Jaa

inline_outer table-valued generator function

  • Artikkeli

Applies to: check marked yes Databricks SQL check marked yes Databricks Runtime

Explodes an array of structs into a table with OUTER semantics.

In Databricks Runtime 16.1 and above this function supports named parameter invocation.

Syntax

inline_outer(input)

Arguments

  • input: An ARRAY < STRUCT > expression.

A set of rows composed of the fields in the struct elements of the array input. The columns produced by inline are the names of the fields.

If input is NULL a single row with NULLs for each column is produced.

  • Applies to: check marked yes Databricks Runtime 12.1 and earlier:

    inline_outer can only be placed in the SELECT list as the root of an expression or following a LATERAL VIEW. When placing the function in the SELECT list there must be no other generator function in the same SELECT list or UNSUPPORTED_GENERATOR.MULTI_GENERATOR is raised.

  • Applies to: check marked yes Databricks SQL check marked yes Databricks Runtime 12.2 LTS and above:

    Invocation from the LATERAL VIEW clause or the SELECT list is deprecated. Instead, invoke inline_outer as a table_reference.

Examples

Applies to: check marked yes Databricks Runtime 12.1 and earlier:

> SELECT inline_outer(array(struct(1, 'a'), struct(2, 'b'))), 'Spark SQL';
 1  a Spark SQL
 2  b Spark SQL

> SELECT inline_outer(array(struct(1, 'a'), struct(1, 'b'))),
         inline_outer(array(struct('c', 1.0), struct('d', 2.0))),
         'Spark SQL';
 1  a Spark SQL
 2  b Spark SQL
Error: UNSUPPORTED_GENERATOR.MULTI_GENERATOR

Applies to: check marked yes Databricks SQL check marked yes Databricks Runtime 12.2 LTS and above:

> SELECT i.*, 'Spark SQL'
    FROM inline_outer(array(struct(1, 'a'), struct(2, 'b'))) AS i;
 1  a Spark SQL
 2  b Spark SQL

> SELECT i1.*, i2.*, 'Spark SQL'
   FROM  inline_outer(array(struct(1, 'a'), struct(1, 'b'))) AS i1,
         inline_outer(array(struct('c', 1.0), struct('d', 2.0))) AS i2;
 1      a       c       1.0     Spark SQL
 1      b       c       1.0     Spark SQL
 1      a       d       2.0     Spark SQL
 1      b       d       2.0     Spark SQL