GeezerVison

| `UnixTimestamp` | `datetime` | Unix timestamp (nullable) |

| `Uuid` | `uuid.UUID` | UUID (any version) |

| `TimeUuid` | `uuid.UUID` | Time-based UUID (version 1) |

| `Decimal` | `decimal.Decimal` | Arbitrary-precision decimal number |

| `Array(UnixTimestamp)` | `List[datetime]` | List of timestamps |

| `Array(Uuid)` | `List[uuid.UUID]` | List of UUIDs |

| `Array(TimeUuid)` | `List[uuid.UUID]` | List of time-based UUIDs |

| `Array(Decimal)` | `List[decimal.Decimal]` | List of arbitrary-precision decimals |

| `Set(UnixTimestamp)` | `Set[datetime]` | Set of unique timestamps |

| `Set(Uuid)` | `Set[uuid.UUID]` | Set of unique UUIDs |

| `Set(TimeUuid)` | `Set[uuid.UUID]` | Set of unique time-based UUIDs |

| `Set(Decimal)` | `Set[decimal.Decimal]` | Set of unique arbitrary-precision decimals |

**Note:** Set types automatically remove duplicate values. When converting from lists or other iterables to sets, duplicates are eliminated.

from feast import Entity, FeatureView, Field, FileSource

from feast.types import (

Int32, Int64, Float32, Float64, String, Bytes, Bool, UnixTimestamp,

Uuid, TimeUuid, Array, Set, Map, Json, Struct

Uuid, TimeUuid, Decimal, Array, Set, Map, Json, Struct

)

Field(name="last_login", dtype=UnixTimestamp),

Field(name="session_id", dtype=Uuid),

Field(name="event_id", dtype=TimeUuid),

Field(name="price", dtype=Decimal),

# Array types

Field(name="daily_steps", dtype=Array(Int32)),

Field(name="login_timestamps", dtype=Array(UnixTimestamp)),

Field(name="related_session_ids", dtype=Array(Uuid)),

Field(name="event_chain", dtype=Array(TimeUuid)),

Field(name="historical_prices", dtype=Array(Decimal)),

# Set types (unique values only — see backend caveats above)

Field(name="visited_pages", dtype=Set(String)),

Field(name="preferred_languages", dtype=Set(String)),

Field(name="unique_device_ids", dtype=Set(Uuid)),

Field(name="unique_event_ids", dtype=Set(TimeUuid)),

Field(name="unique_prices", dtype=Set(Decimal)),

# Map types

Field(name="user_preferences", dtype=Map),

unique_devices = {uuid.uuid4(), uuid.uuid4()}

The `Decimal` type stores arbitrary-precision decimal numbers using Python's `decimal.Decimal`.

Values are stored as strings in the proto to preserve full precision — no floating-point rounding occurs.

import decimal

price = decimal.Decimal("19.99")

tax_rate = decimal.Decimal("0.08750000000000000000")

response = store.get_online_features(

features=["product_features:price"],

entity_rows=[{"product_id": 42}],

)

result = response.to_dict()

historical_prices = [

decimal.Decimal("18.50"),

decimal.Decimal("19.00"),

decimal.Decimal("19.99"),

]

unique_prices = {decimal.Decimal("9.99"), decimal.Decimal("19.99"), decimal.Decimal("29.99")}

Nested collections allow storing multi-dimensional data with unlimited depth:

{% hint style="warning" %}

`Decimal` is **not** inferred from any backend schema. You must declare it explicitly in your feature view schema. The pandas dtype for `Decimal` columns is `object` (holding `decimal.Decimal` instances), not a numeric dtype.

{% endhint %}

* `source_datatype_to_feast_value_type` calls the appropriate method in `type_map.py`. For example, if a `SnowflakeSource` is being examined, `snowflake_python_type_to_feast_value_type` from `type_map.py` will be called.

{% hint style="info" %}

**Types that cannot be inferred:** `Set`, `Json`, `Struct`, `PdfBytes`, and `ImageBytes` types are never inferred from backend schemas. If you use these types, you must declare them explicitly in your feature view schema.

**Types that cannot be inferred:** `Set`, `Json`, `Struct`, `Decimal`, `PdfBytes`, and `ImageBytes` types are never inferred from backend schemas. If you use these types, you must declare them explicitly in your feature view schema.

{% endhint %}

TIME_UUID_SET = 41;

VALUE_LIST = 42;

VALUE_SET = 43;

DECIMAL = 44;

DECIMAL_LIST = 45;

DECIMAL_SET = 46;

}

}

StringSet time_uuid_set_val = 41;

RepeatedValue list_val = 42;

RepeatedValue set_val = 43;

string decimal_val = 44;

StringList decimal_list_val = 45;

StringSet decimal_set_val = 46;

}

}