Legacy SQL Syntax, Functions and Operators
This document details legacy SQL query syntax, functions and operators. The preferred query syntax for BigQuery is GoogleSQL. For information on GoogleSQL, see GoogleSQL query syntax.
Query syntax
Note: Keywords are not case-sensitive. In this document, keywords such
as SELECT
are capitalized for illustration purposes.
SELECT clause
The SELECT
clause specifies a list of expressions to be computed. Expressions in the
SELECT
clause can contain field names, literals, and
function calls (including aggregate functions
and window functions) as well as combinations of the three. The
expression list is comma-separated.
Each expression can be given an alias by adding a space followed by an identifier after the
expression. The optional AS
keyword can be added between the expression and the alias
for improved readability. Aliases defined in a SELECT
clause can be referenced in the
GROUP BY
, HAVING
, and ORDER BY
clauses of the query, but
not by the FROM
, WHERE
, or OMIT RECORD IF
clauses nor by
other expressions in the same SELECT
clause.
Notes:
-
If you use an aggregate function in your
SELECT
clause, you must either use an aggregate function in all expressions or your query must have aGROUP BY
clause which includes all non-aggregated fields in yourSELECT
clause as grouping keys. For example:#legacySQL SELECT word, corpus, COUNT(word) FROM [bigquery-public-data:samples.shakespeare] WHERE word CONTAINS "th" GROUP BY word, corpus; /* Succeeds because all non-aggregated fields are group keys. */
#legacySQL SELECT word, corpus, COUNT(word) FROM [bigquery-public-data:samples.shakespeare] WHERE word CONTAINS "th" GROUP BY word; /* Fails because corpus is not aggregated nor is it a group key. */
-
You can use square brackets to escape
reserved words
so that you can use them as field name and aliases. For example, if you have a column named
"partition", which is a reserved word in BigQuery syntax, the queries referencing
that field fail with obscure error messages unless you escape it with square brackets:
SELECT [partition] FROM ...
Example
This example defines aliases in the SELECT
clause and then references one of them in
the ORDER BY
clause. Notice that the word column can not be referenced using
the word_alias in the WHERE
clause; it must be referenced by name. The
len alias also is not visible in the WHERE
clause. It would be visible to a
HAVING
clause.
#legacySQL SELECT word AS word_alias, LENGTH(word) AS len FROM [bigquery-public-data:samples.shakespeare] WHERE word CONTAINS 'th' ORDER BY len;
WITHIN modifier for aggregate functions
aggregate_function WITHIN RECORD [ [ AS ] alias ]
The WITHIN
keyword causes the aggregate function to aggregate across repeated values
within each record. For every input record, exactly one aggregated output will be produced. This
type of aggregation is referred to as scoped aggregation. Since scoped aggregation
produces output for every record, non-aggregated expressions can be selected alongside
scoped-aggregated expressions without using a GROUP BY
clause.
Most commonly you will use the RECORD
scope when using scoped aggregation. If you
have a very complex nested, repeated schema, you may find a need to perform aggregations within
sub-record scopes. This can be done by replacing the RECORD
keyword in the syntax
above with the name of the node in your schema where you want the aggregation to be performed.
For more information about that advanced behavior, see
Dealing with data.
Example
This example performs a scoped COUNT
aggregation and then filters and sorts the
records by the aggregated value.
#legacySQL SELECT repository.url, COUNT(payload.pages.page_name) WITHIN RECORD AS page_count FROM [bigquery-public-data:samples.github_nested] HAVING page_count > 80 ORDER BY page_count DESC;
FROM clause
FROM [project_name:]datasetId.tableId [ [ AS ] alias ] | (subquery) [ [ AS ] alias ] |JOIN
clause |FLATTEN
clause | table wildcard function
The FROM
clause specifies the source data to be queried. BigQuery queries
can execute directly over tables, over subqueries, over joined tables, and over tables modified by
special-purpose operators described below. Combinations of these data sources can be queried using
the comma, which is the UNION ALL
operator in
BigQuery.
Referencing tables
When referencing a table, both datasetId and tableId must be specified; project_name is optional. If project_name is not specified, BigQuery defaults to the current project. If your project name includes a dash, you must surround the entire table reference with brackets.
Example
[my-dashed-project:dataset1.tableName]
Tables can be given an alias by adding a space followed by an identifier after the table name. The
optional AS
keyword can be added between the tableId and the alias for
improved readability.
When referencing columns from a table, you can use the simple column name or you can prefix the column name with either the alias, if you specified one, or with the datasetId and tableId as long as no project_name was specified. The project_name cannot be included in the column prefix because the colon character is not allowed in field names.
Examples
This example references a column with no table prefix.
#legacySQL SELECT word FROM [bigquery-public-data:samples.shakespeare];
This example prefixes the column name with the datasetId and tableId. Notice that the project_name cannot be included in this example. This method will only work if the dataset is in your current default project.
#legacySQL SELECT samples.shakespeare.word FROM samples.shakespeare;
This example prefixes the column name with a table alias.
#legacySQL SELECT t.word FROM [bigquery-public-data:samples.shakespeare] AS t;
Integer-range partitioned tables
Legacy SQL supports using table decorators to address a specific partition in an integer-range partitioned table. The key to address a range partition is the start of the range.
The following example queries the range partition that starts with 30:
#legacySQL SELECT * FROM dataset.table$30;
Note that you cannot use legacy SQL to query across an entire integer-range partitioned table. Instead, the query returns an error like the following:
Querying tables partitioned on a field is not supported in Legacy SQL
Using subqueries
A subquery is a nested SELECT
statement wrapped in parentheses. The
expressions computed in the SELECT
clause of the subquery are available to the outer
query just as columns of a table would be available.
Subqueries can be used to compute aggregations and other expressions. The full range of SQL operators are available in the subquery. This means a subquery can itself contain other subqueries, subqueries can perform joins and grouping aggregations, etc.
Comma as UNION ALL
Unlike GoogleSQL, legacy SQL uses the comma as a UNION ALL
operator rather
than a CROSS JOIN
operator. This is a legacy behavior that evolved because
historically BigQuery did not support CROSS JOIN
and BigQuery users regularly needed to write
UNION ALL
queries. In GoogleSQL, queries that perform unions are particularly
verbose. Using the comma as the union operator allows such queries to be written much more
efficiently. For example, this query can be used to run a single query over logs from multiple
days.
#legacySQL SELECT FORMAT_UTC_USEC(event.timestamp_in_usec) AS time, request_url FROM [applogs.events_20120501], [applogs.events_20120502], [applogs.events_20120503] WHERE event.username = 'root' AND NOT event.source_ip.is_internal;
Queries that union a large number of tables typically run more slowly than queries that process the same amount of data from a single table. The difference in performance can be up to 50 ms per additional table. A single query can union at most 1,000 tables.
Table wildcard functions
The term table wildcard function refers to a special type of function unique to BigQuery.
These functions are used in the FROM
clause to match a collection of table names
using one of several types of filters. For example, the TABLE_DATE_RANGE
function
can be used to query only a specific set of daily tables. For more information on these functions,
see Table wildcard functions.
FLATTEN operator
(FLATTEN([project_name:]datasetId.tableId, field_to_be_flattened)) (FLATTEN((subquery), field_to_be_flattened))
Unlike typical SQL-processing systems, BigQuery is designed to handle repeated data. Because of
this, BigQuery users sometimes need to write queries that manipulate the structure of repeated
records. One way to do this is by using the FLATTEN
operator.
FLATTEN
converts one node in the schema from repeated to optional. Given a record
with one or more values for a repeated field, FLATTEN
will create multiple records,
one for each value in the repeated field. All other fields selected from the record are duplicated
in each new output record. FLATTEN
can be applied repeatedly in order to remove
multiple levels of repetition.
For more information and examples, see Dealing with data.
JOIN operator
BigQuery supports multiple JOIN
operators in each FROM
clause.
Subsequent JOIN
operations use the results of the previous JOIN
operation as the left JOIN
input. Fields from any preceding JOIN
input
can be used as keys in the ON
clauses of subsequent JOIN
operators.
JOIN types
BigQuery supports INNER
, [FULL|RIGHT|LEFT] OUTER
and
CROSS JOIN
operations. If left unspecified, the default is INNER
.
CROSS JOIN
operations do not allow ON
clauses. CROSS JOIN
can return a large amount of data and might result in a slow and inefficient query or in a query
that exceeds the maximum allowed per-query resources. Such queries will fail with an error. When
possible, prefer queries that do not use CROSS JOIN
. For example, CROSS JOIN
is often used in places where window functions would
be more efficient.
EACH modifier
The EACH
modifier is a hint that tells BigQuery to execute the JOIN
using multiple partitions. This is particularly useful when you know that both sides of the
JOIN
are large. The EACH
modifier can't be used in
CROSS JOIN
clauses.
EACH
used to be encouraged in many cases, but this is no longer the case. When
possible, use JOIN
without the EACH
modifier for better performance.
Use JOIN EACH
when your query has failed with a resources exceeded error message.
Semi-join and Anti-join
In addition to supporting JOIN
in the FROM
clause, BigQuery also
supports two types of joins in the WHERE
clause: semi-join and anti-semi-join. A
semi-join is specified using the IN
keyword with a subquery; anti-join, using the
NOT IN
keywords.
Examples
The following query uses a semi-join to find ngrams where the first word in the ngram is also the second word in another ngram that has "AND" as the third word in the ngram.
#legacySQL SELECT ngram FROM [bigquery-public-data:samples.trigrams] WHERE first IN (SELECT second FROM [bigquery-public-data:samples.trigrams] WHERE third = "AND") LIMIT 10;
The following query uses a semi-join to return the number of women over age 50 who gave birth in the 10 states with the most births.
#legacySQL SELECT mother_age, COUNT(mother_age) total FROM [bigquery-public-data:samples.natality] WHERE state IN (SELECT state FROM (SELECT state, COUNT(state) total FROM [bigquery-public-data:samples.natality] GROUP BY state ORDER BY total DESC LIMIT 10)) AND mother_age > 50 GROUP BY mother_age ORDER BY mother_age DESC
To see the numbers for the other 40 states, you can use an anti-join. The following query is
nearly identical to the previous example, but uses NOT IN
instead of IN
to return the number of women over age 50 who gave birth in the 40 states with the least births.
#legacySQL SELECT mother_age, COUNT(mother_age) total FROM [bigquery-public-data:samples.natality] WHERE state NOT IN (SELECT state FROM (SELECT state, COUNT(state) total FROM [bigquery-public-data:samples.natality] GROUP BY state ORDER BY total DESC LIMIT 10)) AND mother_age > 50 GROUP BY mother_age ORDER BY mother_age DESC
Notes:
- BigQuery does not support correlated semi- or anti-semi-joins. The subquery can not reference any fields from the outer query.
- The subquery used in a semi- or anti-semi-join must select exactly one field.
-
The types of the selected field and the field being used from the outer query in the
WHERE
clause must match exactly. BigQuery will not do any type coercion for semi- or anti-semi-joins.
WHERE clause
The WHERE
clause, sometimes called the predicate, filters records produced by the
FROM
clause using a boolean expression. Multiple conditions can be joined by boolean
AND
and OR
clauses, optionally surrounded by parentheses—()—
to group them. The fields listed in a WHERE
clause do not need to be selected in the
corresponding SELECT
clause and the WHERE
clause expression cannot
reference expressions computed in the SELECT
clause of the query to which the
WHERE
clause belongs.
Note: Aggregate functions cannot be used in the WHERE
clause. Use a
HAVING
clause and an outer query if you need to filter on the
output of an aggregate function.
Example
The following example uses a disjunction of boolean expressions in the WHERE
clause—the two expressions joined by an OR
operator. An input record will pass
through the WHERE
filter if either of the expressions returns true
.
#legacySQL SELECT word FROM [bigquery-public-data:samples.shakespeare] WHERE (word CONTAINS 'prais' AND word CONTAINS 'ing') OR (word CONTAINS 'laugh' AND word CONTAINS 'ed');
OMIT RECORD IF clause
The OMIT RECORD IF
clause is a construct that is unique to BigQuery. It is
particularly useful for dealing with nested, repeated schemas. It is similar to a WHERE
clause, but different in two important ways. First, it uses an exclusionary condition,
which means that records are omitted if the expression returns true
, but kept if the
expression returns false
or null
. Second, the OMIT RECORD IF
clause can (and usually does) use scoped aggregate functions in its condition.
In addition to filtering full records, OMIT...IF
can specify a more narrow scope
to filter just portions of a record. This is done by using the name of a non-leaf node in your
schema rather than RECORD
in your OMIT...IF
clause. This functionality
is rarely used by BigQuery users. You can find more documentation about this advanced behavior
linked from the WITHIN
documentation above.
If you use OMIT...IF
to exclude a portion of a record in a repeating field, and the query also
selects other independently repeating fields, BigQuery omits a
portion of the other repeated records in the query. If you see the error
Cannot perform OMIT IF on repeated scope <scope> with independently repeating pass through field <field>,
we recommend that you switch to GoogleSQL. For information about migrating
OMIT...IF
statements to GoogleSQL, see
Migrating
to GoogleSQL.
Example
Referring back to the example used for the WITHIN
modifier, OMIT RECORD IF
can be used to accomplish the same thing WITHIN
and HAVING
were
used to do in that example.
#legacySQL SELECT repository.url FROM [bigquery-public-data:samples.github_nested] OMIT RECORD IF COUNT(payload.pages.page_name) <= 80;
GROUP BY clause
The GROUP BY
clause lets you group rows that have the same values for a given
field or set of fields so that you can compute aggregations of related fields. Grouping occurs
after the filtering performed in the WHERE
clause but before the expressions in the
SELECT
clause are computed. The expression results cannot be used as group keys in
the GROUP BY
clause.
Example
This query finds the top ten most common first words in the trigrams sample dataset.
In addition to demonstrating the use of the GROUP BY
clause, it demonstrates how
positional indexes can be used instead of field names in the GROUP BY
and
ORDER BY
clauses.
#legacySQL SELECT first, COUNT(ngram) FROM [bigquery-public-data:samples.trigrams] GROUP BY 1 ORDER BY 2 DESC LIMIT 10;
Aggregation performed using a GROUP BY
clause is called grouped aggregation
. Unlike scoped aggregation, grouped aggregation is
common in most SQL processing systems.
The EACH
modifier
The EACH
modifier is a hint that tells BigQuery to execute the GROUP BY
using multiple partitions. This is particularly useful when you know that your dataset contains a
large number of distinct values for the group keys.
EACH
used to be encouraged in many cases, but this is no longer the case.
Using GROUP BY
without the EACH
modifier usually provides better performance.
Use GROUP EACH BY
when your query has failed with a resources exceeded error message.
The ROLLUP
function
When the ROLLUP
function is used, BigQuery adds extra rows to the query result that
represent rolled up aggregations. All fields listed after ROLLUP
must be
enclosed in a single set of parentheses. In rows added because of the ROLLUP
function, NULL
indicates the columns for which the aggregation is rolled up.
Example
This query generates per-year counts of male and female births from the sample natality dataset.
#legacySQL SELECT year, is_male, COUNT(1) as count FROM [bigquery-public-data:samples.natality] WHERE year >= 2000 AND year <= 2002 GROUP BY ROLLUP(year, is_male) ORDER BY year, is_male;
These are the results of the query. Notice that there are rows where one or both of the group keys
are NULL
. These rows are the rollup rows.
