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• Supported functions and operators
  • Aggregate functions
  • Arithmetic operators
  • Bitwise functions
  • Casting functions
  • Comparison functions
  • Date and time functions
  • IP functions
  • JSON functions
  • Logical operators
  • Mathematical functions
  • Regular expression functions
  • String functions
  • Table wildcard functions
  • URL functions
  • Window functions
  • Other functions

Query syntax

All BigQuery queries are SELECT statements of this form:

SELECT expr1 [[AS] alias1] [, expr2 [[AS] alias2], ...]
    [agg_function(expr3) WITHIN expr4]
    [FROM [(FLATTEN(table_name1|(subselect1)] [, table_name2|(subselect2), ...)]
    [[INNER|LEFT OUTER|CROSS] JOIN [EACH] table_2|(subselect2) [[AS] tablealias2]
      ON join_condition_1 [... AND join_condition_N ...]]+
    [WHERE condition]
    [GROUP [EACH] BY field1|alias1 [, field2|alias2, ...]]
    [HAVING condition]
    [ORDER BY field1|alias1 [DESC|ASC] [, field2|alias2 [DESC|ASC], ...]]
    [LIMIT n]
    ;

Note: Keywords are not case-sensitive. In this document, keywords such as SELECT are capitalized for illustration purposes.

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SELECT clause

SELECT expr1 [AS alias1 ], expr2 [AS alias2 ], ...

The SELECT clause specifies the set of values to be returned by a query. Expressions ( expr1 , etc.) in the SELECT clause can be field names, literals, or functional expressions that operate on fields or literals. Expressions must be comma-separated.

The SELECT clause supports an AS section, which defines an alias for the field, literal, or functional expression. You can refer to an alias only in GROUP BY and ORDER BY clauses.

Notes:

• If you use an aggregation function on any result, such as COUNT , you must use the GROUP BY clause to group all non-aggregated fields. For example:

  SELECT word, corpus, COUNT(word)
    FROM publicdata:samples.shakespeare
    WHERE word CONTAINS "th" GROUP BY word, corpus; // Succeeds
  
  SELECT word, corpus, COUNT(word)
    FROM publicdata:samples.shakespeare
    WHERE word CONTAINS "th" GROUP BY word; // INVALID! Didn't group by corpus
  

Example
This example defines an alias for the sorting clause:

SELECT word, LENGTH(word) AS len
  FROM publicdata:samples.shakespeare
  WHERE word CONTAINS 'th' ORDER BY len;

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WITHIN clause

The WITHIN clause can be used as a SELECT field with the following syntax:

SELECT expr1 [WITHIN RECORD|node] [AS alias1], expr2 [WITHIN RECORD|node] [AS alias2], ... 

The WITHIN keyword specifically works with aggregate functions to aggregate across children and repeated fields within records and nested fields.

When you specify the WITHIN keyword, you need to specify the scope over which you want to aggregate:

• WITHIN RECORD : Aggregates data in the repeated values within the record, where a record represents an entire unique protocol buffer message, including children and repeated values.
• WITHIN node_name : Aggregates data in the repeated values within the specified node, where a node is a parent node of the field in the aggregation function.

For more information and examples, see WITHIN in the developer's guide.

FROM clause

...
FROM
  [project_name:]datasetId.tableId |
  (subselect_clause) |
  table wildcard function
[[AS] alias]
...

project_name is an optional name of the project that contains the dataset. If not specified, the query will assume it is in the current project. Note: if your project name includes a dash, you must surround the entire table reference with brackets, as in [my-dashed-project:dataset1.tableName] .

subselect_clause is a nested SELECT clause. The subquery is evaluated and subquery results are treated just like a table. The result of the subquery must contain the appropriate columns and data required by the containing SELECT statement. If the query includes multiple subqueries or a combination or subqueries and tables, all of the tables and subqueries must contain all of the fields in the SELECT clause of the main query. The rules that apply to queries also apply to subqueries, except that subqueries do not end in a semicolon.

table wildcard function refers to a supported table wildcard function , such as TABLE_DATE_RANGE to query only a specific set of daily tables.

alias is primarily used in JOIN statements, where you may need to provide a name to a subselect clause. You should only refer to a field as alias.field in order to disambiguate between the two tables involved in a JOIN . Do not use alias with a table wildcard function.

