Reference

Types

SQLLoad's portable column types, how each maps to Snowflake, Databricks, DuckDB and Parquet, and how source types are read.

SQLLoad reasons about columns in a small set of portable types. You write them in columns, plan shows them next to each warehouse type, and one table can load into different warehouses with the same declarations.

[sources.app.tables."public.orders"]
description = "Orders, one row per order."
columns = { total = "decimal(18,2)", closes_at = "timestamp_tz" }

Portable types

Type	Snowflake	Databricks	DuckDB	Parquet (Arrow)
`bool`	`BOOLEAN`	`BOOLEAN`	`BOOLEAN`	`Boolean`
`smallint`	`NUMBER(38,0)` ¹	`SMALLINT`	`SMALLINT`	`Int16`
`int` (64-bit)	`NUMBER(38,0)`	`BIGINT`	`BIGINT`	`Int64`
`decimal(p,s)`	`NUMBER(p,s)`	`DECIMAL(p,s)`	`DECIMAL(p,s)`	`Decimal128(p,s)`
`float` (64-bit)	`FLOAT`	`DOUBLE`	`DOUBLE`	`Float64`
`text`	`VARCHAR`	`STRING`	`VARCHAR`	`Utf8`
`varchar(n)`	`VARCHAR(n)`	`STRING` ¹	`VARCHAR` ¹	`Utf8`
`date`	`DATE`	`DATE`	`DATE`	`Date32`
`time`	`TIME(9)`	`STRING` ¹	`TIME`	`Time64`
`timestamp_tz`	`TIMESTAMP_TZ(9)`	`TIMESTAMP` ¹	`TIMESTAMP WITH TIME ZONE` ¹	`Timestamp(UTC)`
`timestamp_ntz`	`TIMESTAMP_NTZ(9)`	`TIMESTAMP_NTZ`	`TIMESTAMP`	`Timestamp`
`json`	`VARIANT`	`VARIANT`	`JSON`	`Utf8`
`uuid`	`VARCHAR(36)` ¹	`STRING` ¹	`UUID`	`Utf8`
`binary`	`BINARY`	`BINARY`	`BLOB`	`Binary`
`native("...")`	passed through	passed through	passed through	not available

¹ Held with a loss, which plan notes: Snowflake stores smallint as a 38-digit integer and uuid as text; Databricks doesn’t enforce varchar lengths, stores time and uuid as text, and keeps timestamp_tz as an instant without its offset; DuckDB doesn’t enforce varchar lengths, and stores timestamp_tz as a UTC instant without its offset.

native("GEOGRAPHY") passes a warehouse-only type through unchanged. SQLLoad doesn’t widen or convert such columns.

Names SQLLoad accepts

Type names are parsed strictly. An unknown or malformed name fails plan with the nearest valid one.

Written	Means
`integer`, `bigint`	`int`
`boolean`	`bool`
`string`, `varchar`, `character varying`	`text`
`double`, `double precision`	`float`
`numeric(p,s)`	`decimal(p,s)`
`timestamp with time zone`, `timestamp without time zone`, `datetime`	`timestamp_tz`, `timestamp_ntz`, `timestamp_ntz`
`varbinary`	`binary`

Warehouse-specific names, such as Snowflake’s NUMBER or VARIANT or Postgres’s jsonb, are accepted only when every destination of the table is that warehouse.

Some names are rejected because they mean different things in different places:

Name	Use instead
`timestamp`	`timestamp_tz` or `timestamp_ntz`
`timestamp_ltz`	`timestamp_tz` (it depends on each session’s time zone)
`real`	`float` (64-bit)
`tinyint`	`smallint`
`char`, `character`	`text` or `varchar(n)` (they pad with spaces)

decimal needs a precision and a scale, such as decimal(18,2).

Postgres source types

Postgres	Portable type
`boolean`	`bool`
`smallint`	`smallint`
`integer`, `bigint`, `oid`	`int`
`real`, `double precision`	`float`
`numeric(p,s)`	`decimal(p,s)`
`numeric` without a precision	Pin a type, such as `decimal(38,10)` or `text`; `plan` asks for one.
`text`	`text`
`varchar(n)`	`varchar(n)`
`date`, `time`	`date`, `time`
`timestamp`, `timestamptz`	`timestamp_ntz`, `timestamp_tz`
`json`, `jsonb`	`json`
`uuid`	`uuid`
`bytea`	`binary`
arrays	`json`
other types (enums, `interval`, network types, `timetz`, extension types)	`text`

Columns pinned in columns are read as the pinned type, when the source type can be read that way.

Python and files

Python values are typed as described in Python sources. Parquet columns keep their Arrow types. CSV and JSON columns are inferred from a sample of records; pin any you want typed differently.

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