+------+---------+----------+ | year | is_male | count | +------+---------+----------+ | NULL | NULL | 12122730 | | 2000 | NULL | 4063823 | | 2000 | false | 1984255 | | 2000 | true | 2079568 | | 2001 | NULL | 4031531 | | 2001 | false | 1970770 | | 2001 | true | 2060761 | | 2002 | NULL | 4027376 | | 2002 | false | 1966519 | | 2002 | true | 2060857 | +------+---------+----------+
When using the ROLLUP
function, you can use the GROUPING
function
to distinguish between rows that were added because of the ROLLUP
function and rows
that actually have a NULL
value for the group key.
Example
This query adds the GROUPING
function to the previous example to better identify the
rows added because of the ROLLUP
function.
#legacySQL SELECT year, GROUPING(year) as rollup_year, is_male, GROUPING(is_male) as rollup_gender, COUNT(1) as count FROM [bigquery-public-data:samples.natality] WHERE year >= 2000 AND year <= 2002 GROUP BY ROLLUP(year, is_male) ORDER BY year, is_male;
These are the result the new query returns.
+------+-------------+---------+---------------+----------+ | year | rollup_year | is_male | rollup_gender | count | +------+-------------+---------+---------------+----------+ | NULL | 1 | NULL | 1 | 12122730 | | 2000 | 0 | NULL | 1 | 4063823 | | 2000 | 0 | false | 0 | 1984255 | | 2000 | 0 | true | 0 | 2079568 | | 2001 | 0 | NULL | 1 | 4031531 | | 2001 | 0 | false | 0 | 1970770 | | 2001 | 0 | true | 0 | 2060761 | | 2002 | 0 | NULL | 1 | 4027376 | | 2002 | 0 | false | 0 | 1966519 | | 2002 | 0 | true | 0 | 2060857 | +------+-------------+---------+---------------+----------+
Notes:
-
Non-aggregated fields in the
SELECT
clause must be listed in theGROUP BY
clause.#legacySQL SELECT word, corpus, COUNT(word) FROM [bigquery-public-data:samples.shakespeare] WHERE word CONTAINS "th" GROUP BY word, corpus; /* Succeeds because all non-aggregated fields are group keys. */
#legacySQL SELECT word, corpus, COUNT(word) FROM [bigquery-public-data:samples.shakespeare] WHERE word CONTAINS "th" GROUP BY word; /* Fails because corpus is not aggregated nor is it a group key. */
-
Expressions computed in the
SELECT
clause cannot be used in the correspondingGROUP BY
clause.#legacySQL SELECT word, corpus, COUNT(word) word_count FROM [bigquery-public-data:samples.shakespeare] WHERE word CONTAINS "th" GROUP BY word, corpus, word_count; /* Fails because word_count is not visible to this
GROUP BY
clause. */ - Grouping by float and double values is not supported, because the equality function for those types is not well-defined.
-
Because the system is interactive, queries that produce a large number of groups might fail. The
use of the
TOP
function instead ofGROUP BY
might solve some scaling problems.
HAVING clause
The HAVING
clause behaves exactly like the WHERE
clause except that it is evaluated after the SELECT
clause so the results of all
computed expressions are visible to the HAVING
clause. The HAVING clause can only
refer to outputs of the corresponding SELECT
clause.
Example
This query computes the most common first words in the ngram sample dataset that contain the letter a and occur at most 10,000 times.
#legacySQL SELECT first, COUNT(ngram) ngram_count FROM [bigquery-public-data:samples.trigrams] GROUP BY 1 HAVING first contains "a" AND ngram_count < 10000 ORDER BY 2 DESC LIMIT 10;
ORDER BY clause
The ORDER BY
clause sorts the results of a query in ascending or descending order
using one or more key fields. To sort by multiple fields or aliases, enter them as a
comma-separated list. The results are sorted on the fields in the order in which they are listed.
Use DESC
(descending) or ASC
(ascending) to specify the sort direction.
ASC
is the default. A different sort direction can be specified for each sort key.
The ORDER BY
clause is evaluated after the SELECT
clause so it can
reference the output of any expression computed in the SELECT
. If a field is given
an alias in the SELECT
clause, the alias must be used in the ORDER BY
clause.
LIMIT clause
The LIMIT
clause limits the number of rows in the returned result set. Since BigQuery
queries regularly operate over very large numbers of rows, LIMIT
is a good way to
avoid long-running queries by processing only a subset of the rows.
Notes:
-
The
LIMIT
clause will stop processing and return results when it satisfies your requirements. This can reduce processing time for some queries, but when you specify aggregate functions such as COUNT orORDER BY
clauses, the full result set must still be processed before returning results. TheLIMIT
clause is the last to be evaluated. -
A query with a
LIMIT
clause may still be non-deterministic if there is no operator in the query that guarantees the ordering of the output result set. This is because BigQuery executes using a large number of parallel workers. The order in which parallel jobs return is not guaranteed. -
The
LIMIT
clause cannot contain any functions; it takes only a numeric constant.
Query grammar
The individual clauses of BigQuery SELECT
statements are described in detail
above. Here we present the full grammar of SELECT
statements in a compact form with links back to the individual sections.
query: SELECT { * | field_path.* | expression } [ [ AS ] alias ] [ , ... ] [ FROM from_body [ WHERE bool_expression ] [ OMIT RECORD IF bool_expression] [ GROUP [ EACH ] BY [ ROLLUP ] { field_name_or_alias } [ , ... ] ] [ HAVING bool_expression ] [ ORDER BY field_name_or_alias [ { DESC | ASC } ] [, ... ] ] [ LIMIT n ] ]; from_body: { from_item [, ...] | # Warning: Comma means UNION ALL here from_item [ join_type ] JOIN [ EACH ] from_item [ ON join_predicate ] | (FLATTEN({ table_name | (query) }, field_name_or_alias)) | table_wildcard_function } from_item: { table_name | (query) } [ [ AS ] alias ] join_type: { INNER | [ FULL ] [ OUTER ] | RIGHT [ OUTER ] | LEFT [ OUTER ] | CROSS } join_predicate: field_from_one_side_of_the_join = field_from_the_other_side_of_the_join [ AND ...] expression: { literal_value | field_name_or_alias | function_call } bool_expression: { expression_which_results_in_a_boolean_value | bool_expression AND bool_expression | bool_expression OR bool_expression | NOT bool_expression }
Notation:
- Square brackets "[ ]" indicate optional clauses.
- Curly braces "{ }" enclose a set of options.
- The vertical bar "|" indicates a logical OR.
- A comma or keyword followed by an ellipsis within square brackets "[, ... ]" indicates that the preceding item can repeat in a list with the specified separator.
- Parentheses "( )" indicate literal parentheses.
Supported functions and operators
Most SELECT
statement clauses support functions. Fields
referenced in a function don't need to be listed in any SELECT
clause. Therefore, the following query is valid, even though the
clicks
field is not displayed directly:
#legacySQL SELECT country, SUM(clicks) FROM table GROUP BY country;
Aggregate functions | |
---|---|
AVG() |
Returns the average of the values for a group of rows ... |
BIT_AND() |
Returns the result of a bitwise AND operation ... |
BIT_OR() |
Returns the result of a bitwise OR operation ... |
BIT_XOR() |
Returns the result of a bitwise XOR operation ... |
CORR() |
Returns the Pearson correlation coefficient of a set of number pairs. |
COUNT() |
Returns the total number of values ... |
COUNT([DISTINCT]) |
Returns the total number of non-NULL values ... |
COVAR_POP() |
Computes the population covariance of the values ... |
COVAR_SAMP() |
Computes the sample covariance of the values ... |
EXACT_COUNT_DISTINCT() |
Returns the exact number of non-NULL, distinct values for the specified field. |
FIRST() |
Returns the first sequential value in the scope of the function. |
GROUP_CONCAT() |
Concatenates multiple strings into a single string ... |
GROUP_CONCAT_UNQUOTED() |
Concatenates multiple strings into a single string ... will not add double quotes ... |
LAST() |
Returns the last sequential value ... |
MAX() |
Returns the maximum value ... |
MIN() |
Returns the minimum value ... |
NEST() |
Aggregates all values in the current aggregation scope into a repeated field. |
NTH() |
Returns the nth sequential value ... |
QUANTILES() |
Computes approximate minimum, maximum, and quantiles ... |
STDDEV() |
Returns the standard deviation ... |
STDDEV_POP() |
Computes the population standard deviation ... |
STDDEV_SAMP() |
Computes the sample standard deviation ... |
SUM() |
Returns the sum total of the values ... |
TOP() ... COUNT(*) |
Returns the top max_records records by frequency. |
UNIQUE() |
Returns the set of unique, non-NULL values ... |
VARIANCE() |
Computes the variance of the values ... |
VAR_POP() |
Computes the population variance of the values ... |
VAR_SAMP() |
Computes the sample variance of the values ... |
Arithmetic operators | |
---|---|
+ |
Addition |
- |
Subtraction |
* |
Multiplication |
/ |
Division |
% |
Modulo |
Bitwise functions | |
---|---|
& |
Bitwise AND |
| |
Bitwise OR |
^ |
Bitwise XOR |
<< |
Bitwise shift left |
>> |
Bitwise shift right |
~ |
Bitwise NOT |
BIT_COUNT() |
Returns the number of bits ... |
Casting functions | |
---|---|
BOOLEAN() |
Cast to boolean. |
BYTES() |
Cast to bytes. |
CAST(expr AS type) |
Converts expr into a variable of type type . |
FLOAT() |
Cast to double. |
HEX_STRING() |
Cast to hexadecimal string. |
INTEGER() |
Cast to integer. |
STRING() |
Cast to string. |
Comparison functions | |
---|---|
expr1 = expr2 |
Returns true if the expressions are equal. |
expr1 != expr2 expr1 <> expr2
|
Returns true if the expressions are not equal. |
expr1 > expr2 |
Returns true if expr1 is greater than expr2 . |
expr1 < expr2 |
Returns true if expr1 is less than expr2 . |
expr1 >= expr2 |
Returns true if expr1 is greater than or equal to expr2 . |
expr1 <= expr2 |
Returns true if expr1 is less than or equal to expr2 . |
expr1 BETWEEN expr2 AND expr3 |
Returns true if the value of expr1
is between expr2 and expr3 , inclusive. |
expr IS NULL |
Returns true if expr is NULL. |
expr IN() |
Returns true if expr matches
expr1 , expr2 , or any value in the parentheses. |
COALESCE() |
Returns the first argument that isn't NULL. |
GREATEST() |
Returns the largest numeric_expr parameter. |
IFNULL() |
If argument is not null, returns the argument. |
IS_INF() |
Returns true if positive or negative infinity. |
IS_NAN() |
Returns true if argument is NaN . |
IS_EXPLICITLY_DEFINED() |
deprecated: Use expr IS NOT NULL instead. |
LEAST() |
Returns the smallest argument numeric_expr parameter. |
NVL() |
If expr is not null, returns expr , otherwise returns null_default . |
Date and time functions | |
---|---|
CURRENT_DATE() |
Returns current date in the format %Y-%m-%d . |
CURRENT_TIME() |
Returns the server's current time in the format %H:%M:%S . |
CURRENT_TIMESTAMP() |
Returns the server's current time in the format %Y-%m-%d %H:%M:%S . |
DATE() |
Returns the date in the format %Y-%m-%d . |
DATE_ADD() |
Adds the specified interval to a TIMESTAMP data type. |
DATEDIFF() |
Returns the number of days between two TIMESTAMP data types. |
DAY() |
Returns the day of the month as an integer between 1 and 31. |
DAYOFWEEK() |
Returns the day of the week as an integer between 1 (Sunday) and 7 (Saturday). |
DAYOFYEAR() |
Returns the day of the year as an integer between 1 and 366. |
FORMAT_UTC_USEC() |
Returns a UNIX timestamp in the format YYYY-MM-DD HH:MM:SS.uuuuuu . |
HOUR() |
Returns the hour of a TIMESTAMP as an integer between 0 and 23. |
MINUTE() |
Returns the minutes of a TIMESTAMP as an integer between 0 and 59. |
MONTH() |
Returns the month of a TIMESTAMP as an integer between 1 and 12. |
MSEC_TO_TIMESTAMP() |
Converts a UNIX timestamp in milliseconds to a TIMESTAMP. |
NOW() |
Returns the current UNIX timestamp in microseconds. |
PARSE_UTC_USEC() |
Converts a date string to a UNIX timestamp in microseconds. |
QUARTER() |
Returns the quarter of the year of a TIMESTAMP as an integer between 1 and 4. |
SEC_TO_TIMESTAMP() |
Converts a UNIX timestamp in seconds to a TIMESTAMP. |
SECOND() |
Returns the seconds of a TIMESTAMP as an integer between 0 and 59. |
STRFTIME_UTC_USEC() |
Returns a date string in the format date_format_str. |
TIME() |
Returns a TIMESTAMP in the format %H:%M:%S . |
TIMESTAMP() |
Convert a date string to a TIMESTAMP. |
TIMESTAMP_TO_MSEC() |
Converts a TIMESTAMP to a UNIX timestamp in milliseconds. |
TIMESTAMP_TO_SEC() |
Converts a TIMESTAMP to a UNIX timestamp in seconds. |
TIMESTAMP_TO_USEC() |
Converts a TIMESTAMP to a UNIX timestamp in microseconds. |
USEC_TO_TIMESTAMP() |
Converts a UNIX timestamp in microseconds to a TIMESTAMP. |
UTC_USEC_TO_DAY() |
Shifts a UNIX timestamp in microseconds to the beginning of the day it occurs in. |
UTC_USEC_TO_HOUR() |
Shifts a UNIX timestamp in microseconds to the beginning of the hour it occurs in. |
UTC_USEC_TO_MONTH() |
Shifts a UNIX timestamp in microseconds to the beginning of the month it occurs in. |
UTC_USEC_TO_WEEK() |
Returns a UNIX timestamp in microseconds that represents a day in the week. |
UTC_USEC_TO_YEAR() |
Returns a UNIX timestamp in microseconds that represents the year. |
WEEK() |
Returns the week of a TIMESTAMP as an integer between 1 and 53. |
YEAR() |
Returns the year of a TIMESTAMP. |
IP functions | |
---|---|
FORMAT_IP() |
Converts 32 least significant bits of integer_value to human-readable IPv4 address string. |
PARSE_IP() |
Converts a string representing IPv4 address to unsigned integer value. |
FORMAT_PACKED_IP() |
Returns a human-readable IP address in the form
10.1.5.23 or 2620:0:1009:1:216:36ff:feef:3f . |
PARSE_PACKED_IP() |
Returns an IP address in BYTES. |
JSON functions | |
---|---|
JSON_EXTRACT() |
Selects a value according to the JSONPath expression and returns a JSON string. |
JSON_EXTRACT_SCALAR() |
Selects a value according to the JSONPath expression and returns a JSON scalar. |
Logical operators | |
---|---|
expr AND expr |
Returns true if both expressions are true. |
expr OR expr |
Returns true if one or both expressions are true. |
NOT expr |
Returns true if the expression is false. |
Mathematical functions | |
---|---|
ABS() |
Returns the absolute value of the argument. |
ACOS() |
Returns the arc cosine of the argument. |
ACOSH() |
Returns the arc hyperbolic cosine of the argument. |
ASIN() |
Returns the arc sine of the argument. |
ASINH() |
Returns the arc hyperbolic sine of the argument. |
ATAN() |
Returns the arc tangent of the argument. |
ATANH() |
Returns the arc hyperbolic tangent of the argument. |
ATAN2() |
Returns the arc tangent of the two arguments. |
CEIL() |
Rounds the argument up to the nearest whole number and returns the rounded value. |
COS() |
Returns the cosine of the argument. |
COSH() |
Returns the hyperbolic cosine of the argument. |
DEGREES() |
Converts from radians to degrees. |
EXP() |
Returns e to the power of the argument. |
FLOOR() |
Rounds the argument down to the nearest whole number. |
LN() LOG()
|
Returns the natural logarithm of the argument. |
LOG2() |
Returns the Base-2 logarithm of the argument. |
LOG10() |
Returns the Base-10 logarithm of the argument. |
PI() |
Returns the constant π. |
POW() |
Returns first argument to the power of the second argument. |
RADIANS() |
Converts from degrees to radians. |
RAND() |
Returns a random float value in the range 0.0 <= value < 1.0. |
ROUND() |
Rounds the argument either up or down to the nearest whole number. |
SIN() |
Returns the sine of the argument. |
SINH() |
Returns the hyperbolic sine of the argument. |
SQRT() |
Returns the square root of the expression. |
TAN() |
Returns the tangent of the argument. |
TANH() |
Returns the hyperbolic tangent of the argument. |
Regular expression functions | |
---|---|
REGEXP_MATCH() |
Returns true if the argument matches the regular expression. |
REGEXP_EXTRACT() |
Returns the portion of the argument that matches the capturing group within the regular expression. |
REGEXP_REPLACE() |
Replaces a substring that matches a regular expression. |
String functions | |
---|---|
CONCAT() |
Returns the concatenation of two or more strings, or NULL if any of the values are NULL. |
expr CONTAINS 'str' |
Returns true if expr contains the specified string argument. |
INSTR() |
Returns the one-based index of the first occurrence of a string. |
LEFT() |
Returns the leftmost characters of a string. |
LENGTH() |
Returns the length of the string. |
LOWER() |
Returns the original string with all characters in lower case. |
LPAD() |
Inserts characters to the left of a string. |
LTRIM() |
Removes characters from the left side of a string. |
REPLACE() |
Replaces all occurrences of a substring. |
RIGHT() |
Returns the rightmost characters of a string. |
RPAD() |
Inserts characters to the right side of a string. |
RTRIM() |
Removes trailing characters from the right side of a string. |
SPLIT() |
Splits a string into repeated substrings. |
SUBSTR() |
Returns a substring ... |
UPPER() |
Returns the original string with all characters in upper case. |
Table wildcard functions | |
---|---|
TABLE_DATE_RANGE() |
Queries multiple daily tables that span a date range. |
TABLE_DATE_RANGE_STRICT() |
Queries multiple daily tables that span a date range, with no missing dates. |
TABLE_QUERY() |
Queries tables whose names match a specified predicate. |
URL functions | |
---|---|
HOST() |
Given a URL, returns the host name as a string. |
DOMAIN() |
Given a URL, returns the domain as a string. |
TLD() |
Given a URL, returns the top level domain plus any country domain in the URL. |
Window functions | |
---|---|
AVG() COUNT(*) COUNT([DISTINCT]) MAX() MIN() STDDEV() SUM() |
The same operation as the corresponding Aggregate functions, but are computed over a window defined by the OVER clause. |
CUME_DIST() |
Returns a double that indicates the cumulative distribution of a value in a group of values ... |
DENSE_RANK() |
Returns the integer rank of a value in a group of values. |
FIRST_VALUE() |
Returns the first value of the specified field in the window. |
LAG() |
Enables you to read data from a previous row within a window. |
LAST_VALUE() |
Returns the last value of the specified field in the window. |
LEAD() |
Enables you to read data from a following row within a window. |
NTH_VALUE() |
Returns the value of <expr> at position
<n> of the window frame ...
