Query variant data

Άρθρο
12/03/2024

Important

This article describes the Databricks SQL operators you can use to query and transform semi-structured data stored as VARIANT. The VARIANT data type is available in Databricks Runtime 15.3 and above.

Databricks recommends using VARIANT over JSON strings. For users currently using JSON strings looking to migrate, see How is variant different than JSON strings?.

If you want to see examples for querying semi-structured data stored with JSON strings, see Query JSON strings.

Note

VARIANT columns cannot be used for clustering keys, partitions, or Z-order keys. The VARIANT data type cannot be used for comparisons, grouping, ordering, and set operations. For a full list of limitations, see Limitations.

Create a table with a variant column

Run the following query to create a table with highly nested data stored as VARIANT. The examples in this article all reference this table.

CREATE TABLE store_data AS
SELECT parse_json(
  '{
    "store":{
        "fruit": [
          {"weight":8,"type":"apple"},
          {"weight":9,"type":"pear"}
        ],
        "basket":[
          [1,2,{"b":"y","a":"x"}],
          [3,4],
          [5,6]
        ],
        "book":[
          {
            "author":"Nigel Rees",
            "title":"Sayings of the Century",
            "category":"reference",
            "price":8.95
          },
          {
            "author":"Herman Melville",
            "title":"Moby Dick",
            "category":"fiction",
            "price":8.99,
            "isbn":"0-553-21311-3"
          },
          {
            "author":"J. R. R. Tolkien",
            "title":"The Lord of the Rings",
            "category":"fiction",
            "reader":[
              {"age":25,"name":"bob"},
              {"age":26,"name":"jack"}
            ],
            "price":22.99,
            "isbn":"0-395-19395-8"
          }
        ],
        "bicycle":{
          "price":19.95,
          "color":"red"
        }
      },
      "owner":"amy",
      "zip code":"94025",
      "fb:testid":"1234"
  }'
) as raw

Query fields in a variant column

The syntax for querying JSON strings and other complex data types on Azure Databricks applies to VARIANT data, including the following:

Use : to select top level fields.
Use . or [<key>] to select nested fields with named keys.
Use [<index>] to select values from arrays.

Note

If a field name contains a period (.), you must escape it with square brackets ([ ]). For example, the following query selects a field named zip.code:

SELECT raw:['zip.code'] FROM store_data

Extract a top-level variant field

To extract a field, specify the name of the JSON field in your extraction path. Field names are always case sensitive.

SELECT raw:owner FROM store_data

+-------+
| owner |
+-------+
| "amy" |
+-------+

-- Use backticks to escape special characters.
SELECT raw:`zip code`, raw:`fb:testid` FROM store_data

+----------+-----------+
| zip code | fb:testid |
+----------+-----------+
| "94025"  | "1234"    |
+----------+-----------+

If a path cannot be found, the result is NULL of type VARIANT.

Extract variant nested fields

You specify nested fields through dot notation or using brackets. Field names are always case sensitive.

-- Use dot notation
SELECT raw:store.bicycle FROM store_data

+------------------+
| bicycle          |
+------------------+
| {                |
|   "color":"red", |
|   "price":19.95  |
| }                |
+------------------+

-- Use brackets
SELECT raw:store['bicycle'] FROM store_data

+------------------+
| bicycle          |
+------------------+
| {                |
|   "color":"red", |
|   "price":19.95  |
| }                |
+------------------+

If a path cannot be found, the result is NULL of type VARIANT.

Extract values from variant arrays

You index elements in arrays with brackets. Indices are 0-based.

-- Index elements
SELECT raw:store.fruit[0], raw:store.fruit[1] FROM store_data

+-------------------+------------------+
| fruit             | fruit            |
+-------------------+------------------+
| {                 | {                |
|   "type":"apple", |   "type":"pear", |
|   "weight":8      |   "weight":9     |
| }                 | }                |
+-------------------+------------------+

If the path cannot be found, or if the array-index is out of bounds, the result is NULL.

Flatten variant objects and arrays

The variant_explode table-valued generator function can be used to flatten VARIANT arrays and objects.

Because variant_explode is a generator function, you use it as part of the FROM clause rather than in the SELECT list, as in the following examples:

SELECT key, value
  FROM store_data,
  LATERAL variant_explode(store_data.raw:store);

+-------+--------------------+
|    key|               value|
+-------+--------------------+
| basket|[[1,2,{"a":"x","b...|
|bicycle|{"color":"red","p...|
|   book|[{"author":"Nigel...|
|  fruit|[{"type":"apple",...|
+-------+--------------------+

SELECT pos, value
  FROM store_data,
  LATERAL variant_explode(store_data.raw:store.basket[0]);

+---+-----------------+
|pos|            value|
+---+-----------------+
|  0|                1|
|  1|                2|
|  2|{"a":"x","b":"y"}|
+---+-----------------+

Variant type casting rules

You can store arrays and scalars using VARIANT type. When trying to cast variant types to other types, normal casting rules apply for individual values and fields, with the following additional rules.

Note

variant_get and try_variant_get take type arguments and follow these casting rules.

Source type	Behavior
`VOID`	The result is a `NULL` of type `VARIANT`.
`ARRAY<elementType>`	The `elementType` must be a type that can be cast to `VARIANT`.

When inferring type with schema_of_variant or schema_of_variant_agg, functions fall back to VARIANT type rather than STRING type when conflicting types are present that can’t be resolved.

You can use :: or cast to cast values to supported data types.

-- price is returned as a double, not a string
SELECT raw:store.bicycle.price::double FROM store_data

+------------------+
| price            |
+------------------+
| 19.95            |
+------------------+

-- cast into more complex types
SELECT cast(raw:store.bicycle AS STRUCT<price DOUBLE, color STRING>) bicycle FROM store_data;
-- `::` also supported
SELECT raw:store.bicycle::STRUCT<price DOUBLE, color STRING> bicycle FROM store_data;

+------------------+
| bicycle          |
+------------------+
| {                |
|   "price":19.95, |
|   "color":"red"  |
| }                |
+------------------+

Variant null rules

Variants can contain two kinds of nulls:

SQL NULL: SQL NULLs indicate that the value is missing. These are the same NULLs as when dealing with structured data.
Variant NULL: Variant NULLs indicate that the variant explicitly contains a NULL value. These are not the same as SQL NULLs, because the NULL value is stored in the data.

Use the is_variant_null function to determine if the variant value is a variant NULL.

SELECT
  is_variant_null(parse_json(NULL)) AS sql_null,
  is_variant_null(parse_json('null')) AS variant_null,
  is_variant_null(parse_json('{ "field_a": null }'):field_a) AS variant_null_value,
  is_variant_null(parse_json('{ "field_a": null }'):missing) AS missing_sql_value_null

+--------+------------+------------------+----------------------+
|sql_null|variant_null|variant_null_value|missing_sql_value_null|
+--------+------------+------------------+----------------------+
|   false|        true|              true|                 false|
+--------+------------+------------------+----------------------+

Κοινή χρήση μέσω

Query variant data

Create a table with a variant column

Query fields in a variant column

Extract a top-level variant field

Extract variant nested fields

Extract values from variant arrays

Flatten variant objects and arrays

Variant type casting rules

Variant null rules

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