Feature Spec¶
Feature specs act as source of truth for all metadata related to features: their dependencies, fields, code versions, and so on.
pydantic-model
¶
FeatureSpec(*, key: CoercibleToFeatureKey, id_columns: IDColumns, deps: list[FeatureDep] | None = None, fields: Sequence[str | FieldSpec] | None = None, lineage: LineageRelationship | None = None, metadata: Mapping[str, JsonValue] | None = None, **kwargs: Any)
FeatureSpec(*, key: CoercibleToFeatureKey, id_columns: IDColumns, deps: list[FeatureDep] | list[CoercibleToFeatureDep] | None = None, fields: Sequence[str | FieldSpec] | None = None, lineage: LineageRelationship | None = None, metadata: Mapping[str, JsonValue] | None = None, **kwargs: Any)
Bases: FrozenBaseModel
Show JSON schema:
{
"$defs": {
"AggregationRelationship": {
"description": "Many-to-one relationship where multiple parent rows aggregate to one child row.\n\nParent features have more granular ID columns than the child. The child aggregates\nmultiple parent rows by grouping on a subset of the parent's ID columns.\n\nAttributes:\n on: Columns to group by for aggregation. These should be a subset of the\n target feature's ID columns. If not specified, uses all target ID columns.\n\nExamples:\n >>> # Aggregate sensor readings by hour\n >>> AggregationRelationship(on=[\"sensor_id\", \"hour\"])\n >>> # Parent has: sensor_id, hour, minute\n >>> # Child has: sensor_id, hour\n\n >>> # Or use the classmethod\n >>> LineageRelationship.aggregation(on=[\"user_id\", \"session_id\"])",
"properties": {
"type": {
"const": "N:1",
"default": "N:1",
"title": "Type",
"type": "string"
},
"on": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Columns to group by for aggregation. Defaults to all target ID columns.",
"title": "On"
}
},
"title": "AggregationRelationship",
"type": "object"
},
"AllFieldsMapping": {
"description": "Field mapping that explicitly depends on all upstream fields.",
"properties": {
"type": {
"const": "all",
"default": "all",
"title": "Type",
"type": "string"
}
},
"title": "AllFieldsMapping",
"type": "object"
},
"DefaultFieldsMapping": {
"description": "Default automatic field mapping configuration.\n\nWhen used, automatically maps fields to matching upstream fields based on field keys.\n\nAttributes:\n match_suffix: If True, allows suffix matching (e.g., \"french\" matches \"audio/french\")\n exclude_fields: List of field keys to exclude from auto-mapping",
"properties": {
"type": {
"const": "default",
"default": "default",
"title": "Type",
"type": "string"
},
"match_suffix": {
"default": false,
"title": "Match Suffix",
"type": "boolean"
},
"exclude_fields": {
"items": {
"$ref": "#/$defs/FieldKey"
},
"title": "Exclude Fields",
"type": "array"
}
},
"title": "DefaultFieldsMapping",
"type": "object"
},
"ExpansionRelationship": {
"description": "One-to-many relationship where one parent row expands to multiple child rows.\n\nChild features have more granular ID columns than the parent. Each parent row\ngenerates multiple child rows with additional ID columns.\n\nAttributes:\n on: Parent ID columns that identify the parent record. Child records with\n the same parent IDs will share the same upstream provenance.\n If not specified, will be inferred from the available columns.\n id_generation_pattern: Optional pattern for generating child IDs.\n Can be \"sequential\", \"hash\", or a custom pattern. If not specified,\n the feature's load_input() method is responsible for ID generation.\n\nExamples:\n >>> # Video frames from video\n >>> ExpansionRelationship(\n ... on=[\"video_id\"], # Parent ID\n ... id_generation_pattern=\"sequential\"\n ... )\n >>> # Parent has: video_id\n >>> # Child has: video_id, frame_id (generated)\n\n >>> # Text chunks from document\n >>> ExpansionRelationship(on=[\"doc_id\"])\n >>> # Parent has: doc_id\n >>> # Child has: doc_id, chunk_id (generated in load_input)",
"properties": {
"type": {
"const": "1:N",
"default": "1:N",
"title": "Type",
"type": "string"
},
"on": {
"description": "Parent ID columns for grouping. Child records with same parent IDs share provenance. Required for expansion relationships.",
"items": {
"type": "string"
},