|
NTILE() |
Divides the window into the specified number of buckets. |
PERCENT_RANK() |
Returns the rank of the current row, relative to the other rows in the partition. |
PERCENTILE_CONT() |
Returns an interpolated value that would map to the percentile argument with respect to the window ... |
PERCENTILE_DISC() |
Returns the value nearest the percentile of the argument over the window. |
RANK() |
Returns the integer rank of a value in a group of values. |
RATIO_TO_REPORT() |
Returns the ratio of each value to the sum of the values. |
ROW_NUMBER() |
Returns the current row number of the query result over the window. |
Other functions | |
---|---|
CASE WHEN ... THEN |
Use CASE to choose among two or more alternate expressions in your query. |
CURRENT_USER() |
Returns the email address of the user running the query. |
EVERY() |
Returns true if the argument is true for all of its inputs. |
FROM_BASE64() |
Converts the base-64 encoded input string into BYTES format. |
HASH() |
Computes and returns a 64-bit signed hash value ... |
FARM_FINGERPRINT() |
Computes and returns a 64-bit signed fingerprint value ... |
IF() |
If first argument is true, returns second argument; otherwise returns third argument. |
POSITION() |
Returns the one-based, sequential position of the argument. |
SHA1() |
Returns a SHA1 hash, in BYTES format. |
SOME() |
Returns true if argument is true for at least one of its inputs. |
TO_BASE64() |
Converts the BYTES argument to a base-64 encoded string. |
Aggregate functions
Aggregate functions return values that represent summaries of larger sets of data, which makes these functions particularly useful for analyzing logs. An aggregate function operates against a collection of values and returns a single value per table, group, or scope:
- Table aggregation
Uses an aggregate function to summarize all qualifying rows in the table. For example:
SELECT COUNT(f1) FROM ds.Table;
- Group aggregation
Uses an aggregate function and a
GROUP BY
clause that specifies a non-aggregated field to summarize rows by group. For example:SELECT COUNT(f1) FROM ds.Table GROUP BY b1;
The TOP function represents a specialized case of group aggregation.
- Scoped aggregation
This feature applies only to tables that have nested fields.
Uses an aggregate function and theWITHIN
keyword to aggregate repeated values within a defined scope. For example:SELECT COUNT(m1.f2) WITHIN RECORD FROM Table;
The scope can be
RECORD
, which corresponds to entire row, or a node (repeated field in a row). Aggregation functions operate over the values within the scope and return aggregated results for each record or node.
You can apply a restriction to an aggregate function using one of the following options:
-
An alias in a subselect query. The restriction is specified in the outer
WHERE
clause.#legacySQL SELECT corpus, count_corpus_words FROM (SELECT corpus, count(word) AS count_corpus_words FROM [bigquery-public-data:samples.shakespeare] GROUP BY corpus) AS sub_shakespeare WHERE count_corpus_words > 4000
-
An alias in a HAVING clause.
#legacySQL SELECT corpus, count(word) AS count_corpus_words FROM [bigquery-public-data:samples.shakespeare] GROUP BY corpus HAVING count_corpus_words > 4000;
You can also refer to an alias in the GROUP BY
or ORDER BY
clauses.
Syntax
Aggregate functions | |
---|---|
AVG() |
Returns the average of the values for a group of rows ... |
BIT_AND() |
Returns the result of a bitwise AND operation ... |
BIT_OR() |
Returns the result of a bitwise OR operation ... |
BIT_XOR() |
Returns the result of a bitwise XOR operation ... |
CORR() |
Returns the Pearson correlation coefficient of a set of number pairs. |
COUNT() |
Returns the total number of values ... |
COUNT([DISTINCT]) |
Returns the total number of non-NULL values ... |
COVAR_POP() |
Computes the population covariance of the values ... |
COVAR_SAMP() |
Computes the sample covariance of the values ... |
EXACT_COUNT_DISTINCT() |
Returns the exact number of non-NULL, distinct values for the specified field. |
FIRST() |
Returns the first sequential value in the scope of the function. |
GROUP_CONCAT() |
Concatenates multiple strings into a single string ... |
GROUP_CONCAT_UNQUOTED() |
Concatenates multiple strings into a single string ... will not add double quotes ... |
LAST() |
Returns the last sequential value ... |
MAX() |
Returns the maximum value ... |
MIN() |
Returns the minimum value ... |
NEST() |
Aggregates all values in the current aggregation scope into a repeated field. |
NTH() |
Returns the nth sequential value ... |
QUANTILES() |
Computes approximate minimum, maximum, and quantiles ... |
STDDEV() |
Returns the standard deviation ... |
STDDEV_POP() |
Computes the population standard deviation ... |
STDDEV_SAMP() |
Computes the sample standard deviation ... |
SUM() |
Returns the sum total of the values ... |
TOP() ... COUNT(*) |
Returns the top max_records records by frequency. |
UNIQUE() |
Returns the set of unique, non-NULL values ... |
VARIANCE() |
Computes the variance of the values ... |
VAR_POP() |
Computes the population variance of the values ... |
VAR_SAMP() |
Computes the sample variance of the values ... |
AVG(numeric_expr)
- Returns the average of the values for a group of rows computed by
numeric_expr
. Rows with a NULL value are not included in the calculation. BIT_AND(numeric_expr)
- Returns the result of a bitwise
AND
operation between each instance ofnumeric_expr
across all rows.NULL
values are ignored. This function returnsNULL
if all instances ofnumeric_expr
evaluate toNULL
. BIT_OR(numeric_expr)
- Returns the result of a bitwise
OR
operation between each instance ofnumeric_expr
across all rows.NULL
values are ignored. This function returnsNULL
if all instances ofnumeric_expr
evaluate toNULL
. BIT_XOR(numeric_expr)
- Returns the result of a bitwise
XOR
operation between each instance ofnumeric_expr
across all rows.NULL
values are ignored. This function returnsNULL
if all instances ofnumeric_expr
evaluate toNULL
. CORR(numeric_expr, numeric_expr)
- Returns the Pearson correlation coefficient of a set of number pairs.
COUNT(*)
- Returns the total number of values (NULL and non-NULL) in the scope of the function. Unless you are using
COUNT(*)
with theTOP
function, it is better to explicitly specify the field to count. COUNT([DISTINCT] field [, n])
- Returns the total number of non-NULL values in the scope of the function.
If you use the
DISTINCT
keyword, the function returns the number of distinct values for the specified field. Note that the returned value forDISTINCT
is a statistical approximation and is not guaranteed to be exact.Use
EXACT_COUNT_DISTINCT()
for an exact answer.If you require greater accuracy from
, you can specify a second parameter,COUNT(DISTINCT)
n
, which gives the threshold below which exact results are guaranteed. By default,n
is 1000, but if you give a largern
, you will get exact results forCOUNT(DISTINCT)
up to that value ofn
. However, giving larger values ofn
will reduce scalability of this operator and may substantially increase query execution time or cause the query to fail.To compute the exact number of distinct values, use EXACT_COUNT_DISTINCT. Or, for a more scalable approach, consider using
GROUP EACH BY
on the relevant field(s) and then applyingCOUNT(*)
. TheGROUP EACH BY
approach is more scalable but might incur a slight up-front performance penalty. COVAR_POP(numeric_expr1, numeric_expr2)
- Computes the population covariance of the values computed by
numeric_expr1
andnumeric_expr2
. COVAR_SAMP(numeric_expr1, numeric_expr2)
- Computes the sample covariance of the values computed by
numeric_expr1
andnumeric_expr2
. EXACT_COUNT_DISTINCT(field)
- Returns the exact number of non-NULL, distinct values for the specified field. For better scalability and performance, use COUNT(DISTINCT field).
FIRST(expr)
- Returns the first sequential value in the scope of the function.
GROUP_CONCAT('str' [, separator])
-
Concatenates multiple strings into a single string, where each value is separated by the optional
separator
parameter. Ifseparator
is omitted, BigQuery returns a comma-separated string.If a string in the source data contains a double quote character,
GROUP_CONCAT
returns the string with double quotes added. For example, the stringa"b
would return as"a""b"
. UseGROUP_CONCAT_UNQUOTED
if you prefer that these strings do not return with double quotes added.Example:
#legacySQL SELECT GROUP_CONCAT(x) FROM ( SELECT 'a"b' AS x), ( SELECT 'cd' AS x);
GROUP_CONCAT_UNQUOTED('str' [, separator])
-
Concatenates multiple strings into a single string, where each value is separated by the optional
separator
parameter. Ifseparator
is omitted, BigQuery returns a comma-separated string.Unlike
GROUP_CONCAT
, this function will not add double quotes to returned values that include a double quote character. For example, the stringa"b
would return asa"b
.Example:
#legacySQL SELECT GROUP_CONCAT_UNQUOTED(x) FROM ( SELECT 'a"b' AS x), ( SELECT 'cd' AS x);
LAST(field)
- Returns the last sequential value in the scope of the function.
MAX(field)
- Returns the maximum value in the scope of the function.
MIN(field)
- Returns the minimum value in the scope of the function.
NEST(expr)
-
Aggregates all values in the current aggregation scope into a repeated field. For example, the query
"SELECT x, NEST(y) FROM ... GROUP BY x"
returns one output record for each distinctx
value, and contains a repeated field for ally
values paired withx
in the query input. TheNEST
function requires aGROUP BY
clause.BigQuery automatically flattens query results, so if you use the
NEST
function on the top level query, the results won't contain repeated fields. Use theNEST
function when using a subselect that produces intermediate results for immediate use by the same query. NTH(n, field)
- Returns the
n
th sequential value in the scope of the function, wheren
is a constant. TheNTH
function starts counting at 1, so there is no zeroth term. If the scope of the function has less thann
values, the function returnsNULL
. QUANTILES(expr[, buckets])
-
Computes approximate minimum, maximum, and quantiles for the input expression.
NULL
input values are ignored. Empty or exclusively-NULL
input results inNULL
output. The number of quantiles computed is controlled with the optionalbuckets
parameter, which includes the minimum and maximum in the count. To compute approximate N-tiles, use N+1buckets
. The default value ofbuckets
is 100. (Note: The default of 100 does not estimate percentiles. To estimate percentiles, use 101buckets
at minimum.) If specified explicitly,buckets
must be at least 2.The fractional error per quantile is epsilon = 1 /
buckets
, which means that the error decreases as the number of buckets increases. For example:QUANTILES(<expr>, 2) # computes min and max with 50% error. QUANTILES(<expr>, 3) # computes min, median, and max with 33% error. QUANTILES(<expr>, 5) # computes quartiles with 25% error. QUANTILES(<expr>, 11) # computes deciles with 10% error. QUANTILES(<expr>, 21) # computes vigintiles with 5% error. QUANTILES(<expr>, 101) # computes percentiles with 1% error.
The
NTH
function can be used to pick a particular quantile, but remember thatNTH
is 1-based, and thatQUANTILES
returns the minimum ("0th" quantile) in the first position, and the maximum ("100th" percentile or "Nth" N-tile) in the last position. For example,NTH(11, QUANTILES(expr, 21))
estimates the median ofexpr
, whereasNTH(20, QUANTILES(expr, 21))
estimates the 19th vigintile (95th percentile) ofexpr
. Both estimates have a 5% margin of error.To improve accuracy, use more buckets. For example, to reduce the margin of error for the previous calculations from 5% to 0.1%, use 1001 buckets instead of 21, and adjust the argument to the
NTH
function accordingly. To calculate the median with 0.1% error, useNTH(501, QUANTILES(expr, 1001))
; for the 95th percentile with 0.1% error, useNTH(951, QUANTILES(expr, 1001))
. STDDEV(numeric_expr)
- Returns the standard deviation of the values computed by
numeric_expr
. Rows with a NULL value are not included in the calculation. TheSTDDEV
function is an alias forSTDDEV_SAMP
. STDDEV_POP(numeric_expr)
- Computes the population standard deviation of the value computed by
numeric_expr
. UseSTDDEV_POP()
to compute the standard deviation of a dataset that encompasses the entire population of interest. If your dataset comprises only a representative sample of the population, useSTDDEV_SAMP()
instead. For more information about population versus sample standard deviation, see Standard deviation on Wikipedia. STDDEV_SAMP(numeric_expr)
- Computes the sample standard deviation of the value computed by
numeric_expr
. UseSTDDEV_SAMP()
to compute the standard deviation of an entire population based on a representative sample of the population. If your dataset comprises the entire population, useSTDDEV_POP()
instead. For more information about population versus sample standard deviation, see Standard deviation on Wikipedia. SUM(field)
- Returns the sum total of the values in the scope of the function. For use with numerical data types only.
