STRUCT Complex Type (CDH 5.5 or higher only)

A complex data type, representing multiple fields of a single item. Frequently used as the element type of an ARRAY or the VALUE part of a MAP.

Syntax:

column_name STRUCT < name : type [COMMENT 'comment_string'], ... >

type ::= primitive_type | complex_type

The names and number of fields within the STRUCT are fixed. Each field can be a different type. A field within a STRUCT can also be another STRUCT, or an ARRAY or a MAP, allowing you to create nested data structures with a maximum nesting depth of 100.

A STRUCT can be the top-level type for a column, or can itself be an item within an ARRAY or the value part of the key-value pair in a MAP.

When a STRUCT is used as an ARRAY element or a MAP value, you use a join clause to bring the ARRAY or MAP elements into the result set, and then refer to array_name.ITEM.field or map_name.VALUE.field. In the case of a STRUCT directly inside an ARRAY or MAP, you can omit the .ITEM and .VALUE pseudocolumns and refer directly to array_name.field or map_name.field.

Usage notes:

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 (CDH 5.5 or higher only) for background information and usage examples.

A STRUCT is similar conceptually to a table row: it contains a fixed number of named fields, each with a predefined type. To combine two related tables, while using complex types to minimize repetition, the typical way to represent that data is as an ARRAY of STRUCT elements.

Because a STRUCT has a fixed number of named fields, it typically does not make sense to have a STRUCT as the type of a table column. In such a case, you could just make each field of the STRUCT into a separate column of the table. The STRUCT type is most useful as an item of an ARRAY or the value part of the key-value pair in a MAP. A nested type column with a STRUCT at the lowest level lets you associate a variable number of row-like objects with each row of the table.

The STRUCT type is straightforward to reference within a query. You do not need to include the STRUCT column in a join clause or give it a table alias, as is required for the ARRAY and MAP types. You refer to the individual fields using dot notation, such as struct_column_name.field_name, without any pseudocolumn such as ITEM or VALUE.

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.

Internal details:

Within the Parquet data file, the values for each STRUCT field are stored adjacent to each other, so that they can be encoded and compressed using all the Parquet techniques for storing sets of similar or repeated values. The adjacency applies even when the STRUCT values are part of an ARRAY or MAP. During a query, Impala avoids unnecessary I/O by reading only the portions of the Parquet data file containing the requested STRUCT fields.

Added in: CDH 5.5.0 / Impala 2.3.0

Restrictions:

Columns with this data type can only be used in tables or partitions with the Parquet file format.
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 Limitations and Restrictions for Complex Types for a full list of limitations and associated guidelines about complex type columns.

Kudu considerations:

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

Examples:

The following example shows a table with various kinds of STRUCT columns, both at the top level and nested within other complex types. Practice the CREATE TABLE and query notation for complex type columns using empty tables, until you can visualize a complex data structure and construct corresponding SQL statements reliably.

CREATE TABLE struct_demo
(
  id BIGINT,
  name STRING,

-- A STRUCT as a top-level column. Demonstrates how the table ID column
-- and the ID field within the STRUCT can coexist without a name conflict.
  employee_info STRUCT < employer: STRING, id: BIGINT, address: STRING >,

-- A STRUCT as the element type of an ARRAY.
  places_lived ARRAY < STRUCT <street: STRING, city: STRING, country: STRING >>,

-- A STRUCT as the value portion of the key-value pairs in a MAP.
  memorable_moments MAP < STRING, STRUCT < year: INT, place: STRING, details: STRING >>,

-- A STRUCT where one of the fields is another STRUCT.
  current_address STRUCT < street_address: STRUCT <street_number: INT, street_name: STRING, street_type: STRING>, country: STRING, postal_code: STRING >
)
STORED AS PARQUET;

The following example shows how to examine the structure of a table containing one or more STRUCT columns by using the DESCRIBE statement. You can visualize each STRUCT as its own table, with columns named the same as each field of the STRUCT. If the STRUCT is nested inside another complex type, such as ARRAY, you can extend the qualified name passed to DESCRIBE until the output shows just the STRUCT fields.

