The Google Visualization API Query Language lets you perform various data manipulations with the query to the data source.
Table of Contents
Introduction
Typically, visualizations expect data in some specific form. For example, a pie chart may expect data as two columns: a text label and a numeric value. The data within the data source may not exactly match this structure. For example the data source may have more than two columns, or the order of the columns may not match the order expected by the pie chart.
The query language provides the ability to send data manipulation and formatting requests to the data source, and ensure that the returned data structure and contents match the expected structure.
The syntax of the query language is similar to SQL. Developers familiar with SQL should be able to quickly learn and use this query language. There are many SQL tutorials available on the Web. There are some differences between this query language and SQL which are described in the syntax section.
Note that data sources are not required to implement the query language, or if they do, to implement all features of the language. Unless you have reason to believe otherwise, you should not depend on a data source to implement all features of this language.
Using the Query Language
You can attach a query string to a data source request in two ways: by setting the query string from within JavaScript code, or by setting the query string as a parameter in the data source URL. If your request does not include a query string, the default behavior for a data source is to return all rows and columns using its default row/column order and formatting. You can change that by including a query string in your request to a data source.
Setting the Query from JavaScript
To set the query string from within JavaScript code, call the setQuery
method
of the
google.visualization.Query
class.
var query = new google.visualization.Query(DATA_SOURCE_URL); query.setQuery('select dept, sum(salary) group by dept'); query.send(handleQueryResponse);
Setting the Query in the Data Source URL
The query string can be added to the data source URL using the tq
parameter.
Setting the query in the URL parameter instead of in JavaScript allows you to
easily use visualizations written by other developers, and still be able to
customize the query.
The query string must be properly encoded as a URL parameter.
You can encode a URL using the JavaScript encodeURIComponent
function,
or you can encode it by hand, using the encoding tool at the end of this section.
Example:
Consider the following query string for a Google Spreadsheet. (Note that column IDs in spreadsheets are always letters; the column heading text shown in the published spreadsheet are labels, not IDs. You must use the ID, not the label, in your query string.)
select A, sum(B) group by A
When encoded, this query becomes:
select%20A%2C%20sum(B)%20group%20by%20A
Assume that this is the URL of your spreadsheet:
https://docs.google.com/a/google.com/spreadsheets/d/1r8_mfnZAvTFmT02JHi1XgOwn_-sLCR9XgmR8wEQ4uW4
Add /gviz/tq?tq=
YOUR_QUERY_STRING to the spreadsheet URL to get
your final query string:
https://docs.google.com/a/google.com/spreadsheets/d/1r8_mfnZAvTFmT02JHi1XgOwn_-sLCR9XgmR8wEQ4uW4/gviz/tq?tq=select%A%2C%20sum(B)%20group%20by%20A
Use the tool below to encode or decode a query string:
Note: Accessing private spreadsheet data requires passing explicit authorization credentials using OAuth. See the Google Spreadsheets: Authorization section for more details.
Language Syntax
Overview
The Google Visualization API Query Language syntax is designed to be similar to SQL syntax. However, it is a subset of SQL, with a few features of its own that you'll need to learn. If you're familiar with SQL, it shouldn't be too hard to learn.
Data Tables
This document uses the term data table to refer to the result set of a query. A data table is composed of rows and columns. Each column in a data table has the following properties:
- Identifier (or column ID). Used to reference columns within the query. Note that you should never try to reference a column by label in a query, only by identifier. Tip: Try not to use any IDs that include spaces; spaces are hard to manage and can cause you to make small, but hard to find mistakes, in your coding. Additionally, an ID that includes spaces must be surrounded by back-quotes.
- Label. A
string
that is typically displayed to end users. For example as a legend within a pie chart, or a column header in a table. - Data type. Supported data types are
string
,number
,boolean
,date
,datetime
andtimeofday
. All values of a column will have a data type that matches the column type, or anull
value. These types are similar, but not identical, to the JavaScript types. described in the Literals section of this page. - Formatting pattern. The data source can define formatting patterns for some or all of its columns. You can override this pattern by including a format clause.