TOP(field|alias[, max_values][,multiplier]) ... COUNT(*)
- Returns the top max_records records by frequency. See the TOP description below for details.
UNIQUE(expr)
- Returns the set of unique, non-NULL values in the scope of the function in an undefined order. Similar to a large
GROUP BY
clause without theEACH
keyword, the query will fail with a "Resources Exceeded" error if there are too many distinct values. UnlikeGROUP BY
, however, theUNIQUE
function can be applied with scoped aggregation, allowing efficient operation on nested fields with a limited number of values. VARIANCE(numeric_expr)
- Computes the variance of the values computed by
numeric_expr
. Rows with a NULL value are not included in the calculation. TheVARIANCE
function is an alias forVAR_SAMP
. VAR_POP(numeric_expr)
- Computes the population variance of the values computed by
numeric_expr
. For more information about population versus sample standard deviation, see Standard deviation on Wikipedia. VAR_SAMP(numeric_expr)
- Computes the sample variance of the values computed by
numeric_expr
. For more information about population versus sample standard deviation, see Standard deviation on Wikipedia.
TOP() function
TOP is a function that is an alternative to the GROUP BY clause. It is used as simplified syntax for GROUP BY ... ORDER BY ... LIMIT ...
. Generally, the TOP function performs faster than the full ... GROUP BY ... ORDER BY ... LIMIT ...
query, but may only return approximate results. The following is the syntax for the TOP function:
TOP(field|alias[, max_values][,multiplier]) ... COUNT(*)
When using TOP in a SELECT
clause, you must include COUNT(*)
as one of the fields.
A query that uses the TOP() function can return only two fields: the TOP field, and the COUNT(*) value.
field|alias
- The field or alias to return.
max_values
- [Optional] The maximum number of results to return. Default is 20.
multiplier
- A positive integer that increases the value(s) returned by
COUNT(*)
by the multiple specified.
TOP() examples
-
Basic example queries that use
TOP()
The following queries use
TOP()
to return 10 rows.Example 1:
#legacySQL SELECT TOP(word, 10) as word, COUNT(*) as cnt FROM [bigquery-public-data:samples.shakespeare] WHERE word CONTAINS "th";
Example 2:
#legacySQL SELECT word, left(word, 3) FROM (SELECT TOP(word, 10) AS word, COUNT(*) FROM [bigquery-public-data:samples.shakespeare] WHERE word CONTAINS "th");
-
Compare
TOP()
toGROUP BY...ORDER BY...LIMIT
The query returns, in order, the top 10 most frequently used words containing "th", and the number of documents the words was used in. The
TOP
query will execute much faster:Example without
TOP()
:#legacySQL SELECT word, COUNT(*) AS cnt FROM ds.Table WHERE word CONTAINS 'th' GROUP BY word ORDER BY cnt DESC LIMIT 10;
Example with
TOP()
:#legacySQL SELECT TOP(word, 10), COUNT(*) FROM ds.Table WHERE word contains 'th';
-
Using the
multiplier
parameter.The following queries show how the
multiplier
parameter affects the query result. The first query returns the number of births per month in Wyoming. The second query uses tomultiplier
parameter to multiply thecnt
values by 100.Example without the
multiplier
parameter:#legacySQL SELECT TOP(month,3) as month, COUNT(*) as cnt FROM [bigquery-public-data:samples.natality] WHERE state = "WY";
Returns:
+-------+-------+ | month | cnt | +-------+-------+ | 7 | 19594 | | 5 | 19038 | | 8 | 19030 | +-------+-------+
Example with the
multiplier
parameter:#legacySQL SELECT TOP(month,3,100) as month, COUNT(*) as cnt FROM [bigquery-public-data:samples.natality] WHERE state = "WY";
Returns:
+-------+---------+ | month | cnt | +-------+---------+ | 7 | 1959400 | | 5 | 1903800 | | 8 | 1903000 | +-------+---------+
Note: You must include COUNT(*)
in the SELECT
clause to use TOP
.
Advanced examples
-
Average and standard deviation grouped by condition
The following query returns the average and standard deviation of birth weights in Ohio in 2003, grouped by mothers who do and do not smoke.
Example:
#legacySQL SELECT cigarette_use, /* Finds average and standard deviation */ AVG(weight_pounds) baby_weight, STDDEV(weight_pounds) baby_weight_stdev, AVG(mother_age) mother_age FROM [bigquery-public-data:samples.natality] WHERE year=2003 AND state='OH' /* Group the result values by those */ /* who smoked and those who didn't. */ GROUP BY cigarette_use;
-
Filter query results using an aggregated value
In order to filter query results using an aggregated value (for example, filtering by the value of a
SUM
), use theHAVING
function.HAVING
compares a value to a result determined by an aggregation function, as opposed toWHERE
, which operates on each row prior to aggregation.Example:
#legacySQL SELECT state, /* If 'is_male' is True, return 'Male', */ /* otherwise return 'Female' */ IF (is_male, 'Male', 'Female') AS sex, /* The count value is aliased as 'cnt' */ /* and used in the HAVING clause below. */ COUNT(*) AS cnt FROM [bigquery-public-data:samples.natality] WHERE state != '' GROUP BY state, sex HAVING cnt > 3000000 ORDER BY cnt DESC
Returns:
+-------+--------+---------+ | state | sex | cnt | +-------+--------+---------+ | CA | Male | 7060826 | | CA | Female | 6733288 | | TX | Male | 5107542 | | TX | Female | 4879247 | | NY | Male | 4442246 | | NY | Female | 4227891 | | IL | Male | 3089555 | +-------+--------+---------+
Arithmetic operators
Arithmetic operators take numeric arguments and return a numeric result. Each argument can be a numeric literal or a numeric value returned by a query. If the arithmetic operation evaluates to an undefined result, the operation returns NULL
.
Syntax
Operator | Description | Example |
---|---|---|
+ | Addition |
Returns: 10 |
- | Subtraction |
Returns: 1 |
* | Multiplication |
Returns: 24 |
/ | Division |
Returns: 1.5 |
% | Modulo |
Returns: 2 |
Bitwise functions
Bitwise functions operate at the level of individual bits and require numerical arguments. For more information about bitwise functions, see Bitwise operation.
Three additional bitwise functions, BIT_AND
, BIT_OR
and BIT_XOR
, are documented in aggregate functions.
Syntax
Operator | Description | Example |
---|---|---|
& | Bitwise AND |
Returns: 0 |
| | Bitwise OR |
Returns: 28 |
^ | Bitwise XOR |
Returns: 1 |
<< | Bitwise shift left |
Returns: 16 |
>> | Bitwise shift right |
Returns: 2 |
~ | Bitwise NOT |
Returns: -3 |
BIT_COUNT(<numeric_expr>) |
Returns the number of bits that are set in |
Returns: 4 |
Casting functions
Casting functions change the data type of a numeric expression. Casting functions are particularly useful for ensuring that arguments in a comparison function have the same data type.
Syntax
Casting functions | |
---|---|
BOOLEAN() |
Cast to boolean. |
BYTES() |
Cast to bytes. |
CAST(expr AS type) |
Converts expr into a variable of type type . |
FLOAT() |
Cast to double. |
HEX_STRING() |
Cast to hexadecimal string. |
INTEGER() |
Cast to integer. |
STRING() |
Cast to string. |
BOOLEAN(<numeric_expr>)
-
- Returns
true
if<numeric_expr>
is not 0 and not NULL. - Returns
false
if<numeric_expr>
is 0. - Returns
NULL
if<numeric_expr>
is NULL.
- Returns
BYTES(string_expr)
- Returns
string_expr
as a value of typebytes
. CAST(expr AS type)
- Converts
expr
into a variable of typetype
. FLOAT(expr)
-
Returns
expr
as a double. Theexpr
can be a string like'45.78'
, but the function returnsNULL
for non-numeric values. HEX_STRING(numeric_expr)
- Returns
numeric_expr
as a hexadecimal string. INTEGER(expr)
-
Casts
expr
to a 64-bit integer.- Returns NULL if
expr
is a string that doesn't correspond to an integer value. - Returns the number of microseconds since the unix epoch if
expr
is a timestamp.
- Returns NULL if
STRING(numeric_expr)
- Returns
numeric_expr
as a string.
Comparison functions
Comparison functions return true
or false
, based on the following types of comparisons:
- A comparison of two expressions.
- A comparison of an expression or set of expressions to a specific criteria, such as being in a specified list, being NULL, or being a non-default optional value.
Some of the functions listed below return values other than true
or false
, but the values they return are based on comparison operations.
You can use either numeric or string expressions as arguments for comparison functions. (String constants must be enclosed in single or double quotes.) The expressions can be literals or values fetched by a query. Comparison functions are most often used as filtering conditions in WHERE
clauses, but they can be used in other clauses.
Syntax
Comparison functions | |
---|---|
expr1 = expr2 |
Returns true if the expressions are equal. |
expr1 != expr2 expr1 <> expr2
|
Returns true if the expressions are not equal. |
expr1 > expr2 |
Returns true if expr1 is greater than expr2 . |
expr1 < expr2 |
Returns true if expr1 is less than expr2 . |
expr1 >= expr2 |
Returns true if expr1 is greater than or equal to expr2 . |
expr1 <= expr2 |
Returns true if expr1 is less than or equal to expr2 . |
expr1 BETWEEN expr2 AND expr3 |
Returns true if the value of expr1
is between expr2 and expr3 , inclusive. |
expr IS NULL |
Returns true if expr is NULL. |
expr IN() |
Returns true if expr matches
expr1 , expr2 , or any value in the parentheses. |
COALESCE() |
Returns the first argument that isn't NULL. |
GREATEST() |
Returns the largest numeric_expr parameter. |
IFNULL() |
If argument is not null, returns the argument. |
IS_INF() |
Returns true if positive or negative infinity. |
IS_NAN() |
Returns true if argument is NaN . |
IS_EXPLICITLY_DEFINED() |
deprecated: Use expr IS NOT NULL instead. |
LEAST() |
Returns the smallest argument numeric_expr parameter. |
NVL() |
If expr is not null, returns expr , otherwise returns null_default . |
expr1 = expr2
- Returns
true
if the expressions are equal. expr1 != expr2
expr1 <> expr2
- Returns
true
if the expressions are not equal. expr1 > expr2
- Returns
true
ifexpr1
is greater thanexpr2
. expr1 < expr2
- Returns
true
ifexpr1
is less thanexpr2
. expr1 >= expr2
- Returns
true
ifexpr1
is greater than or equal toexpr2
. expr1 <= expr2
- Returns
true
ifexpr1
is less than or equal toexpr2
. expr1 BETWEEN expr2 AND expr3
-
Returns
true
if the value ofexpr1
is greater than or equal toexpr2
, and less than or equal toexpr3
. expr IS NULL
- Returns
true
ifexpr
is NULL. expr IN(expr1, expr2, ...)
- Returns
true
ifexpr
matchesexpr1
,expr2
, or any value in the parentheses. TheIN
keyword is an efficient shorthand for(expr = expr1 || expr = expr2 || ...)
. The expressions used with theIN
keyword must be constants and they must match the data type ofexpr
. TheIN
clause can also be used to create semi-joins and anti-joins. For more information, see Semi-join and Anti-join. COALESCE(<expr1>, <expr2>, ...)
- Returns the first argument that isn't NULL.
GREATEST(numeric_expr1, numeric_expr2, ...)
-
Returns the largest
numeric_expr
parameter. All parameters must be numeric, and all parameters must be the same type. If any parameter isNULL
, this function returnsNULL
.To ignore
NULL
values, use theIFNULL
function to changeNULL
values to a value that doesn't affect the comparison. In the following code example, theIFNULL
function is used to changeNULL
values to-1
, which doesn't affect the comparison between positive numbers.SELECT GREATEST(IFNULL(a,-1), IFNULL(b,-1)) FROM (SELECT 1 as a, NULL as b);
IFNULL(expr, null_default)
- If
expr
is not null, returnsexpr
, otherwise returnsnull_default
. IS_INF(numeric_expr)
- Returns
true
ifnumeric_expr
is positive or negative infinity. IS_NAN(numeric_expr)
- Returns
true
ifnumeric_expr
is the specialNaN
numeric value. IS_EXPLICITLY_DEFINED(expr)
-
This function is deprecated. Use
expr IS NOT NULL
instead. LEAST(numeric_expr1, numeric_expr2, ...)
-
Returns the smallest
numeric_expr
parameter. All parameters must be numeric, and all parameters must be the same type. If any parameter isNULL
, this function returnsNULL
NVL(expr, null_default)
- If
expr
is not null, returnsexpr
, otherwise returnsnull_default
. TheNVL
function is an alias forIFNULL
.
Date and time functions
The following functions enable date and time manipulation for UNIX timestamps, date strings and TIMESTAMP data types. For more information about working with the TIMESTAMP data type, see Using TIMESTAMP.
Date and time functions that work with UNIX timestamps operate on UNIX time. Date and time functions return values based upon the UTC time zone.
