A SELECT statement can consist of the following basic clauses.

• SELECT
• INTO
• FROM
• JOIN
• WHERE
• GROUP BY
• HAVING
• UNION
• ORDER BY
• LIMIT

SELECT Syntax

The following syntax diagram outlines the syntax supported by the SQL engine of the driver:

SELECT {
  [ TOP <numeric_literal> | DISTINCT ]
  { 
    * 
    | { 
        <expression> [ [ AS ] <column_reference> ] 
        | { <table_name> | <correlation_name> } .* 
      } [ , ... ] 
  }
  [ INTO csv:// [ filename= ] <file_path> [ ;delimiter=tab ] ]
  { 
    FROM <table_reference> [ [ AS ] <identifier> ] 
  } [ , ... ]
  [ [  
      INNER | { { LEFT | RIGHT | FULL } [ OUTER ] } 
    ] JOIN <table_reference> [ ON <search_condition> ] [ [ AS ] <identifier> ] 
  ] [ ... ] 
  [ WHERE <search_condition> ]
  [ GROUP BY <column_reference> [ , ... ]
  [ HAVING <search_condition> ]
  [ UNION [ ALL ] <select_statement> ]
  [ 
    ORDER BY 
    <column_reference> [ ASC | DESC ] [ NULLS FIRST | NULLS LAST ]
  ]
  [ 
    LIMIT <expression>
    [ 
      { OFFSET | , }
      <expression> 
    ]
  ] 
}

<expression> ::=
  | <column_reference>
  | @ <parameter> 
  | ?
  | COUNT( * | { [ DISTINCT ] <expression> } )
  | { AVG | MAX | MIN | SUM | COUNT } ( <expression> ) 
  | NULLIF ( <expression> , <expression> ) 
  | COALESCE ( <expression> , ... ) 
  | CASE <expression>
      WHEN { <expression> | <search_condition> } THEN { <expression> | NULL } [ ... ]
    [ ELSE { <expression> | NULL } ]
    END 
  | <literal>
  | <sql_function> 

<search_condition> ::= 
  {
    <expression> { = | > | < | >= | <= | <> | != | LIKE | NOT LIKE | IN | NOT IN | IS NULL | IS NOT NULL | AND | OR | CONTAINS | BETWEEN } [ <expression> ]
  } [ { AND | OR } ... ] 

Examples

1. Return all columns:
   

   SELECT * FROM Schemas

2. Rename a column:
   

   SELECT [DatasetName] AS MY_DatasetName FROM Schemas

3. Cast a column's data as a different data type:
   

   SELECT CAST(AnnualRevenue AS VARCHAR) AS Str_AnnualRevenue FROM Schemas

4. Search data:
   

   SELECT * FROM Schemas WHERE ProjectId = 'bigquery-public-data'

5. Return the number of items matching the query criteria:
   

   SELECT COUNT(*) AS MyCount FROM Schemas 

6. Return the number of unique items matching the query criteria:
   

   SELECT COUNT(DISTINCT DatasetName) FROM Schemas 

7. Return the unique items matching the query criteria:
   

   SELECT DISTINCT DatasetName FROM Schemas 

8. Summarize data:
   

   SELECT DatasetName, MAX(AnnualRevenue) FROM Schemas GROUP BY DatasetName

   See Aggregate Functions for details.
9. Retrieve data from multiple tables.
   

   SELECT s.DatasetName, s.Type, t.TableName, t.ResourceName FROM Tables t INNER JOIN Schemas s ON s.DatasetName = t.DatasetName

   See JOIN Queries for details.
10. Sort a result set in ascending order:
    

    SELECT Type, DatasetName FROM Schemas  ORDER BY DatasetName ASC

11. Restrict a result set to the specified number of rows:
    

    SELECT Type, DatasetName FROM Schemas LIMIT 10 

12. Parameterize a query to pass in inputs at execution time. This enables you to create prepared statements and mitigate SQL injection attacks.
    

    SELECT * FROM Schemas WHERE ProjectId = @param

See Explicitly Caching Data for information on using the SELECT statement in offline mode.

Pseudo Columns

Some input-only fields are available in SELECT statements. These fields, called pseudo columns, do not appear as regular columns in the results, yet may be specified as part of the WHERE clause. You can use pseudo columns to access additional features from Google Data Catalog.

    SELECT * FROM Schemas WHERE CreatedTime = '2018-08-13 18:39:00'