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Legacy SQL Syntax, Functions and Operators | BigQuery

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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.

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

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 a GROUP BY clause which includes all non-aggregated fields in your SELECT 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 ...

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;

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.

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
  [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.

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.

[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.

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;

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:

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.

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.

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([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.

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.

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.

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.

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.

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.

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.

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');

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.

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;

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.

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 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.

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.

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.

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 the GROUP 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 corresponding GROUP 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 of GROUP BY might solve some scaling problems.

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 SELECTclause.

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;

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.

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 or ORDER BY clauses, the full result set must still be processed before returning results. The LIMIT 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.
• When the LIMIT clause is used, the total bytes processed and the bytes billed can vary for the same query.

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.

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 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.

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.

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 of numeric_expr across all rows. NULL values are ignored. This function returns NULL if all instances of numeric_expr evaluate to NULL.

BIT_OR(numeric_expr)

Returns the result of a bitwise OR operation between each instance of numeric_expr across all rows. NULL values are ignored. This function returns NULL if all instances of numeric_expr evaluate to NULL.

BIT_XOR(numeric_expr)

Returns the result of a bitwise XOR operation between each instance of numeric_expr across all rows. NULL values are ignored. This function returns NULL if all instances of numeric_expr evaluate to NULL.

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 the TOP 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 for DISTINCT 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 COUNT(DISTINCT), you can specify a second parameter, n, which gives the threshold below which exact results are guaranteed. By default, n is 1000, but if you give a larger n, you will get exact results for COUNT(DISTINCT) up to that value of n. However, giving larger values of n 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 applying COUNT(*). The GROUP 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 and numeric_expr2.

COVAR_SAMP(numeric_expr1, numeric_expr2)

Computes the sample covariance of the values computed by numeric_expr1 and numeric_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. If separator 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 string a"b would return as "a""b". Use GROUP_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. If separator 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 string a"b would return as a"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 distinct x value, and contains a repeated field for all y values paired with x in the query input. The NEST function requires a GROUP 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 the NEST function when using a subselect that produces intermediate results for immediate use by the same query.

NTH(n, field)

Returns the nth sequential value in the scope of the function, where n is a constant. The NTH function starts counting at 1, so there is no zeroth term. If the scope of the function has less than n values, the function returns NULL.

QUANTILES(expr[, buckets])

Computes approximate minimum, maximum, and quantiles for the input expression. NULL input values are ignored. Empty or exclusively-NULL input results in NULL output. The number of quantiles computed is controlled with the optional buckets parameter, which includes the minimum and maximum in the count. To compute approximate N-tiles, use N+1 buckets. The default value of buckets is 100. (Note: The default of 100 does not estimate percentiles. To estimate percentiles, use 101 buckets 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 that NTH is 1-based, and that QUANTILES 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 of expr, whereas NTH(20, QUANTILES(expr, 21)) estimates the 19th vigintile (95th percentile) of expr. 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, use NTH(501, QUANTILES(expr, 1001)); for the 95th percentile with 0.1% error, use NTH(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. The STDDEV function is an alias for STDDEV_SAMP.

STDDEV_POP(numeric_expr)

Computes the population standard deviation of the value computed by numeric_expr. Use STDDEV_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, use STDDEV_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. Use STDDEV_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, use STDDEV_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 the EACH keyword, the query will fail with a "Resources Exceeded" error if there are too many distinct values. Unlike GROUP BY, however, the UNIQUE 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. The VARIANCE function is an alias for VAR_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 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.

• 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() to 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:

  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 to multiplier parameter to multiply the cnt 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.

• 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 the HAVING function. HAVING compares a value to a result determined by an aggregation function, as opposed to WHERE, 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 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.

SELECT 6 + (5 - 1);

Returns: 10

SELECT 6 - (4 + 1);

Returns: 1

SELECT 6 * (5 - 1);

Returns: 24

SELECT 6 / (2 + 2);

Returns: 1.5

SELECT 6 % (2 + 2);

Returns: 2

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.

SELECT (1 + 3) & 1

Returns: 0

SELECT 24 | 12

Returns: 28

SELECT 1 ^ 0

Returns: 1

SELECT 1 << (2 + 2)

Returns: 16

SELECT (6 + 2) >> 2

Returns: 2

SELECT ~2

Returns: -3

Returns the number of bits that are set in <numeric_expr>.

SELECT BIT_COUNT(29);

Returns: 4

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.

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.

BYTES(string_expr)

Returns string_expr as a value of type bytes.

CAST(expr AS type)

Converts expr into a variable of type type.

FLOAT(expr)

Returns expr as a double. The expr can be a string like '45.78', but the function returns NULL 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.

STRING(numeric_expr)

Returns numeric_expr as a string.

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.

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 greater than or equal to expr2, and less than or equal to expr3.

expr IS NULL

Returns true if expr is NULL.

expr IN(expr1, expr2, ...)

Returns true if expr matches expr1, expr2, or any value in the parentheses. The IN keyword is an efficient shorthand for (expr = expr1 || expr = expr2 || ...). The expressions used with the IN keyword must be constants and they must match the data type of expr. The IN 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 is NULL, this function returns NULL.

To ignore NULL values, use the IFNULL function to change NULL values to a value that doesn't affect the comparison. In the following code example, the IFNULL function is used to change NULL 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, returns expr, otherwise returns null_default.

IS_INF(numeric_expr)

Returns true if numeric_expr is positive or negative infinity.

IS_NAN(numeric_expr)

Returns true if numeric_expr is the special NaN 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 is NULL, this function returns NULL

NVL(expr, null_default)

If expr is not null, returns expr, otherwise returns null_default. The NVL function is an alias for IFNULL.

