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Copy file name to clipboardExpand all lines: .github/workflows/publish_images.yml
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required: true
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default: ""
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type: string
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ref:
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description: 'Git ref to checkout (branch, tag, or SHA). Defaults to the triggering ref. Use to rebuild images from a different branch (e.g., master) when the release tag has a broken Dockerfile.'
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required: false
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type: string
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workflow_call: # Allows trigger of the workflow from another workflow
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inputs:
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custom_version: # Optional input for a custom version
Copy file name to clipboardExpand all lines: docs/getting-started/concepts/feast-types.md
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-**UUID types**: `Uuid` and `TimeUuid` for universally unique identifiers. Stored as strings at the proto level but deserialized to `uuid.UUID` objects in Python.
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-**Array types**: ordered lists of any primitive type, e.g. `Array(Int64)`, `Array(String)`, `Array(Uuid)`.
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-**Set types**: unordered collections of unique values for any primitive type, e.g. `Set(String)`, `Set(Int64)`. Set types are not inferred by any backend and must be explicitly declared. They are best suited for online serving use cases.
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-**Map types**: dictionary-like structures with string keys and values that can be any supported Feast type (including nested maps), e.g. `Map`, `Array(Map)`.
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-**Map types**: dictionary-like structures. `Map` has string keys and values that can be any supported Feast type (including nested maps), e.g. `Map`, `Array(Map)`. `ScalarMap` has non-string scalar keys (int, float, bool, UUID, Decimal, bytes, datetime) — Feast infers `ScalarMap` automatically when the first key is not a string. `ScalarMap` must be explicitly declared in schema and is not inferred by any backend.
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-**JSON type**: opaque JSON data stored as a string at the proto level but semantically distinct from `String` — backends use native JSON types (`jsonb`, `VARIANT`, etc.), e.g. `Json`, `Array(Json)`.
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-**Struct type**: schema-aware structured type with named, typed fields. Unlike `Map` (which is schema-free), a `Struct` declares its field names and their types, enabling schema validation, e.g. `Struct({"name": String, "age": Int32})`.
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**Note**: When the backend native type is ambiguous (e.g., `jsonb` could be `Map`, `Json`, or `Struct`), the **schema-declared Feast type takes precedence**. The backend-to-Feast type mappings above are only used for schema inference when no explicit type is provided.
|`Map`|`Dict[str, Any]`| Dictionary with string keys and values of any supported Feast type (including nested maps) |
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|`Array(Map)`|`List[Dict[str, Any]]`| List of dictionaries |
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|`ScalarMap`|`Dict[Any, Any]`| Dictionary with non-string scalar keys (int, float, bool, UUID, Decimal, bytes, datetime) and values of any supported Feast type |
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**Note:** Map keys must always be strings. Map values can be any supported Feast type, including primitives, arrays, or nested maps at the proto level. However, the PyArrow representation is `map<string, string>`, which means backends that rely on PyArrow schemas (e.g., during materialization) treat Map as string-to-string.
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**Note:**`Map` keys must always be strings. `ScalarMap` supports non-string scalar keys — Feast infers `ScalarMap` automatically when the first key of a dict is not a string. Map values can be any supported Feast type, including primitives, arrays, or nested maps at the proto level. However, the PyArrow representation is `map<string, string>`, which means backends that rely on PyArrow schemas (e.g., during materialization) treat Map as string-to-string.
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{% hint style="warning" %}
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`ScalarMap` is **not** inferred from any backend schema. You must declare it explicitly in your feature view schema. It is best suited for online serving use cases where the online store serializes proto bytes directly (e.g., Redis, DynamoDB, SQLite).
python_type_to_feast_value_type({}) # → ValueType.MAP (empty dict defaults to MAP)
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```
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{% hint style="warning" %}
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`ScalarMap` must be **explicitly declared** in your feature view schema — it is never inferred from backend type schemas. It is best suited for online serving via stores that use proto byte serialization (e.g., Redis, DynamoDB, SQLite). Materialization paths that use PyArrow (e.g., BigQuery, Snowflake, Redshift, Spark) do not have native `ScalarMap` support.
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{% endhint %}
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### JSON Type Usage Examples
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Feast's `Json` type stores values as JSON strings at the proto level. You can pass either a
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*`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.
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{% hint style="info" %}
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**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.
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**Types that cannot be inferred:**`Set`, `Json`, `Struct`, `Decimal`, `ScalarMap`, `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.
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