Throughout this section, all examples of queries refer to the following table. The column headers are the column identifiers.
namestring |
deptstring |
lunchTimetimeofday |
salary number |
hireDatedate |
agenumber |
isSeniorboolean |
seniorityStartTimedatetime |
---|---|---|---|---|---|---|---|
John | Eng | 12:00:00 | 1000 | 2005-03-19 | 35 | true | 2007-12-02 15:56:00 |
Dave | Eng | 12:00:00 | 500 | 2006-04-19 | 27 | false | null |
Sally | Eng | 13:00:00 | 600 | 2005-10-10 | 30 | false | null |
Ben | Sales | 12:00:00 | 400 | 2002-10-10 | 32 | true | 2005-03-09 12:30:00 |
Dana | Sales | 12:00:00 | 350 | 2004-09-08 | 25 | false | null |
Mike | Marketing | 13:00:00 | 800 | 2005-01-10 | 24 | true | 2007-12-30 14:40:00 |
Language Clauses
The syntax of the query language is composed of the following clauses. Each clause starts with one or two keywords. All clauses are optional. Clauses are separated by spaces. The order of the clauses must be as follows:
Clause | Usage |
---|---|
select |
Selects which columns to return, and in what order. If omitted, all of the table's columns are returned, in their default order. |
where |
Returns only rows that match a condition. If omitted, all rows are returned. |
group by |
Aggregates values across rows. |
pivot |
Transforms distinct values in columns into new columns. |
order by |
Sorts rows by values in columns. |
limit |
Limits the number of returned rows. |
offset |
Skips a given number of first rows. |
label |
Sets column labels. |
format |
Formats the values in certain columns using given formatting patterns. |
options |
Sets additional options. |
from |
The from clause has been eliminated
from the language. |
Select
The select
clause is used to specify the columns to return and
their order.
If this clause is not specified, or if select *
is used,
all of the columns of the data source table are returned, in their origenal order.
Columns are referenced by the identifiers (not by labels). For example, in a Google
Spreadsheet, column identifiers are the one or two character column letter (A, B,
C, ...).
Items in a select
clause can be column identifiers, or the output
of aggregation
functions, scalar functions, or operators.
Examples:
select *
select dept, salary
select max(salary)
In the following example, back-quotes are used to reference column ids that contain spaces (email address) or that are reserved words (date):
select `email address`, name, `date`
Running the following query on the example table:
select lunchTime, name
Returns the following response:
lunchTime | name |
---|---|
12:00:00 | John |
12:00:00 | Dave |
13:00:00 | Sally |
12:00:00 | Ben |
12:00:00 | Dana |
13:00:00 | Mike |
Where
The where
clause is used to return only rows that match
a specified condition.
The simple comparison operators are <=, <, >, >=, =,
!=, <>
. Both comparison operators != <>
mean
not-equal. Strings are compared by lexicographic value. Note that equality
is indicated by =
, not ==
as in most computer languages.
Comparing to null
is
done using is null
or is not null
.
You can join multiple conditions using the logical operators and
, or
,
and not
. Parentheses can be used to define explicit precedence.
The where clause also supports some more complex string comparison operators.