Syntax
Date and time functions | |
---|---|
CURRENT_DATE() |
Returns current date in the format %Y-%m-%d . |
CURRENT_TIME() |
Returns the server's current time in the format %H:%M:%S . |
CURRENT_TIMESTAMP() |
Returns the server's current time in the format %Y-%m-%d %H:%M:%S . |
DATE() |
Returns the date in the format %Y-%m-%d . |
DATE_ADD() |
Adds the specified interval to a TIMESTAMP data type. |
DATEDIFF() |
Returns the number of days between two TIMESTAMP data types. |
DAY() |
Returns the day of the month as an integer between 1 and 31. |
DAYOFWEEK() |
Returns the day of the week as an integer between 1 (Sunday) and 7 (Saturday). |
DAYOFYEAR() |
Returns the day of the year as an integer between 1 and 366. |
FORMAT_UTC_USEC() |
Returns a UNIX timestamp in the format YYYY-MM-DD HH:MM:SS.uuuuuu . |
HOUR() |
Returns the hour of a TIMESTAMP as an integer between 0 and 23. |
MINUTE() |
Returns the minutes of a TIMESTAMP as an integer between 0 and 59. |
MONTH() |
Returns the month of a TIMESTAMP as an integer between 1 and 12. |
MSEC_TO_TIMESTAMP() |
Converts a UNIX timestamp in milliseconds to a TIMESTAMP. |
NOW() |
Returns the current UNIX timestamp in microseconds. |
PARSE_UTC_USEC() |
Converts a date string to a UNIX timestamp in microseconds. |
QUARTER() |
Returns the quarter of the year of a TIMESTAMP as an integer between 1 and 4. |
SEC_TO_TIMESTAMP() |
Converts a UNIX timestamp in seconds to a TIMESTAMP. |
SECOND() |
Returns the seconds of a TIMESTAMP as an integer between 0 and 59. |
STRFTIME_UTC_USEC() |
Returns a date string in the format date_format_str. |
TIME() |
Returns a TIMESTAMP in the format %H:%M:%S . |
TIMESTAMP() |
Convert a date string to a TIMESTAMP. |
TIMESTAMP_TO_MSEC() |
Converts a TIMESTAMP to a UNIX timestamp in milliseconds. |
TIMESTAMP_TO_SEC() |
Converts a TIMESTAMP to a UNIX timestamp in seconds. |
TIMESTAMP_TO_USEC() |
Converts a TIMESTAMP to a UNIX timestamp in microseconds. |
USEC_TO_TIMESTAMP() |
Converts a UNIX timestamp in microseconds to a TIMESTAMP. |
UTC_USEC_TO_DAY() |
Shifts a UNIX timestamp in microseconds to the beginning of the day it occurs in. |
UTC_USEC_TO_HOUR() |
Shifts a UNIX timestamp in microseconds to the beginning of the hour it occurs in. |
UTC_USEC_TO_MONTH() |
Shifts a UNIX timestamp in microseconds to the beginning of the month it occurs in. |
UTC_USEC_TO_WEEK() |
Returns a UNIX timestamp in microseconds that represents a day in the week. |
UTC_USEC_TO_YEAR() |
Returns a UNIX timestamp in microseconds that represents the year. |
WEEK() |
Returns the week of a TIMESTAMP as an integer between 1 and 53. |
YEAR() |
Returns the year of a TIMESTAMP. |
CURRENT_DATE()
Returns a human-readable string of the current date in the format
%Y-%m-%d
.Example:
SELECT CURRENT_DATE();
Returns: 2013-02-01
CURRENT_TIME()
Returns a human-readable string of the server's current time in the format
%H:%M:%S
.Example:
SELECT CURRENT_TIME();
Returns: 01:32:56
CURRENT_TIMESTAMP()
Returns a TIMESTAMP data type of the server's current time in the format
%Y-%m-%d %H:%M:%S
.Example:
SELECT CURRENT_TIMESTAMP();
Returns: 2013-02-01 01:33:35 UTC
DATE(<timestamp>)
Returns a human-readable string of a TIMESTAMP data type in the format
%Y-%m-%d
.Example:
SELECT DATE(TIMESTAMP('2012-10-01 02:03:04'));
Returns: 2012-10-01
DATE_ADD(<timestamp>,<interval>,
<interval_units>)Adds the specified interval to a TIMESTAMP data type. Possible
interval_units
values includeYEAR
,MONTH
,DAY
,HOUR
,MINUTE
, andSECOND
. Ifinterval
is a negative number, the interval is subtracted from the TIMESTAMP data type.Example:
SELECT DATE_ADD(TIMESTAMP("2012-10-01 02:03:04"), 5, "YEAR");
Returns: 2017-10-01 02:03:04 UTC
SELECT DATE_ADD(TIMESTAMP("2012-10-01 02:03:04"), -5, "YEAR");
Returns: 2007-10-01 02:03:04 UTC
DATEDIFF(<timestamp1>,<timestamp2>)
Returns the number of days between two TIMESTAMP data types. The result is positive if the first TIMESTAMP data type comes after the second TIMESTAMP data type, and otherwise the result is negative.
Example:
SELECT DATEDIFF(TIMESTAMP('2012-10-02 05:23:48'), TIMESTAMP('2011-06-24 12:18:35'));
Returns: 466
Example:
SELECT DATEDIFF(TIMESTAMP('2011-06-24 12:18:35'), TIMESTAMP('2012-10-02 05:23:48'));
Returns: -466
DAY(<timestamp>)
Returns the day of the month of a TIMESTAMP data type as an integer between 1 and 31, inclusively.
Example:
SELECT DAY(TIMESTAMP('2012-10-02 05:23:48'));
Returns: 2
DAYOFWEEK(<timestamp>)
Returns the day of the week of a TIMESTAMP data type as an integer between 1 (Sunday) and 7 (Saturday), inclusively.
Example:
SELECT DAYOFWEEK(TIMESTAMP("2012-10-01 02:03:04"));
Returns: 2
DAYOFYEAR(<timestamp>)
Returns the day of the year of a TIMESTAMP data type as an integer between 1 and 366, inclusively. The integer 1 refers to January 1.
Example:
SELECT DAYOFYEAR(TIMESTAMP("2012-10-01 02:03:04"));
Returns: 275
FORMAT_UTC_USEC(<unix_timestamp>)
Returns a human-readable string representation of a UNIX timestamp in the format
YYYY-MM-DD HH:MM:SS.uuuuuu
.Example:
SELECT FORMAT_UTC_USEC(1274259481071200);
Returns: 2010-05-19 08:58:01.071200
HOUR(<timestamp>)
Returns the hour of a TIMESTAMP data type as an integer between 0 and 23, inclusively.
Example:
SELECT HOUR(TIMESTAMP('2012-10-02 05:23:48'));
Returns: 5
MINUTE(<timestamp>)
Returns the minutes of a TIMESTAMP data type as an integer between 0 and 59, inclusively.
Example:
SELECT MINUTE(TIMESTAMP('2012-10-02 05:23:48'));
Returns: 23
MONTH(<timestamp>)
Returns the month of a TIMESTAMP data type as an integer between 1 and 12, inclusively.
Example:
SELECT MONTH(TIMESTAMP('2012-10-02 05:23:48'));
Returns: 10
MSEC_TO_TIMESTAMP(<expr>)
- Converts a UNIX timestamp in milliseconds to a TIMESTAMP data type.
Example:
SELECT MSEC_TO_TIMESTAMP(1349053323000);
Returns: 2012-10-01 01:02:03 UTC
SELECT MSEC_TO_TIMESTAMP(1349053323000 + 1000)
Returns: 2012-10-01 01:02:04 UTC
NOW()
Returns the current UNIX timestamp in microseconds.
Example:
SELECT NOW();
Returns: 1359685811687920
PARSE_UTC_USEC(<date_string>)
-
Converts a date string to a UNIX timestamp in microseconds.
date_string
must have the formatYYYY-MM-DD HH:MM:SS[.uuuuuu]
. The fractional part of the second can be up to 6 digits long or can be omitted.TIMESTAMP_TO_USEC is an equivalent function that converts a TIMESTAMP data type argument instead of a date string.
Example:
SELECT PARSE_UTC_USEC("2012-10-01 02:03:04");
Returns: 1349056984000000
QUARTER(<timestamp>)
Returns the quarter of the year of a TIMESTAMP data type as an integer between 1 and 4, inclusively.
Example:
SELECT QUARTER(TIMESTAMP("2012-10-01 02:03:04"));
Returns: 4
SEC_TO_TIMESTAMP(<expr>)
Converts a UNIX timestamp in seconds to a TIMESTAMP data type.
Example:
SELECT SEC_TO_TIMESTAMP(1355968987);
Returns: 2012-12-20 02:03:07 UTC
SELECT SEC_TO_TIMESTAMP(INTEGER(1355968984 + 3));
Returns: 2012-12-20 02:03:07 UTC
SECOND(<timestamp>)
-
Returns the seconds of a TIMESTAMP data type as an integer between 0 and 59, inclusively.
During a leap second, the integer range is between 0 and 60, inclusively.
Example:
SELECT SECOND(TIMESTAMP('2012-10-02 05:23:48'));
Returns: 48
STRFTIME_UTC_USEC(<unix_timestamp>,
<date_format_str>)-
Returns a human-readable date string in the format date_format_str. date_format_str can include date-related punctuation characters (such as / and -) and special characters accepted by the strftime function in C++ (such as %d for day of month).
Use the
UTC_USEC_TO_<function_name>
functions if you plan to group query data by time intervals, such as getting all data for a certain month, because the functions are more efficient.Example:
SELECT STRFTIME_UTC_USEC(1274259481071200, "%Y-%m-%d");
Returns: 2010-05-19
TIME(<timestamp>)
Returns a human-readable string of a TIMESTAMP data type, in the format
%H:%M:%S
.Example:
SELECT TIME(TIMESTAMP('2012-10-01 02:03:04'));
Returns: 02:03:04
TIMESTAMP(<date_string>)
Convert a date string to a TIMESTAMP data type.
Example:
SELECT TIMESTAMP("2012-10-01 01:02:03");
Returns: 2012-10-01 01:02:03 UTC
TIMESTAMP_TO_MSEC(<timestamp>)
Converts a TIMESTAMP data type to a UNIX timestamp in milliseconds.
Example:
SELECT TIMESTAMP_TO_MSEC(TIMESTAMP("2012-10-01 01:02:03"));
Returns: 1349053323000
TIMESTAMP_TO_SEC(<timestamp>)
- Converts a TIMESTAMP data type to a UNIX timestamp in seconds.
Example:
SELECT TIMESTAMP_TO_SEC(TIMESTAMP("2012-10-01 01:02:03"));
Returns: 1349053323
TIMESTAMP_TO_USEC(<timestamp>)
-
Converts a TIMESTAMP data type to a UNIX timestamp in microseconds.
PARSE_UTC_USEC is an equivalent function that converts a data string argument instead of a TIMESTAMP data type.
Example:
SELECT TIMESTAMP_TO_USEC(TIMESTAMP("2012-10-01 01:02:03"));
Returns: 1349053323000000
USEC_TO_TIMESTAMP(<expr>)
Converts a UNIX timestamp in microseconds to a TIMESTAMP data type.
Example:
SELECT USEC_TO_TIMESTAMP(1349053323000000);
Returns: 2012-10-01 01:02:03 UTC
SELECT USEC_TO_TIMESTAMP(1349053323000000 + 1000000)
Returns: 2012-10-01 01:02:04 UTC
UTC_USEC_TO_DAY(<unix_timestamp>)
-
Shifts a UNIX timestamp in microseconds to the beginning of the day it occurs in.
For example, if
unix_timestamp
occurs on May 19th at 08:58, this function returns a UNIX timestamp for May 19th at 00:00 (midnight).Example:
SELECT UTC_USEC_TO_DAY(1274259481071200);
Returns: 1274227200000000
UTC_USEC_TO_HOUR(<unix_timestamp>)
-
Shifts a UNIX timestamp in microseconds to the beginning of the hour it occurs in.
For example, if
unix_timestamp
occurs at 08:58, this function returns a UNIX timestamp for 08:00 on the same day.Example:
SELECT UTC_USEC_TO_HOUR(1274259481071200);
Returns: 1274256000000000
UTC_USEC_TO_MONTH(<unix_timestamp>)
-
Shifts a UNIX timestamp in microseconds to the beginning of the month it occurs in.
For example, if
unix_timestamp
occurs on March 19th, this function returns a UNIX timestamp for March 1st of the same year.Example:
SELECT UTC_USEC_TO_MONTH(1274259481071200);
Returns: 1272672000000000
UTC_USEC_TO_WEEK(<unix_timestamp>,
<day_of_week>)-
Returns a UNIX timestamp in microseconds that represents a day in the week of the
unix_timestamp
argument. This function takes two arguments: a UNIX timestamp in microseconds, and a day of the week from 0 (Sunday) to 6 (Saturday).For example, if
unix_timestamp
occurs on Friday, 2008-04-11, and you setday_of_week
to 2 (Tuesday), the function returns a UNIX timestamp for Tuesday, 2008-04-08.Example:
SELECT UTC_USEC_TO_WEEK(1207929480000000, 2) AS tuesday;
Returns: 1207612800000000
UTC_USEC_TO_YEAR(<unix_timestamp>)
-
Returns a UNIX timestamp in microseconds that represents the year of the
unix_timestamp
argument.For example, if
unix_timestamp
occurs in 2010, the function returns1274259481071200
, the microsecond representation of2010-01-01 00:00
.Example:
SELECT UTC_USEC_TO_YEAR(1274259481071200);
Returns: 1262304000000000
WEEK(<timestamp>)
Returns the week of a TIMESTAMP data type as an integer between 1 and 53, inclusively.
Weeks begin on Sunday, so if January 1 is on a day other than Sunday, week 1 has fewer than 7 days and the first Sunday of the year is the first day of week 2.
Example:
SELECT WEEK(TIMESTAMP('2014-12-31'));
Returns: 53
YEAR(<timestamp>)
- Returns the year of a TIMESTAMP data type.
Example:
SELECT YEAR(TIMESTAMP('2012-10-02 05:23:48'));
Returns: 2012
Advanced examples
-
Convert integer timestamp results into human-readable format
The following query finds the top 5 moments in time in which the most Wikipedia revisions took place. In order to display results in a human-readable format, use BigQuery's
FORMAT_UTC_USEC()
function, which takes a timestamp, in microseconds, as an input. This query multiplies the Wikipedia POSIX format timestamps (in seconds) by 1000000 to convert the value into microseconds.Example:
#legacySQL SELECT /* Multiply timestamp by 1000000 and convert */ /* into a more human-readable format. */ TOP (FORMAT_UTC_USEC(timestamp * 1000000), 5) AS top_revision_time, COUNT (*) AS revision_count FROM [bigquery-public-data:samples.wikipedia];
Returns:
+----------------------------+----------------+ | top_revision_time | revision_count | +----------------------------+----------------+ | 2002-02-25 15:51:15.000000 | 20976 | | 2002-02-25 15:43:11.000000 | 15974 | | 2010-02-02 03:34:51.000000 | 3 | | 2010-02-02 01:04:59.000000 | 3 | | 2010-02-01 23:55:05.000000 | 3 | +----------------------------+----------------+
-
Bucketing Results by Timestamp
It's useful to use date and time functions to group query results into buckets corresponding to particular years, months, or days. The following example uses the
UTC_USEC_TO_MONTH()
function to display how many characters each Wikipedia contributor uses in their revision comments per month.Example:
#legacySQL SELECT contributor_username, /* Return the timestamp shifted to the * start of the month, formatted in * a human-readable format. Uses the * 'LEFT()' string function to return only * the first 7 characters of the formatted timestamp. */ LEFT (FORMAT_UTC_USEC( UTC_USEC_TO_MONTH(timestamp * 1000000)),7) AS month, SUM(LENGTH(comment)) as total_chars_used FROM [bigquery-public-data:samples.wikipedia] WHERE (contributor_username != '' AND contributor_username IS NOT NULL) AND timestamp > 1133395200 AND timestamp < 1157068800 GROUP BY contributor_username, month ORDER BY total_chars_used DESC;
Returns (truncated):
+--------------------------------+---------+-----------------------+ | contributor_username | month | total_chars_used | +--------------------------------+---------+-----------------------+ | Kingbotk | 2006-08 | 18015066 | | SmackBot | 2006-03 | 7838365 | | SmackBot | 2006-05 | 5148863 | | Tawkerbot2 | 2006-05 | 4434348 | | Cydebot | 2006-06 | 3380577 | etc ...
IP functions
IP functions convert IP addresses to and from human-readable form.
Syntax
IP functions | |
---|---|
FORMAT_IP() |
Converts 32 least significant bits of integer_value to human-readable IPv4 address string. |
PARSE_IP() |
Converts a string representing IPv4 address to unsigned integer value. |
FORMAT_PACKED_IP() |
Returns a human-readable IP address in the form
10.1.5.23 or 2620:0:1009:1:216:36ff:feef:3f . |
PARSE_PACKED_IP() |
Returns an IP address in BYTES. |
FORMAT_IP(integer_value)
- Converts 32 least significant bits of
integer_value
to human-readable IPv4 address string. For example,FORMAT_IP(1)
will return string'0.0.0.1'
. PARSE_IP(readable_ip)
- Converts a string representing IPv4 address to unsigned integer value. For example,
PARSE_IP('0.0.0.1')
will return1
. If string is not a valid IPv4 address,PARSE_IP
will returnNULL
.
BigQuery supports writing IPv4 and IPv6 addresses in packed strings, as 4- or 16-byte binary data in network byte order. The functions described below support parsing the addresses to and from human readable form. These functions work only on string fields with IPs.