The table name must be qualified with the dataset and project IDs, unless you specify default dataset or project IDs as part of the query request.

Example

SELECT COUNT(*)
  FROM publicdata:samples.shakespeare
  WHERE word CONTAINS "th";

Note: Unlike many other SQL-based systems, BigQuery uses the comma syntax to indicate table unions, not joins. This means you can run a query over several tables with compatible schemas as follows:

// Find suspicious activity over several days
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 perform a union over large number of tables can expect to run more slowly than the same query over a single table with the same amount of data. The difference in performance can be up to 50 ms per additional table.

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FLATTEN clause

...(FLATTEN ([project_name:]datasetId.tableId, flattenField))..
...(FLATTEN (subselect_clause, flattenField))..

FLATTEN converts a repeated field into an optional field. Given one record with many values for a repeated field, FLATTEN unrolls it into many records, one record for each value of the (formerly) repeated field; any non-repeated fields become duplicated to fill out each of the new records formed. FLATTEN removes one level of nesting.

For more information and examples, see FLATTEN in the developer's guide.

JOIN clause

SELECT field_1 [..., field_n] FROM
   table_1 [[AS] alias_1]
 [[INNER|LEFT OUTER|CROSS] JOIN [EACH]
   table_2 [[AS] alias_2]
  [ON join_condition_1 [... AND join_condition_n]]
 ]+

BigQuery supports multiple JOIN operations in each SELECT statement. BigQuery executes multiple JOIN operations pairwise, starting with the first pair of inputs after the FROM keyword. 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 a JOIN key in the ON clause of a subsequent JOIN operation.

JOIN types

BigQuery supports INNER , LEFT OUTER and CROSS JOIN operations. The default is INNER .

CROSS JOIN clauses must not contain an ON clause. CROSS JOIN operations can return a large amount of data and might result in a slow and inefficient query. When possible, use regular JOIN instead.

EACH modifier

Normal JOIN operations require that the right-side table contains less than 8 MB of compressed data. The EACH modifier is a hint that informs the query execution engine that the JOIN might reference two large tables. The EACH modifier can't be used in CROSS JOIN clauses.

When possible, use JOIN without the EACH modifier for best performance. Use JOIN EACH when table sizes are too large for JOIN .

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WHERE clause

... WHERE condition ...

The WHERE clause, sometimes called the predicate, states the qualifying conditions for a query. 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 listed in any SELECT clause.

Note: Aggregate functions cannot be used in the WHERE clause. Use HAVING if you need to use aggregate fields.

Example
The following example includes two clauses joined by an OR; either one must be satisfied for the row to be returned.

select word FROM publicdata:samples.shakespeare
  WHERE
    (word CONTAINS 'prais' AND word CONTAINS 'ing') OR
    (word CONTAINS 'laugh' AND word CONTAINS 'ed');

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GROUP BY clause

... GROUP [EACH] BY field1 | alias1 , field2 | alias2 ...

The GROUP BY clause allows you to group rows that have the same values for a given field. You can then perform aggregate functions on each of the groups. Grouping occurs after any selection or aggregation in the SELECT clause.

For example you can group the rows that have the same value for f1 and find the SUM of the f2 values for each group:

SELECT f1, SUM(f2)
  FROM ds.Table
  GROUP BY f1;

This type of aggregation is called group aggregation . Unlike scoped aggregation , group aggregation is supported by traditional relational databases.

The EACH parameter can be used when your dataset contains a large number of distinct values for the group keys. Use GROUP BY without the EACH parameter when possible to improve query performance.

Notes:

• Non-aggregated fields in the SELECT clause must be listed in the GROUP BY clause:

  SELECT f1, COUNT(f2), f3 FROM ds.Table GROUP BY f1; // INVALID! f3 not in GROUP BY clause
  

• You cannot group by an aggregate value:

  SELECT f1, COUNT(f2) as count FROM ds.Table GROUP BY count; // INVALID! Cannot group by aggregate

• 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 of GROUP BY might solve the problem.

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HAVING clause

... HAVING condition ...

The HAVING clause states the qualifying conditions for a query. Multiple conditions can be joined by boolean AND and OR clauses, optionally surrounded by (parentheses) to group them. HAVING is similar to WHERE , but it supports aggregate fields.