"title": "On",
"type": "array"
},
"id_generation_pattern": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Pattern for generating child IDs. If None, handled by load_input().",
"title": "Id Generation Pattern"
}
},
"required": [
"on"
],
"title": "ExpansionRelationship",
"type": "object"
},
"FeatureDep": {
"description": "Feature dependency specification with optional column selection and renaming.\n\nAttributes:\n key: The feature key to depend on. Accepts string (\"a/b/c\"), list ([\"a\", \"b\", \"c\"]),\n FeatureKey instance, or BaseFeature class.\n columns: Optional tuple of column names to select from upstream feature.\n - None (default): Keep all columns from upstream\n - Empty tuple (): Keep only system columns (sample_uid, provenance_by_field, etc.)\n - Tuple of names: Keep only specified columns (plus system columns)\n rename: Optional mapping of old column names to new names.\n Applied after column selection.\n fields_mapping: Optional field mapping configuration for automatic field dependency resolution.\n When provided, fields without explicit deps will automatically map to matching upstream fields.\n Defaults to using `[FieldsMapping.default()][metaxy.models.fields_mapping.DefaultFieldsMapping]`.\n filters: Optional SQL-like filter strings applied to this dependency. Automatically parsed into\n Narwhals expressions (accessible via the `filters` property). Filters are automatically\n applied by FeatureDepTransformer after renames during all FeatureDep operations (including\n resolve_update and version computation).\n\nExamples:\n ```py\n # Keep all columns with default field mapping\n FeatureDep(feature=\"upstream\")\n\n # Keep all columns with suffix matching\n FeatureDep(feature=\"upstream\", fields_mapping=FieldsMapping.default(match_suffix=True))\n\n # Keep all columns with all fields mapping\n FeatureDep(feature=\"upstream\", fields_mapping=FieldsMapping.all())\n\n # Keep only specific columns\n FeatureDep(\n feature=\"upstream/feature\",\n columns=(\"col1\", \"col2\")\n )\n\n # Rename columns to avoid conflicts\n FeatureDep(\n feature=\"upstream/feature\",\n rename={\"old_name\": \"new_name\"}\n )\n\n # Select and rename\n FeatureDep(\n feature=\"upstream/feature\",\n columns=(\"col1\", \"col2\"),\n rename={\"col1\": \"upstream_col1\"}\n )\n\n # SQL filters\n FeatureDep(\n feature=\"upstream\",\n filters=[\"age >= 25\", \"status = 'active'\"]\n )\n ```",
"properties": {
"feature": {
"$ref": "#/$defs/FeatureKey",
"description": "Feature key. Accepts a slashed string ('a/b/c'), a sequence of strings, a FeatureKey instance, or a child class of BaseFeature"
},
"columns": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"title": "Columns"
},
"rename": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"title": "Rename"
},
"fields_mapping": {
"$ref": "#/$defs/FieldsMapping"
},
"filters": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "SQL-like filter strings applied to this dependency.",
"title": "Filters"
}
},
"required": [
"feature"
],
"title": "FeatureDep",
"type": "object"
},
"FeatureKey": {
"description": "Feature key as a sequence of string parts.\n\nHashable for use as dict keys in registries.\nParts cannot contain forward slashes (/) or double underscores (__).\n\nExample:\n\n ```py\n FeatureKey(\"a/b/c\") # String format\n # FeatureKey(parts=['a', 'b', 'c'])\n\n FeatureKey([\"a\", \"b\", \"c\"]) # List format\n # FeatureKey(parts=['a', 'b', 'c'])\n\n FeatureKey(FeatureKey([\"a\", \"b\", \"c\"])) # FeatureKey copy\n # FeatureKey(parts=['a', 'b', 'c'])\n ```",
"items": {
"type": "string"
},
"title": "FeatureKey",
"type": "array"
},
"FieldDep": {
"properties": {
"feature": {
"$ref": "#/$defs/FeatureKey"
},
"fields": {
"anyOf": [
{
"items": {
"$ref": "#/$defs/FieldKey"
},
"type": "array"
},
{
"const": "__METAXY_ALL_DEP__",
"type": "string"
}
],
"default": "__METAXY_ALL_DEP__",
"title": "Fields"
}
},
"required": [
"feature"
],
"title": "FieldDep",
"type": "object"
},
"FieldKey": {