Syntax
FORMAT_PACKED_IP(packed_ip)
Returns a human-readable IP address, in the form
10.1.5.23
or2620:0:1009:1:216:36ff:feef:3f
. Examples:-
FORMAT_PACKED_IP('0123456789@ABCDE')
returns'3031:3233:3435:3637:3839:4041:4243:4445'
FORMAT_PACKED_IP('0123')
returns'48.49.50.51'
-
PARSE_PACKED_IP(readable_ip)
Returns an IP address in BYTES. If the input string is not a valid IPv4 or IPv6 address,
PARSE_PACKED_IP
will returnNULL
. Examples:PARSE_PACKED_IP('48.49.50.51')
returns'MDEyMw=='
PARSE_PACKED_IP('3031:3233:3435:3637:3839:4041:4243:4445')
returns'MDEyMzQ1Njc4OUBBQkNERQ=='
JSON functions
BigQuery's JSON functions give you the ability to find values within your stored JSON data, by using JSONPath-like expressions.
Storing JSON data can be more flexible than declaring all of your individual fields in your table schema, but can lead to higher costs. When you select data from a JSON string, you are charged for scanning the entire string, which is more expensive than if each field is in a separate column. The query is also slower since the entire string needs to be parsed at query time. But for ad-hoc or rapidly-changing schemas, the flexibility of JSON can be worth the extra cost.
Use JSON functions instead of BigQuery's regular expression functions if working with structured data, as JSON functions are easier to use.
Syntax
JSON functions | |
---|---|
JSON_EXTRACT() |
Selects a value according to the JSONPath expression and returns a JSON string. |
JSON_EXTRACT_SCALAR() |
Selects a value according to the JSONPath expression and returns a JSON scalar. |
JSON_EXTRACT(json, json_path)
-
Selects a value in
json
according to the JSONPath expressionjson_path
.json_path
must be a string constant. Returns the value in JSON string format. JSON_EXTRACT_SCALAR(json, json_path)
-
Selects a value in
json
according to the JSONPath expressionjson_path
.json_path
must be a string constant. Returns a scalar JSON value.
Logical operators
Logical operators perform binary or ternary logic on expressions. Binary logic returns true
or false
. Ternary logic accommodates NULL
values and returns true
, false
, or NULL
.
Syntax
Logical operators | |
---|---|
expr AND expr |
Returns true if both expressions are true. |
expr OR expr |
Returns true if one or both expressions are true. |
NOT expr |
Returns true if the expression is false. |
expr AND expr
- Returns
true
if both expressions are true. - Returns
false
if one or both of the expressions are false. - Returns
NULL
if both expressions are NULL or one expression is true and the other is NULL.
- Returns
expr OR expr
- Returns
true
if one or both expressions are true. - Returns
false
if both expressions are false. - Returns
NULL
if both expressions are NULL or one expression is false and the other is NULL.
- Returns
NOT expr
- Returns
true
if the expression is false. - Returns
false
if the expression if true. - Returns
NULL
if the expression is NULL.
You can use
NOT
with other functions as an negation operator. For example,NOT IN(expr1, expr2)
orIS NOT NULL
.- Returns
Mathematical functions
Mathematical functions take numeric arguments and return a numeric result. Each argument can be a numeric literal or a numeric value returned by a query. If the mathematical function evaluates to an undefined result, the operation returns NULL
.
Syntax
Mathematical functions | |
---|---|
ABS() |
Returns the absolute value of the argument. |
ACOS() |
Returns the arc cosine of the argument. |
ACOSH() |
Returns the arc hyperbolic cosine of the argument. |
ASIN() |
Returns the arc sine of the argument. |
ASINH() |
Returns the arc hyperbolic sine of the argument. |
ATAN() |
Returns the arc tangent of the argument. |
ATANH() |
Returns the arc hyperbolic tangent of the argument. |
ATAN2() |
Returns the arc tangent of the two arguments. |
CEIL() |
Rounds the argument up to the nearest whole number and returns the rounded value. |
COS() |
Returns the cosine of the argument. |
COSH() |
Returns the hyperbolic cosine of the argument. |
DEGREES() |
Converts from radians to degrees. |
EXP() |
Returns e to the power of the argument. |
FLOOR() |
Rounds the argument down to the nearest whole number. |
LN() LOG()
|
Returns the natural logarithm of the argument. |
LOG2() |
Returns the Base-2 logarithm of the argument. |
LOG10() |
Returns the Base-10 logarithm of the argument. |
PI() |
Returns the constant π. |
POW() |
Returns first argument to the power of the second argument. |
RADIANS() |
Converts from degrees to radians. |
RAND() |
Returns a random float value in the range 0.0 <= value < 1.0. |
ROUND() |
Rounds the argument either up or down to the nearest whole number. |
SIN() |
Returns the sine of the argument. |
SINH() |
Returns the hyperbolic sine of the argument. |
SQRT() |
Returns the square root of the expression. |
TAN() |
Returns the tangent of the argument. |
TANH() |
Returns the hyperbolic tangent of the argument. |
ABS(numeric_expr)
- Returns the absolute value of the argument.
ACOS(numeric_expr)
- Returns the arc cosine of the argument.
ACOSH(numeric_expr)
- Returns the arc hyperbolic cosine of the argument.
ASIN(numeric_expr)
- Returns the arc sine of the argument.
ASINH(numeric_expr)
- Returns the arc hyperbolic sine of the argument.
ATAN(numeric_expr)
- Returns the arc tangent of the argument.
ATANH(numeric_expr)
- Returns the arc hyperbolic tangent of the argument.
ATAN2(numeric_expr1, numeric_expr2)
- Returns the arc tangent of the two arguments.
CEIL(numeric_expr)
- Rounds the argument up to the nearest whole number and returns the rounded value.
COS(numeric_expr)
- Returns the cosine of the argument.
COSH(numeric_expr)
- Returns the hyperbolic cosine of the argument.
DEGREES(numeric_expr)
- Returns
numeric_expr
, converted from radians to degrees. EXP(numeric_expr)
- Returns the result of raising the constant "e" - the base of the natural logarithm - to the power of numeric_expr.
FLOOR(numeric_expr)
- Rounds the argument down to the nearest whole number and returns the rounded value.
LN(numeric_expr)
LOG(numeric_expr)
- Returns the natural logarithm of the argument.
LOG2(numeric_expr)
- Returns the Base-2 logarithm of the argument.
LOG10(numeric_expr)
- Returns the Base-10 logarithm of the argument.
PI()
- Returns the constant π. The
PI()
function requires parentheses to signify that it is a function, but takes no arguments in those parentheses. You can usePI()
like a constant with mathematical and arithmetic functions. POW(numeric_expr1, numeric_expr2)
- Returns the result of raising
numeric_expr1
to the power ofnumeric_expr2
. RADIANS(numeric_expr)
- Returns
numeric_expr
, converted from degrees to radians. (Note that π radians equals 180 degrees.) RAND([int32_seed])
- Returns a random float value in the range 0.0 <= value < 1.0. Each
int32_seed
value always generates the same sequence of random numbers within a given query, as long as you don't use aLIMIT
clause. Ifint32_seed
is not specified, BigQuery uses the current timestamp as the seed value. ROUND(numeric_expr [, digits])
- Rounds the argument either up or down to the nearest whole number (or if specified, to the specified number of digits) and returns the rounded value.
SIN(numeric_expr)
- Returns the sine of the argument.
SINH(numeric_expr)
- Returns the hyperbolic sine of the argument.
SQRT(numeric_expr)
- Returns the square root of the expression.
TAN(numeric_expr)
- Returns the tangent of the argument.
TANH(numeric_expr)
- Returns the hyperbolic tangent of the argument.
Advanced examples
-
Bounding box query
The following query returns a collection of points within a rectangular bounding box centered around San Francisco (37.46, -122.50).
Example:
#legacySQL SELECT year, month, AVG(mean_temp) avg_temp, MIN(min_temperature) min_temp, MAX(max_temperature) max_temp FROM [weather_geo.table] WHERE /* Return values between a pair of */ /* latitude and longitude coordinates */ lat / 1000 > 37.46 AND lat / 1000 < 37.65 AND long / 1000 > -122.50 AND long / 1000 < -122.30 GROUP BY year, month ORDER BY year, month ASC;
-
Approximate Bounding Circle Query
Return a collection of up to 100 points within an approximated circle determined by the using the Spherical Law of Cosines, centered around Denver Colorado (39.73, -104.98). This query makes use of BigQuery's mathematical and trigonometric functions, such as
PI()
,SIN()
, andCOS()
.Because the Earth isn't an absolute sphere, and longitude+latitude converges at the poles, this query returns an approximation that can be useful for many types of data.
Example:
#legacySQL SELECT distance, lat, long, temp FROM (SELECT ((ACOS(SIN(39.73756700 * PI() / 180) * SIN((lat/1000) * PI() / 180) + COS(39.73756700 * PI() / 180) * COS((lat/1000) * PI() / 180) * COS((-104.98471790 - (long/1000)) * PI() / 180)) * 180 / PI()) * 60 * 1.1515) AS distance, AVG(mean_temp) AS temp, AVG(lat/1000) lat, AVG(long/1000) long FROM [weather_geo.table] WHERE month=1 GROUP BY distance) WHERE distance < 100 ORDER BY distance ASC LIMIT 100;
Regular expression functions
BigQuery provides regular expression support using the re2 library; see that documentation for its regular expression syntax.
Note that the regular expressions are global matches; to start matching at the beginning of a word you must use the ^ character.
Syntax
Regular expression functions | |
---|---|
REGEXP_MATCH() |
Returns true if the argument matches the regular expression. |
REGEXP_EXTRACT() |
Returns the portion of the argument that matches the capturing group within the regular expression. |
REGEXP_REPLACE() |
Replaces a substring that matches a regular expression. |
REGEXP_MATCH('str', 'reg_exp')
Returns true if str matches the regular expression. For string matching without regular expressions, use CONTAINS instead of REGEXP_MATCH.
Example:
#legacySQL SELECT word, COUNT(word) AS count FROM [bigquery-public-data:samples.shakespeare] WHERE (REGEXP_MATCH(word,r'\w\w\'\w\w')) GROUP BY word ORDER BY count DESC LIMIT 3;
Returns:
+-------+-------+ | word | count | +-------+-------+ | ne'er | 42 | | we'll | 35 | | We'll | 33 | +-------+-------+
REGEXP_EXTRACT('str', 'reg_exp')
Returns the portion of str that matches the capturing group within the regular expression.
Example:
#legacySQL SELECT REGEXP_EXTRACT(word,r'(\w\w\'\w\w)') AS fragment FROM [bigquery-public-data:samples.shakespeare] GROUP BY fragment ORDER BY fragment LIMIT 3;
Returns:
+----------+ | fragment | +----------+ | NULL | | Al'ce | | As'es | +----------+
REGEXP_REPLACE('orig_str', 'reg_exp', 'replace_str')
Returns a string where any substring of orig_str that matches reg_exp is replaced with replace_str. For example, REGEXP_REPLACE ('Hello', 'lo', 'p') returns Help.
Example:
#legacySQL SELECT REGEXP_REPLACE(word, r'ne\'er', 'never') AS expanded_word FROM [bigquery-public-data:samples.shakespeare] WHERE REGEXP_MATCH(word, r'ne\'er') GROUP BY expanded_word ORDER BY expanded_word LIMIT 5;
Returns:
+---------------+ | expanded_word | +---------------+ | Whenever | | never | | nevertheless | | whenever | +---------------+
Advanced examples
-
Filter result set by regular expression match
BigQuery's regular expression functions can be used to filter results in a
WHERE
clause, as well as to display results in theSELECT
. The following example combines both of these regular expression use cases into a single query.Example:
#legacySQL SELECT /* Replace white spaces in the title with underscores. */ REGEXP_REPLACE(title, r'\s+', '_') AS regexp_title, revisions FROM (SELECT title, COUNT(revision_id) as revisions FROM [bigquery-public-data:samples.wikipedia] WHERE wp_namespace=0 /* Match titles that start with 'G', end with * 'e', and contain at least two 'o's. */ AND REGEXP_MATCH(title, r'^G.*o.*o.*e$') GROUP BY title ORDER BY revisions DESC LIMIT 100);
-
Using regular expressions on integer or float data
While BigQuery's regular expression functions only work for string data, it's possible to use the
STRING()
function to cast integer or float data into string format. In this example,STRING()
is used to cast the integer valuecorpus_date
to a string, which is then altered byREGEXP_REPLACE
.Example:
#legacySQL SELECT corpus_date, /* Cast the corpus_date to a string value */ REGEXP_REPLACE(STRING(corpus_date), '^16', 'Written in the sixteen hundreds, in the year \'' ) AS date_string FROM [bigquery-public-data:samples.shakespeare] /* Cast the corpus_date to string, */ /* match values that begin with '16' */ WHERE REGEXP_MATCH(STRING(corpus_date), '^16') GROUP BY corpus_date, date_string ORDER BY date_string DESC LIMIT 5;
String functions
String functions operate on string data. String constants must be enclosed
with single or double quotes. String functions are case-sensitive by default.
You can append IGNORE CASE
to the end of a query to enable case-
insensitive matching. IGNORE CASE
works only on ASCII characters
and only at the top level of the query.
Wildcards are not supported in these functions; for regular expression functionality, use regular expression functions.
Syntax
String functions | |
---|---|
CONCAT() |
Returns the concatenation of two or more strings, or NULL if any of the values are NULL. |
expr CONTAINS 'str' |
Returns true if expr contains the specified string argument. |
INSTR() |
Returns the one-based index of the first occurrence of a string. |
LEFT() |
Returns the leftmost characters of a string. |
LENGTH() |
Returns the length of the string. |
LOWER() |
Returns the original string with all characters in lower case. |
LPAD() |
Inserts characters to the left of a string. |
LTRIM() |
Removes characters from the left side of a string. |
REPLACE() |
Replaces all occurrences of a substring. |
RIGHT() |
Returns the rightmost characters of a string. |
RPAD() |
Inserts characters to the right side of a string. |
RTRIM() |
Removes trailing characters from the right side of a string. |
SPLIT() |
Splits a string into repeated substrings. |
SUBSTR() |
Returns a substring ... |
UPPER() |
Returns the original string with all characters in upper case. |
CONCAT('str1', 'str2', '...')
str1 + str2 + ...- Returns the concatenation of two or more strings, or NULL if any of the values are NULL. Example: if
str1
isJava
andstr2
isScript
,CONCAT
returnsJavaScript
. expr CONTAINS 'str'
- Returns
true
ifexpr
contains the specified string argument. This is a case-sensitive comparison. INSTR('str1', 'str2')
- Returns the one-based index of the first occurrence of str2 in str1, or returns 0 if str2 does not occur in str1.
LEFT('str', numeric_expr)
- Returns the leftmost numeric_expr characters of
str
. If the number is longer than str, the full string will be returned. Example:LEFT('seattle', 3)
returnssea
. LENGTH('str')
- Returns a numerical value for the length of the string. Example: if
str
is'123456'
,LENGTH
returns6
. LOWER('str')
- Returns the original string with all characters in lower case.
LPAD('str1', numeric_expr, 'str2')
- Pads
str1
on the left withstr2
, repeatingstr2
until the result string is exactlynumeric_expr
characters. Example:LPAD('1', 7, '?')
returns??????1
. LTRIM('str1' [, str2])
-
Removes characters from the left side of str1. If str2 is omitted,
LTRIM
removes spaces from the left side of str1. Otherwise,LTRIM
removes any characters in str2 from the left side of str1 (case-sensitive).Examples:
SELECT LTRIM("Say hello", "yaS")
returns" hello"
.SELECT LTRIM("Say hello", " ySa")
returns"hello"
. REPLACE('str1', 'str2', 'str3')
-
Replaces all instances of str2 within str1 with str3.