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.

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 include YEAR, MONTH, DAY, HOUR, MINUTE, and SECOND. If interval 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 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.

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 set day_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 returns 1274259481071200, the microsecond representation of 2010-01-01 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

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

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 return 1. If string is not a valid IPv4 address, PARSE_IP will return NULL.

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.

FORMAT_PACKED_IP(packed_ip)

Returns a human-readable IP address, in the form 10.1.5.23 or 2620:0:1009:1:216:36ff: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 return NULL. Examples:

• PARSE_PACKED_IP('48.49.50.51') returns 'MDEyMw=='
• PARSE_PACKED_IP('3031:3233:3435:3637:3839:4041:4243:4445') returns 'MDEyMzQ1Njc4OUBBQkNERQ=='

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.

Selects a value in json according to the JSONPath expression json_path. json_path must be a string constant. Returns the value in JSON string format.

Selects a value in json according to the JSONPath expression json_path. json_path must be a string constant. Returns a scalar JSON value.

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.

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.

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.

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) or IS NOT NULL.

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.

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 use PI() like a constant with mathematical and arithmetic functions.

POW(numeric_expr1, numeric_expr2)

Returns the result of raising numeric_expr1 to the power of numeric_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 a LIMIT clause. If int32_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.

• 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(), and COS().

  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;

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.

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

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

• 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 the SELECT. 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 value corpus_date to a string, which is then altered by REGEXP_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 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.

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 is Java and str2 is Script, CONCAT returns JavaScript.

expr CONTAINS 'str'

Returns true if expr 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) returns sea.

LENGTH('str')

Returns a numerical value for the length of the string. Example: if str is '123456', LENGTH returns 6.

LOWER('str')

Returns the original string with all characters in lower case.

LPAD('str1', numeric_expr, 'str2')

Pads str1 on the left with str2, repeating str2 until the result string is exactly numeric_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) returns land.

RPAD('str1', numeric_expr, 'str2')

Pads str1 on the right with str2, repeating str2 until the result string is exactly numeric_expr characters. Example: RPAD('1', 7, '?') returns 1??????.

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, the SPLIT function breaks str into substrings, using delimiter as the delimiter.

SUBSTR('str', index [, max_len])

Returns a substring of str, starting at index. If the optional max_len parameter is used, the returned string is a maximum of max_len characters long. Counting starts at 1, so the first character in the string is in position 1 (not zero). If index is 5, the substring begins with the 5th character from the left in str. If index is -4, the substring begins with the 4th character from the right in str. Example: SUBSTR('awesome', -4, 4) returns the substring some.

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 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.

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 format YYYYMMDD.

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 a Not 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. The expr 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

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.

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, 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. If PARTITION 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 with allowLargeResults, or if you intend to apply further joins or aggregations to the output of your window function, use PARTITION BY to parallelize execution.

JOIN EACH and GROUP EACH BY clauses can't be used on the output of window functions. To generate large query results when using window functions, you must use PARTITION 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 a RANGE window, you should add an ORDER BY clause. Default order is ASC.

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 specifying ROWS or RANGE, ORDER BY implies that the window extends from the beginning of the partition to the current row. In the absence of an ORDER 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 a window-frame-clause, the default window frame is RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW. If you omit both ORDER BY and the window-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 query SUM(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 the CURRENT ROW are included in a window frame that specifies CURRENT ROW. For example, if you specify the window end to be CURRENT 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:

  {UNBOUNDED PRECEDING | CURRENT ROW | <expr> PRECEDING | <expr> FOLLOWING}

  where <expr> is a positive integer, PRECEDING indicates a preceding row number or range value, and FOLLOWING 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:

  {UNBOUNDED FOLLOWING | CURRENT ROW | <expr> PRECEDING | <expr> FOLLOWING}

  where <expr> is a positive integer, PRECEDING indicates a preceding row number or range value, and FOLLOWING 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 |
+------+-------+---------------+

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 the EXACT_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 the OVER 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 the OVER clause.

#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

Returns: 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.

#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

Returns: 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.

#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

Returns: 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. The ntile() function assigns the bucket numbers as equally as possible and returns a value from 1 to <num_buckets> for each row.

#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

Returns: 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 the OVER clause.

#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

Returns: 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 the OVER clause.

#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

Returns: 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 the OVER clause.

#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

Returns: 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 the OVER clause.

#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

Returns: 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.

#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

Returns: 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.

#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

Returns: word word_count row_num handkerchief 29 1 satisfaction 5 2 displeasure 4 3 instruments 4 4 circumstance 3 5

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 if condition is true for all of its inputs. When used with the OMIT 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 respects IGNORE CASE for strings, returning case invariant values.

FARM_FINGERPRINT(expr)

Computes and returns a 64-bit signed fingerprint value of the STRING or BYTES input using the Fingerprint64 function from the open-source FarmHash library. The output of this function for a particular input will never change and matches the output of the FARM_FINGERPRINT function when using GoogleSQL. Respects IGNORE CASE for strings, returning case invariant values.

IF(condition, true_return, false_return)

Returns either true_return or false_return, depending on whether condition 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 the SELECT 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 if condition is true for at least one of its inputs. When used with the OMIT 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:

#legacySQL
SELECT
  TO_BASE64(SHA1(title))
FROM
  [bigquery-public-data:samples.wikipedia]
LIMIT
  100;

To convert a base64-encoded string to BYTES, use FROM_BASE64().

• 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 the WHEN 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 the ABS function in combination with HASH, because HASH 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;