"description": "Field key as a sequence of string parts.\n\nHashable for use as dict keys in registries.\nParts cannot contain forward slashes (/) or double underscores (__).\n\nExample:\n\n ```py\n FieldKey(\"a/b/c\") # String format\n # FieldKey(parts=['a', 'b', 'c'])\n\n FieldKey([\"a\", \"b\", \"c\"]) # List format\n # FieldKey(parts=['a', 'b', 'c'])\n\n FieldKey(FieldKey([\"a\", \"b\", \"c\"])) # FieldKey copy\n # FieldKey(parts=['a', 'b', 'c'])\n ```",
"items": {
"type": "string"
},
"title": "FieldKey",
"type": "array"
},
"FieldsMapping": {
"description": "Base class for field mapping configurations.\n\nField mappings define how a field automatically resolves its dependencies\nbased on upstream feature fields. This is separate from explicit field\ndependencies which are defined directly.",
"properties": {
"mapping": {
"discriminator": {
"mapping": {
"all": "#/$defs/AllFieldsMapping",
"default": "#/$defs/DefaultFieldsMapping",
"none": "#/$defs/NoneFieldsMapping",
"specific": "#/$defs/SpecificFieldsMapping"
},
"propertyName": "type"
},
"oneOf": [
{
"$ref": "#/$defs/AllFieldsMapping"
},
{
"$ref": "#/$defs/SpecificFieldsMapping"
},
{
"$ref": "#/$defs/NoneFieldsMapping"
},
{
"$ref": "#/$defs/DefaultFieldsMapping"
}
],
"title": "Mapping"
}
},
"required": [
"mapping"
],
"title": "FieldsMapping",
"type": "object"
},
"IdentityRelationship": {
"description": "One-to-one relationship where each child row maps to exactly one parent row.\n\nThis is the default relationship type. Parent and child features share the same\nID columns and have the same cardinality. No aggregation is performed.\n\nExamples:\n >>> # Default 1:1 relationship\n >>> IdentityRelationship()\n\n >>> # Or use the classmethod\n >>> LineageRelationship.identity()",
"properties": {
"type": {
"const": "1:1",
"default": "1:1",
"title": "Type",
"type": "string"
}
},
"title": "IdentityRelationship",
"type": "object"
},
"JsonValue": {},
"LineageRelationship": {
"description": "Wrapper class for lineage relationship configurations with convenient constructors.\n\nThis provides a cleaner API for creating lineage relationships while maintaining\ntype safety through discriminated unions.",
"properties": {
"relationship": {
"discriminator": {
"mapping": {
"1:1": "#/$defs/IdentityRelationship",
"1:N": "#/$defs/ExpansionRelationship",
"N:1": "#/$defs/AggregationRelationship"
},
"propertyName": "type"
},
"oneOf": [
{
"$ref": "#/$defs/IdentityRelationship"
},
{
"$ref": "#/$defs/AggregationRelationship"
},
{
"$ref": "#/$defs/ExpansionRelationship"
}
],
"title": "Relationship"
}
},
"required": [
"relationship"
],
"title": "LineageRelationship",
"type": "object"
},
"NoneFieldsMapping": {
"description": "Field mapping that never matches any upstream fields.",
"properties": {
"type": {
"const": "none",
"default": "none",
"title": "Type",
"type": "string"
}
},
"title": "NoneFieldsMapping",
"type": "object"
},
"SpecialFieldDep": {
"enum": [
"__METAXY_ALL_DEP__"
],
"title": "SpecialFieldDep",
"type": "string"
},
"SpecificFieldsMapping": {
"description": "Field mapping that explicitly depends on specific upstream fields.",
"properties": {
"type": {
"const": "specific",
"default": "specific",
"title": "Type",
"type": "string"
},
"mapping": {
"additionalProperties": {
"items": {
"$ref": "#/$defs/FieldKey"
},
"type": "array",
"uniqueItems": true
},
"propertyNames": {
"$ref": "#/$defs/FieldKey"
},
"title": "Mapping",
"type": "object"
}
},
"required": [
"mapping"
],
"title": "SpecificFieldsMapping",
"type": "object"
}
},
"properties": {
"key": {
"$ref": "#/$defs/FeatureKey"
},
"id_columns": {
"description": "Columns that uniquely identify a sample in this feature.",
"items": {
"type": "string"
},
"title": "Id Columns",
"type": "array"
},
"deps": {
"items": {
"$ref": "#/$defs/FeatureDep"
},
"title": "Deps",
"type": "array"
},
"fields": {
"items": {
"properties": {
"key": {
"$ref": "#/$defs/FieldKey"
},
"code_version": {
"default": "__metaxy_initial__",
"title": "Code Version",
"type": "string"
},
"deps": {
"anyOf": [
{
"$ref": "#/$defs/SpecialFieldDep"
},
{
"items": {
"$ref": "#/$defs/FieldDep"
},
"type": "array"
}
],
"title": "Deps"
}
},
"title": "FieldSpec",
"type": "object"
},
"title": "Fields",
"type": "array"
},
"lineage": {
"$ref": "#/$defs/LineageRelationship",
"description": "Lineage relationship of this feature."