DESCRIBE struct_demo;
+-------------------+--------------------------+
| name              | type                     |
+-------------------+--------------------------+
| id                | bigint                   |
| name              | string                   |
| employee_info     | struct<                  |
|                   |   employer:string,       |
|                   |   id:bigint,             |
|                   |   address:string         |
|                   | >                        |
| places_lived      | array<struct<            |
|                   |   street:string,         |
|                   |   city:string,           |
|                   |   country:string         |
|                   | >>                       |
| memorable_moments | map<string,struct<       |
|                   |   year:int,              |
|                   |   place:string,          |
|                   |   details:string         |
|                   | >>                       |
| current_address   | struct<                  |
|                   |   street_address:struct< |
|                   |     street_number:int,   |
|                   |     street_name:string,  |
|                   |     street_type:string   |
|                   |   >,                     |
|                   |   country:string,        |
|                   |   postal_code:string     |
|                   | >                        |
+-------------------+--------------------------+

The top-level column EMPLOYEE_INFO is a STRUCT. Describing table_name.struct_name displays the fields of the STRUCT as if they were columns of a table:

DESCRIBE struct_demo.employee_info;
+----------+--------+
| name     | type   |
+----------+--------+
| employer | string |
| id       | bigint |
| address  | string |
+----------+--------+

Because PLACES_LIVED is a STRUCT inside an ARRAY, the initial DESCRIBE shows the structure of the ARRAY:

DESCRIBE struct_demo.places_lived;
+------+------------------+
| name | type             |
+------+------------------+
| item | struct<          |
|      |   street:string, |
|      |   city:string,   |
|      |   country:string |
|      | >                |
| pos  | bigint           |
+------+------------------+

Ask for the details of the ITEM field of the ARRAY to see just the layout of the STRUCT:

DESCRIBE struct_demo.places_lived.item;
+---------+--------+
| name    | type   |
+---------+--------+
| street  | string |
| city    | string |
| country | string |
+---------+--------+

Likewise, MEMORABLE_MOMENTS has a STRUCT inside a MAP, which requires an extra level of qualified name to see just the STRUCT part:

DESCRIBE struct_demo.memorable_moments;
+-------+------------------+
| name  | type             |
+-------+------------------+
| key   | string           |
| value | struct<          |
|       |   year:int,      |
|       |   place:string,  |
|       |   details:string |
|       | >                |
+-------+------------------+

For a MAP, ask to see the VALUE field to see the corresponding STRUCT fields in a table-like structure:

DESCRIBE struct_demo.memorable_moments.value;
+---------+--------+
| name    | type   |
+---------+--------+
| year    | int    |
| place   | string |
| details | string |
+---------+--------+

For a STRUCT inside a STRUCT, we can see the fields of the outer STRUCT:

DESCRIBE struct_demo.current_address;
+----------------+-----------------------+
| name           | type                  |
+----------------+-----------------------+
| street_address | struct<               |
|                |   street_number:int,  |
|                |   street_name:string, |
|                |   street_type:string  |
|                | >                     |
| country        | string                |
| postal_code    | string                |
+----------------+-----------------------+

Then we can use a further qualified name to see just the fields of the inner STRUCT:

DESCRIBE struct_demo.current_address.street_address;
+---------------+--------+
| name          | type   |
+---------------+--------+
| street_number | int    |
| street_name   | string |
| street_type   | string |
+---------------+--------+

DESCRIBE struct_demo;
+-------------------+--------------------------+---------+
| name              | type                     | comment |
+-------------------+--------------------------+---------+
| id                | bigint                   |         |
| name              | string                   |         |
| employee_info     | struct<                  |         |
|                   |   employer:string,       |         |
|                   |   id:bigint,             |         |
|                   |   address:string         |         |
|                   | >                        |         |
| places_lived      | array<struct<            |         |
|                   |   street:string,         |         |
|                   |   city:string,           |         |
|                   |   country:string         |         |
|                   | >>                       |         |
| memorable_moments | map<string,struct<       |         |
|                   |   year:int,              |         |
|                   |   place:string,          |         |
|                   |   details:string         |         |
|                   | >>                       |         |
| current_address   | struct<                  |         |
|                   |   street_address:struct< |         |
|                   |     street_number:int,   |         |
|                   |     street_name:string,  |         |
|                   |     street_type:string   |         |
|                   |   >,                     |         |
|                   |   country:string,        |         |
|                   |   postal_code:string     |         |
|                   | >                        |         |
+-------------------+--------------------------+---------+