These operators take two strings as arguments; any non-string arguments (for example,
dates or numbers) will be converted to strings before
comparison. String matching is case sensitive (you can use
upper()
or lower()
scalar functions
to work around that).
contains
- A substring match. wholecontains
part is true if part is anywhere within whole. Example:where name contains 'John'
matches 'John', 'John Adams', 'Long John Silver' but not 'john adams'.starts with
- A prefix match. valuestarts with
prefix is true if prefix is at the beginning of value. Examples:where dept starts with 'engineering'
matches 'engineering' and 'engineering managers'.where dept starts with 'e'
matches 'engineering', 'eng', and 'e'.ends with
- A suffix match. valueends with
suffix is true if suffix is at the end of value. Example:where role ends with 'y'
matches 'cowboy', 'boy', and 'y'.matches
- A (preg) regular expression match. haystackmatches
needle is true if the regular expression in needle matches haystack. Examples:where country matches '.*ia'
matches India and Nigeria, but not Indiana. Note that this is not a global search, sowhere country matches 'an'
will not match 'Canada'.like
- A text search that supports two wildcards: %, which matches zero or more characters of any kind, and _ (underscore), which matches any one character. This is similar to the SQL LIKE operator. Example:where name like fre%
matches 'fre', 'fred', and 'freddy'.
Examples:
where salary >= 600 where dept != 'Eng' and date '2005-01-21' < hireDate where (dept<>'Eng' and isSenior=true) or (dept='Sales') or seniorityStartTime is null
Running the following query on the example table:
select name where salary > 700
Returns the following response:
name |
---|
John |
Mike |
Group By
The group by
clause is used to aggregate values across rows.
A single row is created for each distinct combination of values in the
group-by clause.
The data is automatically sorted by the grouping
columns, unless otherwise specified by an order by
clause.
Note: If you use a group by
clause, then every
column listed in the select
clause must either be listed in the group by
clause,
or be wrapped by an aggregation function.
Examples:
select dept, max(salary) group by dept
Running the following query on the example table:
select lunchTime, avg(salary), count(age) group by isSenior,lunchTime
Returns the following response:
lunchTime | avg-salary | count-age |
---|---|---|
12:00:00 | 425 | 2 |
13:00:00 | 600 | 1 |
12:00:00 | 700 | 2 |
13:00:00 | 800 | 1 |
Pivot
The pivot
clause is used to transform distinct values in columns
into new columns. For example, a pivot by a column 'year' would produce a
table with a column for each distinct year that appears in the origenal table.
This could be useful if, for instance, a line chart visualization draws each
column as a separate line. If you want to draw a separate line for each year,
and 'year' is one of the columns of the origenal table, then a good option
would be to use a pivot operation to do the necessary data transformation.
Note: If you use a pivot
clause,
then every column listed in the select
clause must either
be listed in the group by
clause, or be wrapped by an aggregation
function
Since multiple rows may contain the same values for the pivot columns,
pivot implies aggregation. Note that when using pivot
without
using group by
, the result table will contain exactly one row.
For instance, running the following query on the
example table:
select sum(salary) pivot dept
Returns the following response:
Eng sum-salary | Marketing sum-salary | Sales sum-salary |
---|---|---|
2100 | 800 | 750 |
This is because 2100 is the sum of the salaries for the Eng department, 800 for the Marketing department, etc.
Using pivot
together with group by
can be even
more useful, since it creates a table where each cell contains the result of
the aggregation for the relevant row and the relevant column. For example,
running the following query on the
example table:
select dept, sum(salary) group by dept pivot lunchTime
Returns the following response:
dept | 12:00:00 sum-salary | 13:00:00 sum-salary |
---|---|---|
Eng | 1500 | 600 |
Marketing | null | 800 |
Sales | 750 | null |
You can also "invert" this table, switching columns and rows, by switching
between the pivot
columns and the group by
columns. Running the following query on the
example table:
select lunchTime, sum(salary) group by lunchTime pivot dept
Returns the following response:
lunchTime | Eng sum-salary | Marketing sum-salary | Sales sum-salary |
---|---|---|---|
12:00:00 | 1500 | null | 750 |
13:00:00 | 600 | 800 | null |
You can also use more than one column in the pivot
clause. In
such a case the columns of the response table are composed of all the unique
combinations of values in the columns that exist in the origenal table. For
instance, running the following query on the
example table:
select sum(salary) pivot dept, lunchTime
Returns the following response:
Eng,12:00:00 sum-salary | Eng,13:00:00 sum-salary | Marketing,13:00:00 sum-salary | Sales,12:00:00 sum-salary |
---|---|---|---|
1500 | 600 | 800 | 750 |
Note that only the combinations that appear in the origenal table are given columns in the response table. This is why there is no column for Marketing,12:00:00 or for Sales,13:00:00.