},
"metadata": {
"additionalProperties": {
"$ref": "#/$defs/JsonValue"
},
"description": "Metadata attached to this feature.",
"title": "Metadata",
"type": "object"
}
},
"required": [
"key",
"id_columns"
],
"title": "FeatureSpec",
"type": "object"
}
Fields:
-
key(FeatureKey) -
id_columns(tuple[str, ...]) -
deps(list[FeatureDep]) -
fields(list[FieldSpec]) -
lineage(LineageRelationship) -
metadata(dict[str, JsonValue])
Validators:
Source code in src/metaxy/models/feature_spec.py
def __init__( # pyright: ignore[reportMissingSuperCall]
self,
*,
key: CoercibleToFeatureKey,
id_columns: IDColumns,
deps: list[FeatureDep] | list[CoercibleToFeatureDep] | None = None,
fields: Sequence[str | FieldSpec] | None = None,
lineage: LineageRelationship | None = None,
metadata: Mapping[str, JsonValue] | None = None,
**kwargs: Any,
) -> None: ... # pyright: ignore[reportMissingSuperCall]
Attributes¶
pydantic-field
¶
Columns that uniquely identify a sample in this feature.
pydantic-field
¶
lineage: LineageRelationship
Lineage relationship of this feature.
pydantic-field
¶
Metadata attached to this feature.
cached
property
¶
code_version: str
Hash of this feature's field code_versions only (no dependencies).
property
¶
feature_spec_version: str
Compute SHA256 hash of the complete feature specification.
This property provides a deterministic hash of ALL specification properties, including key, deps, fields, and any metadata/tags. Used for audit trail and tracking specification changes.
Unlike feature_version which only hashes computational properties (for migration triggering), feature_spec_version captures the entire specification for complete reproducibility and audit purposes.
Returns:
-
str–SHA256 hex digest of the specification
Functions¶
pydantic-validator
¶
Validate that all fields have unique keys.
Source code in src/metaxy/models/feature_spec.py
@pydantic.model_validator(mode="after")
def validate_unique_field_keys(self) -> Self:
"""Validate that all fields have unique keys."""
seen_keys: set[tuple[str, ...]] = set()
for field in self.fields:
# Convert to tuple for hashability in case it's a plain list
key_tuple = tuple(field.key)
if key_tuple in seen_keys:
raise ValueError(
f"Duplicate field key found: {field.key}. "
f"All fields must have unique keys."
)
seen_keys.add(key_tuple)
return self
pydantic-validator
¶
Validate that id_columns is non-empty if specified.
Source code in src/metaxy/models/feature_spec.py
Feature Dependencies¶
pydantic-model
¶
FeatureDep(*, feature: str | Sequence[str] | FeatureKey | type[BaseFeature], columns: tuple[str, ...] | None = None, rename: dict[str, str] | None = None, fields_mapping: FieldsMapping | None = None, filters: Sequence[str] | None = None)
Bases: BaseModel
Feature dependency specification with optional column selection and renaming.
Attributes:
-
key–The feature key to depend on. Accepts string ("a/b/c"), list (["a", "b", "c"]), FeatureKey instance, or BaseFeature class.
-
columns(tuple[str, ...] | None) –Optional tuple of column names to select from upstream feature. - None (default): Keep all columns from upstream - Empty tuple (): Keep only system columns (sample_uid, provenance_by_field, etc.) - Tuple of names: Keep only specified columns (plus system columns)
-
rename(dict[str, str] | None) –Optional mapping of old column names to new names. Applied after column selection.
-
fields_mapping(FieldsMapping) –Optional field mapping configuration for automatic field dependency resolution. When provided, fields without explicit deps will automatically map to matching upstream fields. Defaults to using
[FieldsMapping.default()][metaxy.models.fields_mapping.DefaultFieldsMapping]. -
filters(tuple[Expr, ...]) –Optional SQL-like filter strings applied to this dependency. Automatically parsed into Narwhals expressions (accessible via the
filtersproperty). Filters are automatically applied by FeatureDepTransformer after renames during all FeatureDep operations (including resolve_update and version computation).