Note that the HAVING clause can only refer to fields defined in your SELECT clause (if the field has an alias, you must use it; if it doesn't, use the aggregate field name instead).

Example

SELECT keyword, SUM(clicks)/SUM(impressions) ctr FROM ads
  WHERE impressions > 100
  GROUP BY keyword
  HAVING ctr > 0.1;

SELECT foo, SUM(bar) as boo FROM myTable GROUP BY foo HAVING boo>0;

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ORDER BY clause

... ORDER BY field1 | alias1 [DESC|ASC], field2 | alias2 [DESC|ASC] ...

The ORDER BY clause sorts the results of a query in ascending or descending order of one or more fields. Use DESC (descending) or ASC (ascending) to specify the sort direction. ASC is the default.

You can sort by field names or by aliases from the SELECT clause. 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.

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LIMIT clause

... LIMIT num_rows ...

The LIMIT clause limits the number of rows in the returned result set. For example, the following query returns only 5 results:

SELECT COUNT(*), word FROM ds.Table WHERE word CONTAINS 'th' GROUP_BY word LIMIT 5;

Since a query can operate over very large number of rows, LIMIT is a good way to avoid long-running queries for simple queries where representative data is sufficient.

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 or ORDER BY clauses, the full result set must still be processed before returning results.
• The same LIMIT clause can return different results when it shortcuts processing the full result set. This is because queries are handled by parallel processing, and the order in which parallel jobs return is not guaranteed.
• The LIMIT clause cannot contain any functions; it takes only numeric constants.

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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: SELECT country, SUM(clicks) FROM table GROUP BY country;

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 the WITHIN 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.

Note: Aggregate functions cannot be used directly in a WHERE clause. However, you can use an alias in a WHERE clause. For example:

If you want to apply a restriction to an aggregate function, use a subselect query, for example:

SELECT x, y, z, count(*) as cnt FROM foo WHERE cnt > 100   // Succeeds
SELECT x, y, z  FROM foo WHERE count(*)                    // Invalid

Syntax

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 only return 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 numeric constant, expression, or field that is multiplied with max_values to specify how many results to return.

SELECT TOP(word, 10), COUNT(*)
  FROM ds.Table WHERE word CONTAINS "th";

SELECT word, cnt, cnt * 2, left(word, 3)
  FROM (SELECT TOP(word, 10) AS word, COUNT(*) AS cnt
  FROM ds.Table WHERE word CONTAINS "th");

The following queries compare using TOP() versus using GROUP 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:

SELECT word, COUNT(*) AS cnt FROM ds.Table
  WHERE word CONTAINS 'th' GROUP BY word ORDER BY cnt DESC LIMIT 10;

SELECT TOP(word, 10), COUNT(*) FROM ds.Table WHERE word contains 'th';

Note: You must include COUNT(*) in the SELECT clause to use TOP.

Advanced examples

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
  [publicdata: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;

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
  [publicdata:samples.natality]
WHERE
  state != ''
GROUP BY
  state, sex
HAVING
  cnt > 3000000
ORDER BY
  cnt DESC

+-------+--------+---------+
| state |  sex   |   cnt   |
+-------+--------+---------+
| CA    | Male   | 7060826 |
| CA    | Female | 6733288 |
| TX    | Male   | 5107542 |
| TX    | Female | 4879247 |
| NY    | Male   | 4442246 |
| NY    | Female | 4227891 |
| IL    | Male   | 3089555 |
+-------+--------+---------+

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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

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Bitwise functions

Bitwise functions operate at the level of individual bits and require numerical arguments. For more information about bitwise functions, see Bitwise operation .

Syntax

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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

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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.

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

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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

Adds the specified interval to a TIMESTAMP data type. Possible interval_units values include YEAR , MONTH , DAY , HOUR , MINUTE , and SECOND . If interval is a negative number, the interval is subtracted from the TIMESTAMP data type.

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.

SELECT DATEDIFF(TIMESTAMP('2012-10-02 05:23:48'), TIMESTAMP('2011-06-24 12:18:35'));

Returns: 466

DAY( <timestamp> )

Returns the day of the month of a TIMESTAMP data type as an integer between 1 and 31, inclusively.