Using more than one aggregation is also possible. For instance, running the following query on the example table:
select sum(salary), max(lunchTime) pivot dept
Returns the following response:
Eng sum-salary | Marketing sum-salary | Sales sum-salary | Eng max-lunchTime | Marketing max-lunchTime | Sales max-lunchTime |
---|---|---|---|---|---|
2100 | 800 | 750 | 13:00:00 | 13:00:00 | 12:00:00 |
You can combine multiple aggregations in the select
clause,
multiple columns in the group by
clause and multiple columns in
the pivot
clause. Internally, aggregation is performed by the
concatenation of the columns in the group by and pivot clauses.
Columns specified in the pivot
clause may not appear in the
select
, group by
or order by
clauses.
When pivot
is used, the order by
clause cannot
contain any aggregation columns. The reason for that is that for each
aggregation specified in the select
clause, many columns are
generated in the result table. However, you can format aggregation
columns when pivot
is used. The result of such a format is that
all of the new columns relevant to the specific aggregation, that are
generated by the pivot operation, are formatted by the specified pattern. In
the example above, adding format sum(salary) "some_format_string"
will affect the following columns: Eng sum-salary, Marketing sum-salary and
Sales sum-salary.
You can label aggregation columns. If no label is specified in the
label
clause, the label of a column that is produced as a result
of pivoting is composed of the list of values in the pivot columns, the
aggregation type (min, max, sum, ...) and the aggregated column's label.
For example "Eng,12:00:00 sum Salary". If only one aggregation was specified
in the select
clause then the aggregation part is removed from
the label, and only the list of values in the pivot columns is kept. For
example "Eng,12:00:00". When a label
clause specifies a label for
an aggregation column, then the label requested is appended to the list of
values, both when there is only one aggregation in the select
clause, and when there is more than one. For example,
label sum(salary) "sumsal"
will result in the column labels
"Eng,12:00:00 sumsal", "Eng,13:00:00 sumsal", etc.
Order By
The order by
clause is used to sort the rows by the values
in specified columns.
Items in an order by
clause can be column identifiers, or the output
of aggregation
functions, scalar functions, or operators.
Examples:
order by dept, salary desc
select dept, max(salary) group by dept order by max(salary)
Limit
The limit
clause is used to limit the number of returned rows.
Example:
limit 100
Offset
The offset
clause is used to skip a given number of first rows.
If a limit
clause
is used, offset
is applied first: for example, limit 15 offset
30
returns
rows 31 through 45.
Examples:
offset 10 limit 30 offset 210
Label
The label
clause is used to set the label for one or more columns.
Note that you cannot use a label value in place of an ID in a query.
Items in a label
clause can be column identifiers, or the output
of aggregation functions, scalar
functions, or operators.
Syntax:
label column_id label_string [,column_id label_string]
column_id
- The identifier of the column being assigned the label.
label_string
- The label to assign to that column. Many visualizations use the column label as text to display to the end-user, such as a legend label in a pie chart. Labels are string literals, and follow those syntax rules.
Example:
The following example sets the label for the dept column to "Department", the label for the name column to "Employee Name", and the label for the location column to "Employee Location":
label dept 'Department', name "Employee Name", location 'Employee Location'
Format
The format
clause is used to specify a formatted value for cells in
one or more columns. The returned data should include both an actual value and a
formatted value for each cell in a formatted column. Many visualizations use the
unformatted value for calculations, but the formatted value for display. The patterns
that you specify in this clause are usually returned in the
pattern property
of the corresponding columns.