Examples:
# Keep all columns with default field mapping
FeatureDep(feature="upstream")
# Keep all columns with suffix matching
FeatureDep(feature="upstream", fields_mapping=FieldsMapping.default(match_suffix=True))
# Keep all columns with all fields mapping
FeatureDep(feature="upstream", fields_mapping=FieldsMapping.all())
# Keep only specific columns
FeatureDep(
feature="upstream/feature",
columns=("col1", "col2")
)
# Rename columns to avoid conflicts
FeatureDep(
feature="upstream/feature",
rename={"old_name": "new_name"}
)
# Select and rename
FeatureDep(
feature="upstream/feature",
columns=("col1", "col2"),
rename={"col1": "upstream_col1"}
)
# SQL filters
FeatureDep(
feature="upstream",
filters=["age >= 25", "status = 'active'"]
)
Show JSON schema:
{
"$defs": {
"AllFieldsMapping": {
"description": "Field mapping that explicitly depends on all upstream fields.",
"properties": {
"type": {
"const": "all",
"default": "all",
"title": "Type",
"type": "string"
}
},
"title": "AllFieldsMapping",
"type": "object"
},
"DefaultFieldsMapping": {
"description": "Default automatic field mapping configuration.\n\nWhen used, automatically maps fields to matching upstream fields based on field keys.\n\nAttributes:\n match_suffix: If True, allows suffix matching (e.g., \"french\" matches \"audio/french\")\n exclude_fields: List of field keys to exclude from auto-mapping",
"properties": {
"type": {
"const": "default",
"default": "default",
"title": "Type",
"type": "string"
},
"match_suffix": {
"default": false,
"title": "Match Suffix",
"type": "boolean"
},
"exclude_fields": {
"items": {
"$ref": "#/$defs/FieldKey"
},
"title": "Exclude Fields",
"type": "array"
}
},
"title": "DefaultFieldsMapping",
"type": "object"
},
"FeatureKey": {
"description": "Feature key as a sequence of string parts.\n\nHashable for use as dict keys in registries.\nParts cannot contain forward slashes (/) or double underscores (__).\n\nExample:\n\n ```py\n FeatureKey(\"a/b/c\") # String format\n # FeatureKey(parts=['a', 'b', 'c'])\n\n FeatureKey([\"a\", \"b\", \"c\"]) # List format\n # FeatureKey(parts=['a', 'b', 'c'])\n\n FeatureKey(FeatureKey([\"a\", \"b\", \"c\"])) # FeatureKey copy\n # FeatureKey(parts=['a', 'b', 'c'])\n ```",
"items": {
"type": "string"
},
"title": "FeatureKey",
"type": "array"
},
"FieldKey": {
"description": "Field key as a sequence of string parts.\n\nHashable for use as dict keys in registries.\nParts cannot contain forward slashes (/) or double underscores (__).\n\nExample:\n\n ```py\n FieldKey(\"a/b/c\") # String format\n # FieldKey(parts=['a', 'b', 'c'])\n\n FieldKey([\"a\", \"b\", \"c\"]) # List format\n # FieldKey(parts=['a', 'b', 'c'])\n\n FieldKey(FieldKey([\"a\", \"b\", \"c\"])) # FieldKey copy\n # FieldKey(parts=['a', 'b', 'c'])\n ```",
"items": {
"type": "string"
},
"title": "FieldKey",
"type": "array"
},
"FieldsMapping": {
"description": "Base class for field mapping configurations.\n\nField mappings define how a field automatically resolves its dependencies\nbased on upstream feature fields. This is separate from explicit field\ndependencies which are defined directly.",
"properties": {
"mapping": {
"discriminator": {
"mapping": {
"all": "#/$defs/AllFieldsMapping",
"default": "#/$defs/DefaultFieldsMapping",
"none": "#/$defs/NoneFieldsMapping",
"specific": "#/$defs/SpecificFieldsMapping"
},
"propertyName": "type"
},
"oneOf": [
{
"$ref": "#/$defs/AllFieldsMapping"
},
{
"$ref": "#/$defs/SpecificFieldsMapping"
},
{
"$ref": "#/$defs/NoneFieldsMapping"
},
{
"$ref": "#/$defs/DefaultFieldsMapping"
}
],
"title": "Mapping"
}
},
"required": [
"mapping"
],
"title": "FieldsMapping",
"type": "object"
},
"NoneFieldsMapping": {
"description": "Field mapping that never matches any upstream fields.",
"properties": {
"type": {
"const": "none",
"default": "none",
"title": "Type",
"type": "string"
}
},
"title": "NoneFieldsMapping",
