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IDENTIFIER clause

  • Article

Applies to: check marked yes Databricks SQL check marked yes Databricks Runtime 13.3 LTS and above

The IDENTIFIER clause interprets a constant string as a:

  • table or view name
  • function name
  • column name
  • field name
  • schema name
  • catalog name

The clause enables SQL injection safe parameterization of SQL statements.

The IDENTIFIER clause is supported for the following statements:

  • Table, view, or function name of a CREATE, ALTER, DROP, UNDROP
  • Table name of a MERGE, UPDATE, DELETE, INSERT, COPY INTO
  • Target of a SHOW or DESCRIBE
  • USE of a schema
  • A function invocation
  • A column or view referenced in a query. This includes queries embedded in a DDL or DML statement.

Applies to: check marked yes Databricks Runtime 13.3 LTS and above

  • USE of a catalog

When using the identifier clause it may not be embedded within an identifier.

Syntax

IDENTIFIER ( strExpr )

Parameters

  • strExpr: A constant STRING expression typically including one or more parameter markers.

Examples

Scala

// Creation of a table using parameter marker.
spark.sql("CREATE TABLE IDENTIFIER(:mytab)(c1 INT)", args = Map("mytab" -> "tab1"))

// Altering a table with a fixed schema and a parameterized table name.
spark.sql("ALTER TABLE IDENTIFIER('default.' || :mytab) ADD COLUMN c2 INT)", args = Map("mytab" -> "tab1"))

// Dropping a table with separate schema and table parameters.
spark.sql("DROP TABLE IDENTIFIER(:myschema || '.' || :mytab)", args = Map("mySchema" -> "default", "mytab" -> "tab1"))

// A parameterized reference to a table in a query. The table name is qualified and uses back-ticks.
spark.sql("SELECT * FROM IDENTIFIER(:mytab)", args = Map("mytab" -> "`default`.`tab1`"))

// You cannot qualify the IDENTIFIER claue or use it as a qualifier itself.
spark.sql("SELECT * FROM myschema.IDENTIFIER(:mytab)", args = Map("mytab" -> "`tab1`"))

spark.sql("SELECT * FROM IDENTIFIER(:myschema).mytab", args = Map("mychema" -> "`default`"))

// A parameterized column reference
spark.sql("SELECT IDENTIFIER(:col) FROM VALUES(1) AS T(c1)", args = Map("col" -> "t.c1"))

// Passing in an aggregate function name as a parameter
spark.sql("SELECT IDENTIFIER(:agg)(c1) FROM VALUES(1), (2) AS T(c1)", args = Map("agg" -> "max"))

SQL


-- Using a catalog using a variable.
> DECLARE mycat = 'main';
> USE CATALOG IDENTIFIER(mycat);

-- Creation of a table using variable.
> DECLARE mytab = 'tab1';
> CREATE TABLE IDENTIFIER(mytab)(c1 INT);

-- Altering a table with a fixed schema and a parameterized table name.
> ALTER TABLE IDENTIFIER('default.' || mytab) ADD COLUMN c2 INT;

-- Inserting using a parameterized table name. The table name is qualified and uses back-ticks.
> SET VAR mytab = '`default`.`tab1`';
> INSERT INTO IDENTIFIER(mytab) VALUES(1, 2);

-- A parameterized reference to a table in a query.
> SELECT * FROM IDENTIFIER(mytab);
  1   2

-- Dropping a table with separate schema and table parameters.
> DECLARE myschema = 'default';
> SET VAR mytab = 'tab1';
> DROP TABLE IDENTIFIER(myschema || '.' || mytab);

 -- You cannot qualify the IDENTIFIER claue or use it as a qualifier itself.
> SELECT * FROM myschema.IDENTIFIER('tab');
Error: PARSE_SYNTAX_ERROR

> SELECT * FROM IDENTIFIER('default').mytab;
Error: PARSE_SYNTAX_ERROR

-- A parameterized column reference
> DECLARE col = 't.c1';
> SELECT IDENTIFIER(col) FROM VALUES(1) AS T(c1);
  1

-- Passing in an aggregate function name as a parameter
> DECLARE agg = 'max';
> SELECT IDENTIFIER(agg)(c1) FROM VALUES(1), (2) AS T(c1);
  2