SELECT id, employee_info.id FROM struct_demo;

SELECT id, employee_info.id AS employee_id FROM struct_demo;

SELECT id, employee_info.id AS employee_id, employee_info.employer
  FROM struct_demo;

SELECT id, name, street, city, country
  FROM struct_demo, struct_demo.places_lived;

SELECT id, name, places_lived.pos, places_lived.street, places_lived.city, places_lived.country
  FROM struct_demo, struct_demo.places_lived;

SELECT id, name, pl.pos, pl.street, pl.city, pl.country
  FROM struct_demo, struct_demo.places_lived AS pl;

SELECT id, name, places_lived.pos, places_lived.street, places_lived.city, places_lived.country
  FROM struct_demo, struct_demo.places_lived;

SELECT id, name, pos, street, city, country
  FROM struct_demo, struct_demo.places_lived;

SELECT id, name, memorable_moments.key,
  memorable_moments.value.year,
  memorable_moments.value.place,
  memorable_moments.value.details
FROM struct_demo, struct_demo.memorable_moments
WHERE memorable_moments.key IN ('Birthday','Anniversary','Graduation');

SELECT id, name, mm.key, mm.value.year, mm.value.place, mm.value.details
  FROM struct_demo, struct_demo.memorable_moments AS mm
WHERE mm.key IN ('Birthday','Anniversary','Graduation');

SELECT id, name, memorable_moments.key, memorable_moments.value.year,
  memorable_moments.value.place, memorable_moments.value.details
FROM struct_demo, struct_demo.memorable_moments
WHERE key IN ('Birthday','Anniversary','Graduation');

SELECT id, name, key, value.year, value.place, value.details
  FROM struct_demo, struct_demo.memorable_moments
WHERE key IN ('Birthday','Anniversary','Graduation');

SELECT id, name, key, year, place, details
  FROM struct_demo, struct_demo.memorable_moments
WHERE key IN ('Birthday','Anniversary','Graduation');

SELECT id, name,
  current_address.street_address.street_number,
  current_address.street_address.street_name,
  current_address.street_address.street_type,
  current_address.country,
  current_address.postal_code
FROM struct_demo;

For example, this table uses a struct that encodes several data values for each phone number associated with a person. Each person can have a variable-length array of associated phone numbers, and queries can refer to the category field to locate specific home, work, mobile, and so on kinds of phone numbers.

CREATE TABLE contact_info_many_structs
(
  id BIGINT, name STRING,
  phone_numbers ARRAY < STRUCT <category:STRING, country_code:STRING, area_code:SMALLINT, full_number:STRING, mobile:BOOLEAN, carrier:STRING > >
) STORED AS PARQUET;

Because structs are naturally suited to composite values where the fields have different data types, you might use them to decompose things such as addresses:

CREATE TABLE contact_info_detailed_address
(
  id BIGINT, name STRING,
  address STRUCT < house_number:INT, street:STRING, street_type:STRING, apartment:STRING, city:STRING, region:STRING, country:STRING >
);

In a big data context, splitting out data fields such as the number part of the address and the street name could let you do analysis on each field independently. For example, which streets have the largest number range of addresses, what are the statistical properties of the street names, which areas have a higher proportion of "Roads", "Courts" or "Boulevards", and so on.

Related information:

Complex Types (CDH 5.5 or higher only), ARRAY Complex Type (CDH 5.5 or higher only), MAP Complex Type (CDH 5.5 or higher only)

STRING

TIMESTAMP