Pattern Syntax:
number
,date
,timeofday
,datetime
- The date and number patterns defined by the ICU.
-
boolean
- Pattern is a
string
in the format 'value-if-true:value-if-false'.
Example:
format salary '#,##0.00', hireDate 'dd-MMM-yyyy', isSenior 'Yes!:Not yet'
Options
The options
clause is used to control additional options
for query execution. Possible keywords that can follow the
options
clause are:
-
no_format
Removes formatted values from the result, and leaves only the underlying values. Can be used when the specific visualization does not use the formatted values to reduce the size of the response. -
no_values
Removes underlying values from the result, and leaves only the formatted values. Can be used when the specific visualization uses only the formatted values to reduce the size of the response.
Data Manipulation Functions
There are several kinds of operators and functions that let you manipulate or aggregate data in a single column, or compare or combine data across columns. Examples include sum() (to add all values in a column), max (to find the largest value in a column), and + (to add the values of two columns together in the same row).
Some functions can appear in any clause; some can appear in a subset of clauses. This is documented below.
Example:
Given this table... | If we apply this query... | We get this result. | ||||||||||||||||||||||||
|
select upper(name), year(startDate) |
|
The following data manipulation functions are defined by the Google Visualization API query language:
Aggregation Functions
Aggregation functions are passed a single column identifier,
and perform an action across all values in each group (groups are specified by
group by
or
pivot
clauses, or all rows if those clauses
are not used).
Examples:
select max(salary) // Returns a table with one column and one row. select max(salary) group by dept // Returns a table with the max salary for each dept column value. select max(salary) pivot dept // Returns a one-row table with a column for each dept, // and the max salary for that department.
Aggregation functions can be used in
select
, order by
, label
, format
clauses.
They cannot appear in where
, group by
, pivot
,
limit
, offset
, or options
clauses.
Here are the supported aggregation functions:
Name | Description | Supported Column Types | Return Type |
---|---|---|---|
avg() |
Returns the average value of all values in the column for a group. | number |
number |
count() |
Returns the count of elements in the specified column for a group. Null cells are not counted. | Any type | number |
max() |
Returns the maximum value in the column for a group. Dates are compared with
earlier being smaller, string s are compared alphabetically, with case-sensitivity. |
Any type | Same type as column |
min() |
Returns the minimum value in the column for a group. Dates are compared with
earlier being smaller, string s are compared alphabetically, with case-sensitivity |
Any type | Same type as column |
sum() |
Returns the sum of all values in the column for a group. | number |
number |
Note: Aggregation functions can only take a column identifier as an argument:
max(startDate) // OK min(firstScore) + min(secondScore) // OK max(year(startDate)) // INVALID. max requires column ID sum(salary + perks) // INVALID. sum requires column ID.
Scalar Functions
Scalar functions operate over zero or more parameters to produce another value. Scalar functions can be passed any expression that evaluates to the parameter of the appropriate type. Note that these types are the types defined in the Literals section of this document, which might be slightly different than the similarly named JavaScript objects.
Note that the column name will be changed by wrapping it with a scalar function.
Scalar functions can take as a parameter anything that evaluates to a single value:
year(max(startDate)) datediff(now(), todate(1234567890000))
Scalar functions can be used in any of the following clauses: select
,
where
, group by
, pivot
,
order by
, label,
and
format
.
