Black (Gray) │ 127 │ 127 │ 127 │
│ Bright Red │ 255 │ 0 │ 0 │
│ Bright Green │ 0 │ 255 │ 0 │
│ Bright Yellow │ 255 │ 255 │ 0 │
│ Bright Blue │ 92 │ 92 │ 255 │
│ Bright Magenta │ 255 │ 0 │ 255 │
│ Bright Cyan │ 0 │ 255 │ 255 │
│ Bright White │ 255 │ 255 │ 255 │
└─────────────────────┴─────┴───────┴──────┘
| Variable | Type | Default | Description |
|---|---|---|---|
| ARGS | []VARCHAR | [] |
Command line arguments form -e invocation. |
| REALFMT | VARCHAR | %g |
The formatting option for real numbers. |
| TABLEFMT | VARCHAR | uc |
The table formatting style. |
| TERMOUT | BOOLEAN | ON |
Controls the terminal output from the queries. |
- AVG(expression): returns the average value of all the values. The NULL values are ignored.
- COUNT(expression): returns the count of all the values. The NULL values are ignored
- MAX(expression): returns the maximum value of all the values. The NULL values are ignored.
- MIN(expression): returns the minimum value of all the values. The NULL values are ignored.
- NULLIF(expr, value): returns NULL if the expr and value are equal and the value of expr otherwise.
- SUM(Expression): returns the sum of all the values. The NULL values are ignored.
- FLOOR(numeric): rounds the numeric value down to the largest integer less than or equal to the argument value.
- LOG(numeric): returns the natural logarithm of numeric.
- LOG10(numeric): returns the decimal logarithm of numeric.
- BASE64DEC(expression): decodes the Base64 encoded string and returns the resulting data, converted to string
- BASE64ENC(expression): return the Base64 encoding of the string expression
- CHAR(code): returns the Unicode character for integer value code.
- CHARINDEX(expression, search [, start]): return the first index of the substring search in expression. The optional argument start specifies the search start location. If the start argument is omitted or smaller than zero, the search start from the beginning of expression. Note that the returned index value is 1-based. The function returns the value 0 if the search substring could not be found from expression.
- CONCAT(val1, val2 [, ..., valn]): concatenates the argument string expressions into a string. All NULL expressions are handles as empty strings.
- CONCAT_WS(separator, val1, val2 [, ..., valn]): concatenates the argument string expressions into a string where arguments are separated by the separator string. All NULL expressions are ingored and they are not separated by the separator string. If the separator is NULL, this works like the CONCAT() function.
- LASTCHARINDEX(expression, search): return the last index of the substring search in expression. Note that the returned index value is 1-based. The function returns the value 0 if the search substring could not be found from expression.
- LEFT(expression, count): returns the count leftmost characters from the string expression.
- LEN(expression): returns the number of Unicode code points in the string representation of expression.
- LOWER(expression): returns the lowercase representation of the expression.
- LPAD(expression, length [, pad]): pads the expression from the start with pad characters so that the resulting string has lenght characters. If the expression is longer than length, the function returns length leftmost characters from the string expression. If the argument pad is omitted, the space character (' ') is used as padding.
- LTRIM(expression): remove the leading whitespace from the string representation of expression.
- NCHAR(expression): returns the Unicode character with the integer code expression
- REPLICATE(expression, count): repeats the string value expression count times. If the count is negative, the function returns NULL.
- REVERSE(expression): return the reverse order of the argument string expression.
- RIGHT(expression, count): returns the count rightmost characters from the string expression.
- RTRIM(expression): remove the trailing whitespace from the string representation of expression.
- SPACE(count): return a string containing count space characters.
- STUFF(string, start, length, replace): remove length characters from the index start from the string expression string and replace the removed characters with replace. If start is smaller than or equal to 0, the function returns NULL. If the start is larger than the length of string, the function returns NULL. If length is negative, the function returns NULL. If length is larger than the length of string, the function removes all characters starting from the index start. If the replace values is NULL, no replacement characters are inserted.
- SUBSTRING(expression, start, length): returns a substring of the expression. The start specifies the start index of the substring to return. Note that the start index is 1-based. If the start index is 0 or negative, the substring will start from the beginning of the expression. The length specifies non-negative length of the returned substring. If the length argument is negative, an error will be generated. If start + length is larger than the length of expression, the substring contains character to the end of expression.
- TRIM(expression): remove the leading and trailing whitespace from the string representation of expression.
- UNICODE(expression): returns the integer value of the first Unicode character of the string expression
- UPPER(expression): returns the uppercase representation of the expression.
- DATEDIFF(diff, from, to): returns the time difference between
from and to. The diff specifies the units in which the
difference is computed:
year,yy,yyyy: difference between date year partsday,dd,d: difference in calendar dayshour,hh: difference in hoursminute,mi,n: difference in minutessecond,ss,s: difference secondsmillisecond,ms: difference in millisecondsmicrosecond,mcs: difference in microsecondsnanosecond,ns: difference in nanoseconds
- DAY(date): returns an integer representing the day of the month of the argument date
- GETDATE(): returns the current system timestamp
- MONTH(date): returns an integer representing the month of the year of the argument date
- YEAR(date): returns an integer representing the year of the argument date.