"type": "object"
},
"SpecificFieldsMapping": {
"description": "Field mapping that explicitly depends on specific upstream fields.",
"properties": {
"type": {
"const": "specific",
"default": "specific",
"title": "Type",
"type": "string"
},
"mapping": {
"additionalProperties": {
"items": {
"$ref": "#/$defs/FieldKey"
},
"type": "array",
"uniqueItems": true
},
"propertyNames": {
"$ref": "#/$defs/FieldKey"
},
"title": "Mapping",
"type": "object"
}
},
"required": [
"mapping"
],
"title": "SpecificFieldsMapping",
"type": "object"
}
},
"description": "Feature dependency specification with optional column selection and renaming.\n\nAttributes:\n key: The feature key to depend on. Accepts string (\"a/b/c\"), list ([\"a\", \"b\", \"c\"]),\n FeatureKey instance, or BaseFeature class.\n columns: Optional tuple of column names to select from upstream feature.\n - None (default): Keep all columns from upstream\n - Empty tuple (): Keep only system columns (sample_uid, provenance_by_field, etc.)\n - Tuple of names: Keep only specified columns (plus system columns)\n rename: Optional mapping of old column names to new names.\n Applied after column selection.\n fields_mapping: Optional field mapping configuration for automatic field dependency resolution.\n When provided, fields without explicit deps will automatically map to matching upstream fields.\n Defaults to using `[FieldsMapping.default()][metaxy.models.fields_mapping.DefaultFieldsMapping]`.\n filters: Optional SQL-like filter strings applied to this dependency. Automatically parsed into\n Narwhals expressions (accessible via the `filters` property). Filters are automatically\n applied by FeatureDepTransformer after renames during all FeatureDep operations (including\n resolve_update and version computation).\n\nExamples:\n ```py\n # Keep all columns with default field mapping\n FeatureDep(feature=\"upstream\")\n\n # Keep all columns with suffix matching\n FeatureDep(feature=\"upstream\", fields_mapping=FieldsMapping.default(match_suffix=True))\n\n # Keep all columns with all fields mapping\n FeatureDep(feature=\"upstream\", fields_mapping=FieldsMapping.all())\n\n # Keep only specific columns\n FeatureDep(\n feature=\"upstream/feature\",\n columns=(\"col1\", \"col2\")\n )\n\n # Rename columns to avoid conflicts\n FeatureDep(\n feature=\"upstream/feature\",\n rename={\"old_name\": \"new_name\"}\n )\n\n # Select and rename\n FeatureDep(\n feature=\"upstream/feature\",\n columns=(\"col1\", \"col2\"),\n rename={\"col1\": \"upstream_col1\"}\n )\n\n # SQL filters\n FeatureDep(\n feature=\"upstream\",\n filters=[\"age >= 25\", \"status = 'active'\"]\n )\n ```",
"properties": {
"feature": {
"$ref": "#/$defs/FeatureKey",
"description": "Feature key. Accepts a slashed string ('a/b/c'), a sequence of strings, a FeatureKey instance, or a child class of BaseFeature"
},
"columns": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"title": "Columns"
},
"rename": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"title": "Rename"
},
"fields_mapping": {
"$ref": "#/$defs/FieldsMapping"
},
"filters": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "SQL-like filter strings applied to this dependency.",
"title": "Filters"
}
},
"required": [
"feature"
],
"title": "FeatureDep",
"type": "object"
}
Fields:
-
feature(ValidatedFeatureKey) -
columns(tuple[str, ...] | None) -
rename(dict[str, str] | None) -
fields_mapping(FieldsMapping) -
sql_filters(tuple[str, ...] | None)
Source code in src/metaxy/models/feature_spec.py
def __init__( # pyright: ignore[reportMissingSuperCall]
self,
*,
feature: str | Sequence[str] | FeatureKey | type[BaseFeature],
columns: tuple[str, ...] | None = None,
rename: dict[str, str] | None = None,
fields_mapping: FieldsMapping | None = None,
filters: Sequence[str] | None = None,
) -> None: ... # pyright: ignore[reportMissingSuperCall]