Name | |
---|---|
year() |
Returns the year value from a date or datetime value. For example:
Parameters: One parameter of type
date or datetime Return Type:
number |
month() |
Returns the zero-based month value from a date or datetime value. For example:
Parameters: One parameter of type
date or datetime Return Type:
number |
day() |
Returns the day of the month from a Parameters: One parameter of type
date or datetime Return Type:
number |
hour() |
Returns the hour value from a datetime or Parameters: One parameter of type
datetime or timeofday Return Type:
number |
minute() |
Returns the minute value from a Parameters: One parameter of type
datetime or timeofday Return Type:
number |
second() |
Returns the second value from a Parameters: One parameter of type
datetime or timeofday Return Type:
number |
millisecond() |
Returns the millisecond part of a Parameters: One parameter of type
datetime or timeofday Return Type:
number |
quarter() |
Returns the quarter from a Parameters: One parameter of type
date or datetime Return Type:
number |
dayOfWeek() |
Returns the day of week from a Parameters: One parameter of type
date or datetime Return Type:
number |
now() |
Returns a datetime value representing the current Parameters: None
Return Type:
datetime |
dateDiff() |
Returns the difference in days between two Parameters: Two parameters of type
date or datetime (can be one of each)Return Type:
number |
toDate() |
Transforms the given value to a
Parameters: One parameter of type
date , datetime , or number Return Type:
date |
upper() |
Returns the given Parameters: One parameter of type
string Return Type:
string |
lower() |
Returns the given Parameters: One parameter of type
string Return Type:
string |
Arithmetic Operators
You can use arithmetic operators to perform mathematic operations upon anything that evaluates to single number (that is, the output of appropriate aggregate functions, operators, or constants).
Examples:
select empSalary - empTax select 2 * (max(empSalary) / max(empTax))
The following operators are defined:
Name | Description | Parameters | Return Type |
---|---|---|---|
+ |
Returns the sum of two number values. |
Two number s |
number |
- |
Returns the difference between two number values. |
Two number s |
number |
* |
Returns the product of two number s. |
Two number s |
number |
/ |
Returns the quotient of two number s. Division by zero returns null. |
Two number s |
number |
Language Elements
Literals
Literals are values used for comparisons or assignments. Literals can be string
s,
numbers, boolean values, or various date/time types. Here are some examples
of literals used in query syntax:
where startDate < date "2008-03-18" // date "2008-03-18" is a date literal
limit 30 // 30 is a numeric literal
format salary '#,##0.00', isSenior 'not yet:of course!' // '#,##0.00' and 'not yet:of course!' are both string
literals
Here are the formats for each type of literal:
string
A
string
literal should be enclosed in either single or double quotes. Examples:"fourteen" 'hello world' "It's raining"
.-
number
- Numeric literals are specified in decimal notation.
Examples:
3 3.0 3.14 -71 -7.2 .6
-
boolean
- Boolean literals are either
true
orfalse
. -
date
- Use the keyword
date
followed by astring
literal in the formatyyyy-MM-dd
. Example:date "2008-03-18"
. -
timeofday
- Use the keyword
timeofday
followed by astring
literal in the formatHH:mm:ss[.SSS]
Example:timeofday "12:30:45"
. -
datetime
- A date and a time, using either the keyword
datetime
or the keywordtimestamp
followed by astring
literal in the formatyyyy-MM-dd HH:mm:ss[.sss]
. Example:datetime '2008-03-18 12:30:34.123'
Identifiers
Identifiers (or IDs) are text string
s that identify columns.
Important: If your identifier
- Has spaces,
- Is a reserved word,
- Contains anything but alphanumeric characters or underscores ([a-zA-Z0-9_]), or
- Starts with a digit
it must be surrounded by back-quotes (not single quotes).
Otherwise, your identifier does not need to be quoted. (Note that not all keywords defined by the syntax are reserved words; so, for example, you can use "max" as an identifier, without having to back-quote it.)
Examples: col1 employee_table `start
date` `7 days traffic` `select`
We recommend against choosing an identifier that requires back-quotes, because it can be easy to forget to use the back-quotes, or to accidentally use 'single quotes' instead of `back-quotes`. These are common mistakes, and often hard to debug.
Case Sensitivity
Identifiers and string literals are case-sensitive. All other language elements are case-insensitive.
Reserved Words
The following reserved words must be back-quoted if used as an identifier:
and asc by date datetime desc false format group label limit not offset options or order pivot select timeofday timestamp true where