- HBAR(value, min, max, width [,pad]): creates a horizontal histogram bar that is width characters long. The leftmost (value-min)/(max-min) characters are rendered with the Unicode Box Elements (U+2580-U+259F) and the remaining characters are filled with the pad character. The default padding character is space (' ', 0x20). If the pad is a string, it must be one rune long. It is an error if the value range from min to max is zero.
These data visualization examples use a dataset from Freie Universität Berlin. The copyright of the dataset is as follows:
You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License. Please cite as follow: Hartmann, K., Krois, J., Waske, B. (2018): E-Learning Project SOGA: Statistics and Geospatial Data Analysis. Department of Earth Sciences, Freie Universitaet Berlin.
The data set
students.csv
contains 8239 records, each having 16 attributes of a particular
student. The CSV file has header row but it does not have the column
name for the first data column which is the record sequence
number. Therefore, the examples below use the prepend-headers=seq
CSV filtering option. All example snippets below are taken from the
students.iql IQL file.
All examples below assume that the following settings have been made in the sample preamble:
-- Print real numbers two decimal digits.
SET REALFMT = '%.2f';
-- Define the students.csv data URL.
DECLARE dataurl VARCHAR;
SET dataurl = 'https://userpage.fu-berlin.de/soga/200/2010_data_sets/students.csv';The height histogram shows student heights in 5cm data ranges. We use the HBAR() function to create horizontal histogram bars.
SELECT height,
count,
HBAR(count, 0, max(count), 20) AS histogram
FROM (
SELECT height / 5 * 5 AS height,
COUNT(height) AS count
FROM dataurl FILTER 'prepend-headers=seq'
GROUP BY height / 5
ORDER BY height
);┏━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ height ┃ count ┃ histogram ┃
┡━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ 135 │ 3 │ │
│ 140 │ 32 │ ▍ │
│ 145 │ 126 │ █▊ │
│ 150 │ 352 │ █████▏ │
│ 155 │ 732 │ ██████████▊ │
│ 160 │ 1138 │ ████████████████▊ │
│ 165 │ 1230 │ ██████████████████▏ │
│ 170 │ 1350 │ ████████████████████ │
│ 175 │ 1196 │ █████████████████▋ │
│ 180 │ 1037 │ ███████████████▎ │
│ 185 │ 644 │ █████████▌ │
│ 190 │ 291 │ ████▎ │
│ 195 │ 87 │ █▎ │
│ 200 │ 19 │ ▎ │
│ 205 │ 2 │ │
└────────┴───────┴──────────────────────┘
This example draws height histogram for male students.
SELECT height,
count,
HBAR(count, 0, max(count), 20) AS histogram
FROM (
SELECT height / 5 * 5 AS height,
COUNT(height) AS count,
gender AS ",gender"
FROM dataurl FILTER 'prepend-headers=seq'
WHERE gender='Male'
GROUP BY height / 5
ORDER BY height
);┏━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ height ┃ count ┃ histogram ┃
┡━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ 140 │ 1 │ │
│ 145 │ 2 │ │
│ 150 │ 3 │ │
│ 155 │ 25 │ ▌ │
│ 160 │ 117 │ ██▍ │
│ 165 │ 322 │ ██████▋ │
│ 170 │ 711 │ ██████████████▋ │
│ 175 │ 954 │ ███████████████████▋ │
│ 180 │ 969 │ ████████████████████ │
│ 185 │ 630 │ █████████████ │
│ 190 │ 287 │ █████▉ │
│ 195 │ 87 │ █▊ │
│ 200 │ 19 │ ▍ │
│ 205 │ 2 │ │
└────────┴───────┴──────────────────────┘
We can use VT100 codes to change the histogram background color. This helps us to visualize some histogram bars which have very small number of samples.
First, we create a function for rendering the histogram bars:
CREATE FUNCTION histogram(val INTEGER, max INTEGER, width INTEGER)
RETURNS VARCHAR
AS
BEGIN
RETURN CONCAT(CHAR(0x1b), '[107m',
HBAR(val, 0, max, width),
CHAR(0x1b), '[0m');
ENDThen we update the height histogram program to use the new
histogram() function to render the data bars:
SELECT height,
count,
histogram(count, max(count), 20) AS histogram
FROM (
SELECT height / 5 * 5 AS height,
COUNT(height) AS count
FROM dataurl FILTER 'prepend-headers=seq'
GROUP BY height / 5
ORDER BY height
);- Queries:
- Push table specific AND-relation SELECT expressions down to data source.
- Aggregate:
- Value cache
- HTTP resource cache
- YAML data format
- SQL Server base year for YEAR(0) is 1900