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> } .* 
      } [ , ... ] 
  }
  { 
    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> 
    ]
  ] 
} | SCOPE_IDENTITY() 

<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 
  | {RANK() | DENSE_RANK()} OVER ([PARTITION BY <column_reference>] {ORDER BY <column_reference>})
  | <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 [CData].[Sample].Customers

2. Rename a column:
   

   SELECT [CompanyName] AS MY_CompanyName FROM [CData].[Sample].Customers

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

   SELECT CAST(Balance AS VARCHAR) AS Str_Balance FROM [CData].[Sample].Customers

4. Search data:
   

   SELECT * FROM [CData].[Sample].Customers WHERE Country = 'US'

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

   SELECT COUNT(*) AS MyCount FROM [CData].[Sample].Customers 

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

   SELECT COUNT(DISTINCT CompanyName) FROM [CData].[Sample].Customers 

7. Return the unique items matching the query criteria:
   

   SELECT DISTINCT CompanyName FROM [CData].[Sample].Customers 

8. Sort a result set in ascending order:
   

   SELECT City, CompanyName FROM [CData].[Sample].Customers  ORDER BY CompanyName ASC

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

   SELECT City, CompanyName FROM [CData].[Sample].Customers LIMIT 10 

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

    SELECT * FROM [CData].[Sample].Customers WHERE Country = @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 Databricks.

    SELECT * FROM [CData].[Sample].Customers WHERE MyPseudocolumn = 'MyValue'
    

Aggregate Functions

For SELECT examples using aggregate functions, see Aggregate Functions.

JOIN Queries

See JOIN Queries for SELECT query examples using JOINs.

Date Literal Functions

Date Literal Functions contains SELECT examples with date literal functions.

Projection Functions

See Projection Functions for SELECT examples with projection functions.

Predicate Functions

For SELECT examples using predicate functions, see Predicate Functions.