ARRAY complex type

Syntax:

column_name ARRAY < type >

type ::= primitive_type | complex_type

Usage:

Because complex types are often used in combination, for example an ARRAY of STRUCT elements, if you are unfamiliar with the Impala complex types, start with Complex types for background information and usage examples.

The elements of the array have no names. You refer to the value of the array item using the ITEM pseudocolumn, or its position in the array with the POS pseudocolumn.

Each row can have a different number of elements (including none) in the array for that row.

When an array contains items of scalar types, you can use aggregation functions on the array elements without using join notation. For example, you can find the COUNT(), AVG(), SUM(), and so on of numeric array elements, or the MAX() and MIN() of any scalar array elements by referring to table_name.array_column in the FROM clause of the query. When you need to cross-reference values from the array with scalar values from the same row, such as by including a GROUP BY clause to produce a separate aggregated result for each row, then the join clause is required.

A common usage pattern with complex types is to have an array as the top-level type for the column: an array of structs, an array of maps, or an array of arrays. For example, you can model a denormalized table by creating a column that is an ARRAY of STRUCT elements; each item in the array represents a row from a table that would normally be used in a join query. This kind of data structure lets you essentially denormalize tables by associating multiple rows from one table with the matching row in another table.

You typically do not create more than one top-level ARRAY column, because if there is some relationship between the elements of multiple arrays, it is convenient to model the data as an array of another complex type element (either STRUCT or MAP).

You can pass a multi-part qualified name to DESCRIBE to specify an ARRAY, STRUCT, or MAP column and visualize its structure as if it were a table. For example, if table T1 contains an ARRAY column A1, you could issue the statement DESCRIBE t1.a1. If table T1 contained a STRUCT column S1, and a field F1 within the STRUCT was a MAP, you could issue the statement DESCRIBE t1.s1.f1. An ARRAY is shown as a two-column table, with ITEM and POS columns. A STRUCT is shown as a table with each field representing a column in the table. A MAP is shown as a two-column table, with KEY and VALUE columns.

Restrictions:

• Columns with this data type can be used in tables or partitions with the Parquet and ORC file formats.

• Columns with this data type cannot be used as partition key columns in a partitioned table.

• The COMPUTE STATS statement does not produce any statistics for columns of this data type.

• The maximum length of the column definition for any complex type, including declarations for any nested types, is 4000 characters.

• See the Limitations and restrictions for complex types topic for a full list of limitations and associated guidelines about complex type columns.

Kudu considerations:

Currently, the data types CHAR, ARRAY, MAP, and STRUCT cannot be used with Kudu tables.

Examples:

The following example shows how to construct a table with various kinds of ARRAY columns, both at the top level and nested within other complex types. Whenever the ARRAY consists of a scalar value, such as in the PETS column or the CHILDREN field, you can see that future expansion is limited. For example, you could not easily evolve the schema to record the kind of pet or the child's birthday alongside the name. Therefore, it is more common to use an ARRAY whose elements are of STRUCT type, to associate multiple fields with each array element.

CREATE TABLE array_demo
(
  id BIGINT,
  name STRING,
-- An ARRAY of scalar type as a top-level column.
  pets ARRAY <STRING>,

-- An ARRAY with elements of complex type (STRUCT).
  places_lived ARRAY < STRUCT <
    place: STRING,
    start_year: INT
  >>,

-- An ARRAY as a field (CHILDREN) within a STRUCT.
-- (The STRUCT is inside another ARRAY, because it is rare
-- for a STRUCT to be a top-level column.)
  marriages ARRAY < STRUCT <
    spouse: STRING,
    children: ARRAY <STRING>
  >>,

-- An ARRAY as the value part of a MAP.
-- The first MAP field (the key) would be a value such as
-- 'Parent' or 'Grandparent', and the corresponding array would
-- represent 2 parents, 4 grandparents, and so on.
  ancestors MAP < STRING, ARRAY <STRING> >
)
STORED AS PARQUET;

The following example shows how to examine the structure of a table containing one or more ARRAY columns by using the DESCRIBE statement. You can visualize each ARRAY as its own two-column table, with columns ITEM and POS.

DESCRIBE array_demo;
+--------------+---------------------------+
| name         | type                      |
+--------------+---------------------------+
| id           | bigint                    |
| name         | string                    |
| pets         | array<string>             |
| marriages    | array<struct<             |
|              |   spouse:string,          |
|              |   children:array<string>  |
|              | >>                        |
| places_lived | array<struct<             |
|              |   place:string,           |
|              |   start_year:int          |
|              | >>                        |
| ancestors    | map<string,array<string>> |
+--------------+---------------------------+

DESCRIBE array_demo.pets;
+------+--------+
| name | type   |
+------+--------+
| item | string |
| pos  | bigint |
+------+--------+

DESCRIBE array_demo.marriages;
+------+--------------------------+
| name | type                     |
+------+--------------------------+
| item | struct<                  |
|      |   spouse:string,         |
|      |   children:array<string> |
|      | >                        |
| pos  | bigint                   |
+------+--------------------------+

DESCRIBE array_demo.places_lived;
+------+------------------+
| name | type             |
+------+------------------+
| item | struct<          |
|      |   place:string,  |
|      |   start_year:int |
|      | >                |
| pos  | bigint           |
+------+------------------+

DESCRIBE array_demo.ancestors;
+-------+---------------+
| name  | type          |
+-------+---------------+
| key   | string        |
| value | array<string> |
+-------+---------------+

The following example shows queries involving ARRAY columns containing elements of scalar or complex types. You unpack each ARRAY column by referring to it in a join query, as if it were a separate table with ITEM and POS columns. If the array element is a scalar type, you refer to its value using the ITEM pseudocolumn. If the array element is a STRUCT, you refer to the STRUCT fields using dot notation and the field names. If the array element is another ARRAY or a MAP, you use another level of join to unpack the nested collection elements.

-- Array of scalar values.
-- Each array element represents a single string, plus we know its position in the array.
SELECT id, name, pets.pos, pets.item FROM array_demo, array_demo.pets;

-- Array of structs.
-- Now each array element has named fields, possibly of different types.
-- You can consider an ARRAY of STRUCT to represent a table inside another table.
SELECT id, name, places_lived.pos, places_lived.item.place, places_lived.item.start_year
FROM array_demo, array_demo.places_lived;

-- The .ITEM name is optional for array elements that are structs.
-- The following query is equivalent to the previous one, with .ITEM
-- removed from the column references.
SELECT id, name, places_lived.pos, places_lived.place, places_lived.start_year
  FROM array_demo, array_demo.places_lived;

-- To filter specific items from the array, do comparisons against the .POS or .ITEM
-- pseudocolumns, or names of struct fields, in the WHERE clause.
SELECT id, name, pets.item FROM array_demo, array_demo.pets
  WHERE pets.pos in (0, 1, 3);

SELECT id, name, pets.item FROM array_demo, array_demo.pets
  WHERE pets.item LIKE 'Mr. %';

SELECT id, name, places_lived.pos, places_lived.place, places_lived.start_year
  FROM array_demo, array_demo.places_lived
WHERE places_lived.place like '%California%';