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The VARIANT type stores typed, binary data where each row is self-contained with its own type information. This differs from the JSON type, which is physically stored as text. Because type metadata is embedded per-value, VARIANT provides better compression and query performance than JSON for semi-structured data.
The VARIANT type is inspired by Snowflake's semi-structured VARIANT data type. It is available in Parquet since 2025 and also supported by DuckDB's Parquet reader.
Examples
Storing Different Types in the Same Column
A VARIANT column can hold values of different types across rows:
CREATE TABLE events (id INTEGER, data VARIANT);
INSERT INTO events VALUES
(1, 42::VARIANT),
(2, 'hello world'::VARIANT),
(3, [1, 2, 3]::VARIANT),
(4, {'name': 'Alice', 'age': 30}::VARIANT);
SELECT * FROM events;
┌───────┬────────────────────────────┐
│ id │ data │
│ int32 │ variant │
├───────┼────────────────────────────┤
│ 1 │ 42 │
│ 2 │ hello world │
│ 3 │ [1, 2, 3] │
│ 4 │ {'name': Alice, 'age': 30} │
└───────┴────────────────────────────┘
Checking the Type of a Value
Use variant_typeof to inspect the underlying type of each row:
SELECT id, data, variant_typeof(data) AS vtype
FROM events;
┌───────┬────────────────────────────┬───────────────────┐
│ id │ data │ vtype │
│ int32 │ variant │ varchar │
├───────┼────────────────────────────┼───────────────────┤
│ 1 │ 42 │ INT32 │
│ 2 │ hello world │ VARCHAR │
│ 3 │ [1, 2, 3] │ ARRAY(3) │
│ 4 │ {'name': Alice, 'age': 30} │ OBJECT(name, age) │
└───────┴────────────────────────────┴───────────────────┘
Extracting Fields from Nested Variants
Fields can be extracted from nested VARIANT values using dot notation or the variant_extract function:
SELECT data.name FROM events WHERE id = 4;
SELECT variant_extract(data, 'name') AS name FROM events WHERE id = 4;
┌─────────┐
│ name │
│ variant │
├─────────┤
│ Alice │
└─────────┘
Parquet Support
DuckDB supports reading and writing VARIANT types from Parquet files, including shredding, a technique that stores nested data as flat values for more efficient access.
Writing VARIANT to Parquet
When writing VARIANT columns to Parquet, DuckDB can automatically shred (decompose) the variant data into typed columns based on the structure of the first row group. This auto-shredding improves read performance by enabling predicate pushdown and efficient column access.
To explicitly provide a schema for shredding, use the SHREDDING copy option:
COPY events TO 'events.parquet' (
FORMAT parquet,
SHREDDING {'data': 'STRUCT(name VARCHAR, age INTEGER)'}
);
Reading Snowflake VARIANT from Parquet
DuckDB can read shredded VARIANT Parquet files produced by Snowflake, automatically reconstructing the variant values from the shredded columns.