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.

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.

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 .

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.

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.

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.

SELECT MONTH(TIMESTAMP('2012-10-02 05:23:48'));

Returns: 10

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 a UNIX timestamp in microseconds.

SELECT NOW();

Returns: 1359685811687920

PARSE_UTC_USEC( <date_string> )

Converts a date string to a UNIX timestamp in microseconds. date_string must have the format YYYY-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.

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.

SELECT QUARTER(TIMESTAMP("2012-10-01 02:03:04"));

Returns: 4

Converts a UNIX timestamp in seconds to a TIMESTAMP data type.

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.

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.

SELECT STRFTIME_UTC_USEC(1274259481071200, "%Y-%m-%d");

Returns: 2010-05-19

Returns a human-readable string of a TIMESTAMP data type, in the format %H:%M:%S .

SELECT TIME(TIMESTAMP('2012-10-01 02:03:04'));

Returns: 02:03:04

Convert a date string to a TIMESTAMP data type.

SELECT TIMESTAMP("2012-10-01 01:02:03");

Returns: 2012-10-01 01:02:03 UTC

Converts a TIMESTAMP data type to a UNIX timestamp in milliseconds.

SELECT TIMESTAMP_TO_MSEC(TIMESTAMP("2012-10-01 01:02:03"));

Returns: 1349053323000

SELECT TIMESTAMP_TO_SEC(TIMESTAMP("2012-10-01 01:02:03"));

Returns: 1349053323

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.

SELECT TIMESTAMP_TO_USEC(TIMESTAMP("2012-10-01 01:02:03"));

Returns: 1349053323000000

Converts a UNIX timestamp in microseconds to a TIMESTAMP data type.

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

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).

SELECT UTC_USEC_TO_DAY(1274259481071200);

Returns: 1274227200000000

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.

SELECT UTC_USEC_TO_HOUR(1274259481071200);

Returns: 1274256000000000

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.

SELECT UTC_USEC_TO_MONTH(1274259481071200);

Returns: 1272672000000000

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 set day_of_week to 2 (Tuesday), the function returns a UNIX timestamp for Tuesday, 2008-04-08.

SELECT UTC_USEC_TO_WEEK(1207929480000000, 2) AS tuesday;

Returns: 1207612800000000

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 returns 1274259481071200 , the microsecond representation of 2010-01-01 00:00 .

SELECT UTC_USEC_TO_YEAR(1274259481071200);

Returns: 1262304000000000

SELECT YEAR(TIMESTAMP('2012-10-02 05:23:48'));

Returns: 2012

Advanced examples

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
  [publicdata:samples.wikipedia];

+----------------------------+----------------+
|     top_revision_time      | revision_count |
+----------------------------+----------------+
| 2002-02-25 15:51:15.000000 |          20971 |
| 2002-02-25 15:43:11.000000 |          15955 |
| 2010-01-14 15:52:34.000000 |              3 |
| 2009-12-31 19:29:19.000000 |              3 |
| 2009-12-28 18:55:12.000000 |              3 |
+----------------------------+----------------+

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
  [publicdata: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;

+--------------------------------+---------+-----------------------+
|      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 ...

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IP functions

IP functions convert IP addresses to and from human-readable form.

Syntax

IPAddress supports writing IPv4 and IPv6 addresses in packed strings, as 4- or 16-byte binary data in network byte order. The functions described below supports parsing the addresses to and from human readable form. These functions work only on string fields with IPs.

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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.

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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

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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

Advanced examples

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;

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;

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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

Advanced examples

SELECT
  /* Replace underscores in the title with spaces  */
  REGEXP_REPLACE(title, r'_', ' ') AS regexp_title, views
  FROM
    (SELECT title, SUM(views) as views 
    FROM [bigquery-samples:wikimedia_pageviews.201201]
  WHERE
    NOT title CONTAINS ':'
    AND wikimedia_project='wp'
    AND language='en'
    /* Match titles that start with 'G', */
    /* end with 'e', and contain two 'o's  */
    AND REGEXP_MATCH(title, r'^G.*o.*o.*e$')
  GROUP BY
    title
  ORDER BY
    views DESC
  LIMIT 100)

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 [publicdata: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;

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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 and should use LATIN-1 encoding only (use UTF-8 encoding if necessary). You can append IGNORE CASE to the end of a query to enable case-insensitive matching. IGNORE CASE works only for LATIN-1 strings.

