Jaa

explode table-valued generator function

  • Artikkeli

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

Returns a set of rows by un-nesting collection.

In Databricks SQL and Databricks Runtime 13.3 LTS and above this function supports named parameter invocation.

Syntax

explode(collection)

Arguments

  • collection: An ARRAY or MAP expression.

Returns

A set of rows composed of the elements of the array or the keys and values of the map. The column produced by explode of an array is named col. The columns for a map are called key and value.

If collection is NULL no rows are produced. To return a single row with NULLs for the array or map values use the explode_outer() function.

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

    explode 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 explode as a table_reference.

Examples

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

> SELECT explode(array(10, 20)) AS elem, 'Spark';
 10 Spark
 20 Spark

> SELECT explode(map(1, 'a', 2, 'b')) AS (num, val), 'Spark';
 1   a   Spark
 2   b   Spark

> SELECT explode(array(1, 2)), explode(array(3, 4));
  Error: UNSUPPORTED_GENERATOR.MULTI_GENERATOR

-- The difference between explode() and explode_outer() is that explode_outer() returns NULL if the array is NULL.
> SELECT explode_outer(c1) AS elem, 'Spark' FROM VALUES(array(10, 20)), (null) AS T(c1);
 10   Spark
 20   Spark
 NULL Spark

> SELECT explode(c1) AS elem, 'Spark' FROM VALUES(array(10, 20)), (null) AS T(c1);
 10 Spark
 20 Spark

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

> SELECT elem, 'Spark' FROM explode(array(10, 20)) AS t(elem);
 10 Spark
 20 Spark

> SELECT num, val, 'Spark' FROM explode(map(1, 'a', 2, 'b')) AS t(num, val);
 1   a   Spark
 2   b   Spark

> SELECT * FROM explode(array(1, 2)), explode(array(3, 4));
 1   3
 1   4
 2   3
 2   4

-- Using lateral correlation in Databricks 12.2 and above
> SELECT * FROM explode(array(1, 2)) AS t, LATERAL explode(array(3 * t.col, 4 * t.col));
 1   3
 1   4
 2   6
 2   8