RIGHT('str', numeric_expr)
- Returns the rightmost numeric_expr characters of
str
. If the number is longer than the string, it will return the whole string. Example:RIGHT('kirkland', 4)
returnsland
. RPAD('str1', numeric_expr, 'str2')
- Pads
str1
on the right withstr2
, repeatingstr2
until the result string is exactlynumeric_expr
characters. Example:RPAD('1', 7, '?')
returns1??????
. RTRIM('str1' [, str2])
-
Removes trailing characters from the right side of str1. If str2 is omitted,
RTRIM
removes trailing spaces from str1. Otherwise,RTRIM
removes any characters in str2 from the right side of str1 (case-sensitive).Examples:
SELECT RTRIM("Say hello", "leo")
returns"Say h"
.SELECT RTRIM("Say hello ", " hloe")
returns"Say"
. SPLIT('str' [, 'delimiter'])
- Splits a string into repeated substrings. If
delimiter
is specified, theSPLIT
function breaksstr
into substrings, usingdelimiter
as the delimiter. SUBSTR('str', index [, max_len])
- Returns a substring of
str
, starting atindex
. If the optionalmax_len
parameter is used, the returned string is a maximum ofmax_len
characters long. Counting starts at 1, so the first character in the string is in position 1 (not zero). Ifindex
is5
, the substring begins with the 5th character from the left instr
. Ifindex
is-4
, the substring begins with the 4th character from the right instr
. Example:SUBSTR('awesome', -4, 4)
returns the substringsome
. UPPER('str')
- Returns the original string with all characters in upper case.
Escaping special characters in strings
To escape special characters, use one of the following methods:
- Use
'\xDD'
notation, where'\x'
is followed by the two-digit hex representation of the character. - Use an escaping slash in front of slashes, single quotes, and double quotes.
- Use C-style sequences (
'\a', '\b', '\f', '\n', '\r', '\t',
and'\v'
) for other characters.
Some examples of escaping:
'this is a space: \x20'
'this string has \'single quote\' inside it'
'first line \n second line'
"double quotes are also ok"
'\070' -> ERROR: octal escaping is not supported
Table wildcard functions
Table wildcard functions are a convenient way to query data from a specific set of tables. A table wildcard function is equivalent to a comma-separated union of all the tables matched by the wildcard function. When you use a table wildcard function, BigQuery only accesses and charges you for tables that match the wildcard. Table wildcard functions are specified in the query's FROM clause.
If you use table wildcard functions in a query, the functions no longer need to be contained in parentheses. For example, some of the following examples use parentheses, whereas others don't.
Cached results are not supported for queries against multiple tables using a wildcard function (even if the Use Cached Results option is checked). If you run the same wildcard query multiple times, you are billed for each query.
Syntax
Table wildcard functions | |
---|---|
TABLE_DATE_RANGE() |
Queries multiple daily tables that span a date range. |
TABLE_DATE_RANGE_STRICT() |
Queries multiple daily tables that span a date range, with no missing dates. |
TABLE_QUERY() |
Queries tables whose names match a specified predicate. |
TABLE_DATE_RANGE(prefix, timestamp1, timestamp2)
-
Queries daily tables that overlap with the time range between
<timestamp1>
and<timestamp2>
.Table names must have the following format:
<prefix><day>
, where<day>
is in the formatYYYYMMDD
.You can use date and time functions to generate the timestamp parameters. For example:
TIMESTAMP('2012-10-01 02:03:04')
DATE_ADD(CURRENT_TIMESTAMP(), -7, 'DAY')
Example: get tables between two days
This example assumes the following tables exist:
- mydata.people20140325
- mydata.people20140326
- mydata.people20140327
#legacySQL SELECT name FROM TABLE_DATE_RANGE([myproject-1234:mydata.people], TIMESTAMP('2014-03-25'), TIMESTAMP('2014-03-27')) WHERE age >= 35
Matches the following tables:
- mydata.people20140325
- mydata.people20140326
- mydata.people20140327
Example: get tables in a two-day range up to "now"
This example assumes the following tables exist in a project named
myproject-1234
:- mydata.people20140323
- mydata.people20140324
- mydata.people20140325
#legacySQL SELECT name FROM (TABLE_DATE_RANGE([myproject-1234:mydata.people], DATE_ADD(CURRENT_TIMESTAMP(), -2, 'DAY'), CURRENT_TIMESTAMP())) WHERE age >= 35
Matches the following tables:
- mydata.people20140323
- mydata.people20140324
- mydata.people20140325
TABLE_DATE_RANGE_STRICT(prefix, timestamp1, timestamp2)
-
This function is equivalent to
TABLE_DATE_RANGE
. The only difference is that if any daily table is missing in the sequence,TABLE_DATE_RANGE_STRICT
fails and returns aNot Found: Table <table_name>
error.Example: error on missing table
This example assumes the following tables exist:
- people20140325
- people20140327
#legacySQL SELECT name FROM (TABLE_DATE_RANGE_STRICT([myproject-1234:mydata.people], TIMESTAMP('2014-03-25'), TIMESTAMP('2014-03-27'))) WHERE age >= 35
The above example returns an error "Not Found" for the table "people20140326".
TABLE_QUERY(dataset, expr)
-
Queries tables whose names match the supplied
expr
. Theexpr
parameter must be represented as a string and must contain an expression to evaluate. For example,'length(table_id) < 3'
.Example: match tables whose names contain "oo" and have a length greater than 4
This example assumes the following tables exist:
- mydata.boo
- mydata.fork
- mydata.ooze
- mydata.spoon
#legacySQL SELECT speed FROM (TABLE_QUERY([myproject-1234:mydata], 'table_id CONTAINS "oo" AND length(table_id) >= 4'))
Matches the following tables:
- mydata.ooze
- mydata.spoon
Example: match tables whose names start with "boo", followed by 3-5 numeric digits
This example assumes the following tables exist in a project named
myproject-1234
:- mydata.book4
- mydata.book418
- mydata.boom12345
- mydata.boom123456789
- mydata.taboo999
#legacySQL SELECT speed FROM TABLE_QUERY([myproject-1234:mydata], 'REGEXP_MATCH(table_id, r"^boo[\d]{3,5}")')
Matches the following tables:
- mydata.book418
- mydata.boom12345
URL functions
Syntax
URL functions | |
---|---|
HOST() |
Given a URL, returns the host name as a string. |
DOMAIN() |
Given a URL, returns the domain as a string. |
TLD() |
Given a URL, returns the top level domain plus any country domain in the URL. |
HOST('url_str')
- Given a URL, returns the host name as a string. Example: HOST('http://www.google.com:80/index.html') returns 'www.google.com'
DOMAIN('url_str')
- Given a URL, returns the domain as a string. Example: DOMAIN('http://www.google.com:80/index.html') returns 'google.com'.
TLD('url_str')
- Given a URL, returns the top level domain plus any country domain in the URL. Example: TLD('http://www.google.com:80/index.html') returns '.com'. TLD('http://www.google.co.uk:80/index.html') returns '.co.uk'.
Notes:
- These functions don't perform reverse DNS lookup, so if you call these functions using an IP address the functions will return segments of the IP address rather than segments of the host name.
- All of the URL parsing functions expect lower-case characters. Upper-case characters in the URL will result in a NULL or otherwise incorrect result. Consider passing input to this function through LOWER() if your data has mixed casing.
Advanced example
Parse domain names from URL data
This query uses the
DOMAIN()
function to return the most popular domains listed as repository homepages on GitHub. Note the
use of HAVING to filter records using the result of the DOMAIN()
function. This
is a useful function to determine referrer information from URL data.
Examples:
#legacySQL SELECT DOMAIN(repository_homepage) AS user_domain, COUNT(*) AS activity_count FROM [bigquery-public-data:samples.github_timeline] GROUP BY user_domain HAVING user_domain IS NOT NULL AND user_domain != '' ORDER BY activity_count DESC LIMIT 5;
Returns:
+-----------------+----------------+ | user_domain | activity_count | +-----------------+----------------+ | github.com | 281879 | | google.com | 34769 | | khanacademy.org | 17316 | | sourceforge.net | 15103 | | mozilla.org | 14091 | +-----------------+----------------+
To look specifically at TLD information, use the TLD()
function. This
example displays the top TLDs that are not in a list of common examples.
#legacySQL SELECT TLD(repository_homepage) AS user_tld, COUNT(*) AS activity_count FROM [bigquery-public-data:samples.github_timeline] GROUP BY user_tld HAVING /* Only consider TLDs that are NOT NULL */ /* or in our list of common TLDs */ user_tld IS NOT NULL AND NOT user_tld IN ('','.com','.net','.org','.info','.edu') ORDER BY activity_count DESC LIMIT 5;
Returns:
+----------+----------------+ | user_tld | activity_count | +----------+----------------+ | .de | 22934 | | .io | 17528 | | .me | 13652 | | .fr | 12895 | | .co.uk | 9135 | +----------+----------------+
Window functions
Window functions, also known as analytic functions, enable calculations on a specific subset, or "window", of a result set. Window functions make it easier to create reports that include complex analytics such as trailing averages and running totals.
Each window function requires an OVER
clause that specifies
the window top and bottom. The three components of the OVER
clause (partitioning, ordering, and framing) provide additional control
over the window. Partitioning enables you to divide the input data into
logical groups that have a common characteristic. Ordering enables you
to order the results within a partition. Framing enables
you to create a sliding window frame within a partition that moves
relative to the current row. You can configure the size of the moving window frame
based on a number of rows or a range of values, such as a time interval.
#legacySQL SELECT <window_function> OVER ( [PARTITION BY <expr>] [ORDER BY <expr> [ASC | DESC]] [<window-frame-clause>] )
PARTITION BY
- Defines the base partition over which this function operates.
Specify one or more comma-separated column names; one partition will be
created for each distinct set of values for these columns, similar
to a
GROUP BY
clause. IfPARTITION BY
is omitted, the base partition is all rows in the input to the window function. - The
PARTITION BY
clause also allows window functions to partition data and parallelize execution. If you wish to use a window function withallowLargeResults
, or if you intend to apply further joins or aggregations to the output of your window function, usePARTITION BY
to parallelize execution. JOIN EACH
andGROUP EACH BY
clauses can't be used on the output of window functions. To generate large query results when using window functions, you must usePARTITION BY
.ORDER BY
- Sorts the partition. If
ORDER BY
is absent, there is no guarantee of any default sorting order. Sorting occurs at the partition level, before any window frame clause is applied. If you specify aRANGE
window, you should add anORDER BY
clause. Default order isASC
. ORDER BY
is optional in some cases, but certain window functions, such as rank() or dense_rank(), require the clause.- If you use
ORDER BY
without specifyingROWS
orRANGE
,ORDER BY
implies that the window extends from the beginning of the partition to the current row. In the absence of anORDER BY
clause, the window is the entire partition. <window-frame-clause>
-
{ROWS | RANGE} {BETWEEN <start> AND <end> | <start> | <end>}
- A subset of the partition over which to operate. This can be the same
size as the partition or smaller. If you use
ORDER BY
without awindow-frame-clause
, the default window frame isRANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
. If you omit bothORDER BY
and thewindow-frame-clause
, the default window frame is the entire partition.ROWS
- Defines a window in terms of row position, relative to the current row. For example, to add a column showing the sum of the preceding 5 rows of salary values, you would querySUM(salary) OVER (ROWS BETWEEN 5 PRECEDING AND CURRENT ROW)
. The set of rows typically includes the current row, but that is not required.RANGE
- Defines a window in terms of a range of values in a given column, relative to that column's value in the current row. Only operates on numbers and dates, where date values are simple integers (microseconds since the epoch). Neighboring rows with the same value are called peer rows. Peer rows of theCURRENT ROW
are included in a window frame that specifiesCURRENT ROW
. For example, if you specify the window end to beCURRENT ROW
and the following row in the window has the same value, it will be included in the function calculation.BETWEEN <start> AND <end>
- A range, inclusive of the start and end rows. The range need not include the current row, but<start>
must precede or equal<end>
.<start>
- Specifies the start offset for this window, relative to the current row. The following options are supported: where{UNBOUNDED PRECEDING | CURRENT ROW | <expr> PRECEDING | <expr> FOLLOWING}
<expr>
is a positive integer,PRECEDING
indicates a preceding row number or range value, andFOLLOWING
indicates a following row number or range value.UNBOUNDED PRECEDING
means the first row of the partition. If the start precedes the window, it will be set to the first row of the partition.<end>
- Specifies the end offset for this window, relative to the current row. The following options are supported: where{UNBOUNDED FOLLOWING | CURRENT ROW | <expr> PRECEDING | <expr> FOLLOWING}
<expr>
is a positive integer,PRECEDING
indicates a preceding row number or range value, andFOLLOWING
indicates a following row number or range value.UNBOUNDED FOLLOWING
means the last row of the partition. If end is beyond the end of the window, it will be set to the last row of the partition.
Unlike aggregation functions, which collapse many input rows into one
output row, window functions return one row of output for each row of input.
This feature makes it easier to create queries that calculate running totals
and moving averages. For example, the following query returns a running total
for a small dataset of five rows defined by SELECT
statements:
#legacySQL SELECT name, value, SUM(value) OVER (ORDER BY value) AS RunningTotal FROM (SELECT "a" AS name, 0 AS value), (SELECT "b" AS name, 1 AS value), (SELECT "c" AS name, 2 AS value), (SELECT "d" AS name, 3 AS value), (SELECT "e" AS name, 4 AS value);
Return value:
+------+-------+--------------+ | name | value | RunningTotal | +------+-------+--------------+ | a | 0 | 0 | | b | 1 | 1 | | c | 2 | 3 | | d | 3 | 6 | | e | 4 | 10 | +------+-------+--------------+
The following example calculates a moving average of the values in the current row and the row preceding it. The window frame comprises two rows that move with the current row.
#legacySQL SELECT name, value, AVG(value) OVER (ORDER BY value ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS MovingAverage FROM (SELECT "a" AS name, 0 AS value), (SELECT "b" AS name, 1 AS value), (SELECT "c" AS name, 2 AS value), (SELECT "d" AS name, 3 AS value), (SELECT "e" AS name, 4 AS value);
Return value:
+------+-------+---------------+ | name | value | MovingAverage | +------+-------+---------------+ | a | 0 | 0.0 | | b | 1 | 0.5 | | c | 2 | 1.5 | | d | 3 | 2.5 | | e | 4 | 3.5 | +------+-------+---------------+
Syntax
Window functions | |
---|---|
AVG() COUNT(*) COUNT([DISTINCT]) MAX() MIN() STDDEV() SUM() |
The same operation as the corresponding Aggregate functions, but are computed over a window defined by the OVER clause. |
CUME_DIST() |
Returns a double that indicates the cumulative distribution of a value in a group of values ... |
DENSE_RANK() |
Returns the integer rank of a value in a group of values. |
FIRST_VALUE() |
Returns the first value of the specified field in the window. |
LAG() |
Enables you to read data from a previous row within a window. |
LAST_VALUE() |
Returns the last value of the specified field in the window. |
LEAD() |
Enables you to read data from a following row within a window. |
NTH_VALUE() |
Returns the value of <expr> at position
<n> of the window frame ...
|
NTILE() |
Divides the window into the specified number of buckets. |
PERCENT_RANK() |
Returns the rank of the current row, relative to the other rows in the partition. |
PERCENTILE_CONT() |
Returns an interpolated value that would map to the percentile argument with respect to the window ... |
PERCENTILE_DISC() |
Returns the value nearest the percentile of the argument over the window. |
RANK() |
Returns the integer rank of a value in a group of values. |
RATIO_TO_REPORT() |
Returns the ratio of each value to the sum of the values. |
ROW_NUMBER() |
Returns the current row number of the query result over the window. |
-
AVG(numeric_expr)
COUNT(*)
COUNT([DISTINCT] field)
MAX(field)
MIN(field)
STDDEV(numeric_expr)
SUM(field)
-
These window functions perform the same operation as the corresponding
Aggregate functions, but are computed
over a window defined by the OVER clause.