Wildcards are not supported in these functions; for regular expression functionality, use regular expression functions .

Syntax

'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

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Table wildcard functions

Table wildcard functions are a cost-effective way to query data from a specific set of tables. 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 must be contained in parentheses, as shown in the following examples.

SELECT 
  name
FROM 
  (TABLE_DATE_RANGE(mydata.people, 
                    TIMESTAMP('2014-03-25'), 
                    TIMESTAMP('2014-03-27'))) 
WHERE 
  age >= 35

SELECT
  name
FROM 
  (TABLE_DATE_RANGE(mydata.people,
                    DATE_ADD(CURRENT_TIMESTAMP(), -2, 'DAY'),
                    CURRENT_TIMESTAMP()))
WHERE 
  age >= 35

SELECT 
  name
FROM 
  (TABLE_DATE_RANGE_STRICT(people, 
                    TIMESTAMP('2014-03-25'), 
                    TIMESTAMP('2014-03-27')))
WHERE age >= 35

SELECT 
  speed 
FROM (TABLE_QUERY(mydata,
                  'table_id CONTAINS "oo" AND length(table_id) >= 4'))

SELECT 
  speed 
FROM 
  (TABLE_QUERY(mydata,
               'REGEXP_MATCH(table_id, r"^boo[\d]{3,5}")'))

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URL functions

Syntax

Advanced examples

SELECT
  DOMAIN(repository_homepage) AS user_domain,
  COUNT(*) AS activity_count
FROM
  [publicdata:samples.github_timeline]
GROUP BY
  user_domain
HAVING
  user_domain IS NOT NULL AND user_domain != ''
ORDER BY
  activity_count DESC
LIMIT 5;

+-----------------+----------------+
|   user_domain   | activity_count |
+-----------------+----------------+
| github.com      |         281879 |
| google.com      |          34769 |
| khanacademy.org |          17316 |
| sourceforge.net |          15103 |
| mozilla.org     |          14091 |
+-----------------+----------------+

SELECT
  TLD(repository_homepage) AS user_tld,
  COUNT(*) AS activity_count
FROM
  [publicdata: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;

+----------+----------------+
| user_tld | activity_count |
+----------+----------------+
| .de      |          22934 |
| .io      |          17528 |
| .me      |          13652 |
| .fr      |          12895 |
| .co.uk   |           9135 |
+----------+----------------+

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Window functions

Window functions enable calculations on a specific partition, or "window", of a result set. Each window function expects an OVER clause that specifies the partition, in the following syntax:

OVER ([PARTITION BY <expr> ] [ORDER BY <expr> ])

PARTITION BY is always optional. ORDER BY is optional in some cases, but certain window functions, such as rank() or dense_rank() , require the clause.

JOIN EACH and GROUP EACH BY clauses can't be used on the output of window functions. Window functions can't be used to generate large query results .

The query examples associated with each of the following window functions are based upon the publicdata:samples.shakespeare dataset.

Syntax

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Other functions

Syntax

Advanced examples

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
     publicdata:samples.natality
   WHERE
     father_age < 99
   GROUP BY
     year, state)
ORDER BY
  year
LIMIT 5;

+--------+--------------------+--------------------+-------+------+
| 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 |
+--------+--------------------+--------------------+-------+------+

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
     [publicdata:samples.wikipedia]
   WHERE
     REGEXP_MATCH (title, r'^Google.+') AND wp_namespace = 0
  );

+---------------+--------+------+
|  page_title   | search | geo  |
+---------------+--------+------+
| Google search |   4261 |    0 |
| Google Earth  |      0 | 3874 |
| Google Chrome |      0 |    0 |
| Google Maps   |      0 | 2617 |
| Google bomb   |      0 |    0 |
+---------------+--------+------+

SELECT
  title,
  HASH(title) AS hash_value,
  IF(ABS(HASH(title)) % 2 == 1, 'True', 'False') 
    AS included_in_sample
FROM
  [publicdata:samples.wikipedia]
WHERE
  wp_namespace = 0
LIMIT 5;

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