Another significant difference is that the
COUNT([DISTINCT] field)
function produces exact results when used as a window function, with behavior similar to theEXACT_COUNT_DISTINCT()
aggregate function.In the example query, the
ORDER BY
clause causes the window to be computed from the start of the partition to the current row, which generates a cumulative sum for that year.#legacySQL SELECT corpus_date, corpus, word_count, SUM(word_count) OVER ( PARTITION BY corpus_date ORDER BY word_count) annual_total FROM [bigquery-public-data:samples.shakespeare] WHERE word='love' ORDER BY corpus_date, word_count
Returns:
corpus_date corpus word_count annual_total 0 various 37 37 0 sonnets 157 194 1590 2kinghenryvi 18 18 1590 1kinghenryvi 24 42 1590 3kinghenryvi 40 82 CUME_DIST()
-
Returns a double that indicates the cumulative distribution of a value in a group of values, calculated using the formula
<number of rows preceding or tied with the current row> / <total rows>
. Tied values return the same cumulative distribution value.This window function requires
ORDER BY
in theOVER
clause.#legacySQL SELECT word, word_count, CUME_DIST() OVER (PARTITION BY corpus ORDER BY word_count DESC) cume_dist, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 5
Returns:
word word_count cume_dist handkerchief 29 0.2 satisfaction 5 0.4 displeasure 4 0.8 instruments 4 0.8 circumstance 3 1.0 DENSE_RANK()
-
Returns the integer rank of a value in a group of values. The rank is calculated based on comparisons with other values in the group.
Tied values display as the same rank. The rank of the next value is incremented by 1. For example, if two values tie for rank 2, the next ranked value is 3. If you prefer a gap in the ranking list, use rank().
This window function requires
ORDER BY
in theOVER
clause. Returns:#legacySQL SELECT word, word_count, DENSE_RANK() OVER (PARTITION BY corpus ORDER BY word_count DESC) dense_rank, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 5
word word_count dense_rank handkerchief 29 1 satisfaction 5 2 displeasure 4 3 instruments 4 3 circumstance 3 4 FIRST_VALUE(<field_name>)
-
Returns the first value of
<field_name>
in the window. Returns:#legacySQL SELECT word, word_count, FIRST_VALUE(word) OVER (PARTITION BY corpus ORDER BY word_count DESC) fv, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 1
word word_count fv imperfectly 1 imperfectly LAG(<expr>[, <offset>[, <default_value>]])
-
Enables you to read data from a previous row within a window. Specifically,
LAG()
returns the value of<expr>
for the row located<offset>
rows before the current row. If the row doesn't exist,<default_value>
returns.#legacySQL SELECT word, word_count, LAG(word, 1) OVER (PARTITION BY corpus ORDER BY word_count DESC) lag, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 5
Returns:
word word_count lag handkerchief 29 null satisfaction 5 handkerchief displeasure 4 satisfaction instruments 4 displeasure circumstance 3 instruments LAST_VALUE(<field_name>)
-
Returns the last value of
<field_name>
in the window.#legacySQL SELECT word, word_count, LAST_VALUE(word) OVER (PARTITION BY corpus ORDER BY word_count DESC) lv, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 1
Returns:
word word_count lv imperfectly 1 imperfectly LEAD(<expr>[, <offset>[, <default_value>]])
-
Enables you to read data from a following row within a window. Specifically,
LEAD()
returns the value of<expr>
for the row located<offset>
rows after the current row. If the row doesn't exist,<default_value>
returns. Returns:#legacySQL SELECT word, word_count, LEAD(word, 1) OVER (PARTITION BY corpus ORDER BY word_count DESC) lead, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 5
word word_count lead handkerchief 29 satisfaction satisfaction 5 displeasure displeasure 4 instruments instruments 4 circumstance circumstance 3 null NTH_VALUE(<expr>, <n>)
-
Returns the value of
<expr>
at position<n>
of the window frame, where<n>
is a one-based index. NTILE(<num_buckets>)
-
Divides a sequence of rows into
<num_buckets>
buckets and assigns a corresponding bucket number, as an integer, with each row. Thentile()
function assigns the bucket numbers as equally as possible and returns a value from 1 to<num_buckets>
for each row. Returns:#legacySQL SELECT word, word_count, NTILE(2) OVER (PARTITION BY corpus ORDER BY word_count DESC) ntile, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 5
word word_count ntile handkerchief 29 1 satisfaction 5 1 displeasure 4 1 instruments 4 2 circumstance 3 2 PERCENT_RANK()
-
Returns the rank of the current row, relative to the other rows in the partition. Returned values range between 0 and 1, inclusively. The first value returned is 0.0.
This window function requires
ORDER BY
in theOVER
clause. Returns:#legacySQL SELECT word, word_count, PERCENT_RANK() OVER (PARTITION BY corpus ORDER BY word_count DESC) p_rank, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 5
word word_count p_rank handkerchief 29 0.0 satisfaction 5 0.25 displeasure 4 0.5 instruments 4 0.5 circumstance 3 1.0 PERCENTILE_CONT(<percentile>)
-
Returns an interpolated value that would map to the percentile argument with respect to the window, after ordering them per the
ORDER BY
clause.<percentile>
must be between 0 and 1.This window function requires
ORDER BY
in theOVER
clause. Returns:#legacySQL SELECT word, word_count, PERCENTILE_CONT(0.5) OVER (PARTITION BY corpus ORDER BY word_count DESC) p_cont, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 5
word word_count p_cont handkerchief 29 4 satisfaction 5 4 displeasure 4 4 instruments 4 4 circumstance 3 4 PERCENTILE_DISC(<percentile>)
-
Returns the value nearest the percentile of the argument over the window.
<percentile>
must be between 0 and 1.This window function requires
ORDER BY
in theOVER
clause. Returns:#legacySQL SELECT word, word_count, PERCENTILE_DISC(0.5) OVER (PARTITION BY corpus ORDER BY word_count DESC) p_disc, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 5
word word_count p_disc handkerchief 29 4 satisfaction 5 4 displeasure 4 4 instruments 4 4 circumstance 3 4 RANK()
-
Returns the integer rank of a value in a group of values. The rank is calculated based on comparisons with other values in the group.
Tied values display as the same rank. The rank of the next value is incremented according to how many tied values occurred before it. For example, if two values tie for rank 2, the next ranked value is 4, not 3. If you prefer no gaps in the ranking list, use dense_rank().
This window function requires
ORDER BY
in theOVER
clause. Returns:#legacySQL SELECT word, word_count, RANK() OVER (PARTITION BY corpus ORDER BY word_count DESC) rank, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 5
word word_count rank handkerchief 29 1 satisfaction 5 2 displeasure 4 3 instruments 4 3 circumstance 3 5 RATIO_TO_REPORT(<column>)
-
Returns the ratio of each value to the sum of the values, as a double between 0 and 1.
Returns:#legacySQL SELECT word, word_count, RATIO_TO_REPORT(word_count) OVER (PARTITION BY corpus ORDER BY word_count DESC) r_to_r, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 5
word word_count r_to_r handkerchief 29 0.6444444444444445 satisfaction 5 0.1111111111111111 displeasure 4 0.08888888888888889 instruments 4 0.08888888888888889 circumstance 3 0.06666666666666667 ROW_NUMBER()
-
Returns the current row number of the query result over the window, starting with 1.
Returns:#legacySQL SELECT word, word_count, ROW_NUMBER() OVER (PARTITION BY corpus ORDER BY word_count DESC) row_num, FROM [bigquery-public-data:samples.shakespeare] WHERE corpus='othello' and length(word) > 10 LIMIT 5
word word_count row_num handkerchief 29 1 satisfaction 5 2 displeasure 4 3 instruments 4 4 circumstance 3 5
Other functions
Syntax
Other functions | |
---|---|
CASE WHEN ... THEN |
Use CASE to choose among two or more alternate expressions in your query. |
CURRENT_USER() |
Returns the email address of the user running the query. |
EVERY() |
Returns true if the argument is true for all of its inputs. |
FROM_BASE64() |
Converts the base-64 encoded input string into BYTES format. |
HASH() |
Computes and returns a 64-bit signed hash value ... |
FARM_FINGERPRINT() |
Computes and returns a 64-bit signed fingerprint value ... |
IF() |
If first argument is true, returns second argument; otherwise returns third argument. |
POSITION() |
Returns the one-based, sequential position of the argument. |
SHA1() |
Returns a SHA1 hash, in BYTES format. |
SOME() |
Returns true if argument is true for at least one of its inputs. |
TO_BASE64() |
Converts the BYTES argument to a base-64 encoded string. |
CASE WHEN when_expr1 THEN then_expr1
WHEN when_expr2 THEN then_expr2 ...
ELSE else_expr END- Use CASE to choose among two or more alternate expressions in your query. WHEN expressions must be boolean, and all the expressions in THEN clauses and ELSE clause must be compatible types.
CURRENT_USER()
- Returns the email address of the user running the query.
EVERY(<condition>)
- Returns
true
ifcondition
is true for all of its inputs. When used with theOMIT IF
clause, this function is useful for queries that involve repeated fields. FROM_BASE64(<str>)
- Converts the base64-encoded input string
str
into BYTES format. To convert BYTES to a base64-encoded string, use TO_BASE64(). HASH(expr)
- Computes and returns a 64-bit signed hash value of the bytes of
expr
as defined by the CityHash library (version 1.0.3). Any string or integer expression is supported and the function respectsIGNORE CASE
for strings, returning case invariant values. FARM_FINGERPRINT(expr)
- Computes and returns a 64-bit signed fingerprint value of the
STRING
orBYTES
input using theFingerprint64
function from the open-source FarmHash library. The output of this function for a particular input will never change and matches the output of theFARM_FINGERPRINT
function when using GoogleSQL. RespectsIGNORE CASE
for strings, returning case invariant values. IF(condition, true_return, false_return)
- Returns either
true_return
orfalse_return
, depending on whethercondition
is true or false. The return values can be literals or field-derived values, but they must be the same data type. Field-derived values do not need to be included in theSELECT
clause. POSITION(field)
- Returns the one-based, sequential position of field within a set of repeated fields.
SHA1(<str>)
- Returns a SHA1 hash, in BYTES format, of the input string
str
. You can convert the result to base64 using TO_BASE64(). For example:#legacySQL SELECT TO_BASE64(SHA1(corpus)) FROM [bigquery-public-data:samples.shakespeare] LIMIT 100;
SOME(<condition>)
- Returns
true
ifcondition
is true for at least one of its inputs. When used with theOMIT IF
clause, this function is useful for queries that involve repeated fields. TO_BASE64(<bin_data>)
- Converts the BYTES input
bin_data
to a base64-encoded string. For example: To convert a base64-encoded string to BYTES, use FROM_BASE64().#legacySQL SELECT TO_BASE64(SHA1(title)) FROM [bigquery-public-data:samples.wikipedia] LIMIT 100;
Advanced examples
-
Bucketing results into categories using conditionals
The following query uses a
CASE/WHEN
block to bucket results into "region" categories based on a list of states. If the state does not appear as an option in one of theWHEN
statements, the state value will default to "None."Example:
#legacySQL SELECT CASE WHEN state IN ('WA', 'OR', 'CA', 'AK', 'HI', 'ID', 'MT', 'WY', 'NV', 'UT', 'CO', 'AZ', 'NM') THEN 'West' WHEN state IN ('OK', 'TX', 'AR', 'LA', 'TN', 'MS', 'AL', 'KY', 'GA', 'FL', 'SC', 'NC', 'VA', 'WV', 'MD', 'DC', 'DE') THEN 'South' WHEN state IN ('ND', 'SD', 'NE', 'KS', 'MN', 'IA', 'MO', 'WI', 'IL', 'IN', 'MI', 'OH') THEN 'Midwest' WHEN state IN ('NY', 'PA', 'NJ', 'CT', 'RI', 'MA', 'VT', 'NH', 'ME') THEN 'Northeast' ELSE 'None' END as region, average_mother_age, average_father_age, state, year FROM (SELECT year, state, SUM(mother_age)/COUNT(mother_age) as average_mother_age, SUM(father_age)/COUNT(father_age) as average_father_age FROM [bigquery-public-data:samples.natality] WHERE father_age < 99 GROUP BY year, state) ORDER BY year LIMIT 5;
Returns:
+--------+--------------------+--------------------+-------+------+ | region | average_mother_age | average_father_age | state | year | +--------+--------------------+--------------------+-------+------+ | South | 24.342600163532296 | 27.683769419460344 | AR | 1969 | | West | 25.185041908446163 | 28.268214055448098 | AK | 1969 | | West | 24.780776677578217 | 27.831181063905248 | CA | 1969 | | West | 25.005834769924412 | 27.942978384829598 | AZ | 1969 | | South | 24.541730952905738 | 27.686430093306885 | AL | 1969 | +--------+--------------------+--------------------+-------+------+
-
Simulating a Pivot Table
Use conditional statements to organize the results of a subselect query into rows and columns. In the example below, results from a search for most revised Wikipedia articles that start with the value 'Google' are organized into columns where the revision counts are displayed if they meet various criteria.
Example:
#legacySQL SELECT page_title, /* Populate these columns as True or False, */ /* depending on the condition */ IF (page_title CONTAINS 'search', INTEGER(total), 0) AS search, IF (page_title CONTAINS 'Earth' OR page_title CONTAINS 'Maps', INTEGER(total), 0) AS geo, FROM /* Subselect to return top revised Wikipedia articles */ /* containing 'Google', followed by additional text. */ (SELECT TOP (title, 5) as page_title, COUNT (*) as total FROM [bigquery-public-data:samples.wikipedia] WHERE REGEXP_MATCH (title, r'^Google.+') AND wp_namespace = 0 );
Returns:
+---------------+--------+------+ | page_title | search | geo | +---------------+--------+------+ | Google search | 4261 | 0 | | Google Earth | 0 | 3874 | | Google Chrome | 0 | 0 | | Google Maps | 0 | 2617 | | Google bomb | 0 | 0 | +---------------+--------+------+
-
Using HASH to select a random sample of your data
Some queries can provide a useful result using random subsampling of the result set. To retrieve a random sampling of values, use the
HASH
function to return results in which the modulo "n" of the hash equals zero.For example, the following query will find the
HASH()
of the "title" value, and then checks if that value modulo "2" is zero. This should result in about 50% of the values being labeled as "sampled." To sample fewer values, increase the value of the modulo operation from "2" to something larger. The query uses theABS
function in combination withHASH
, becauseHASH
can return negative values, and the modulo operator on a negative value yields a negative value.Example:
#legacySQL SELECT title, HASH(title) AS hash_value, IF(ABS(HASH(title)) % 2 == 1, 'True', 'False') AS included_in_sample FROM [bigquery-public-data:samples.wikipedia] WHERE wp_namespace = 0 LIMIT 5;