feast/sdk/python/feast/dbt/mapper.py at master · feast-dev/feast · GitHub

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"""

dbt to Feast type and object mapper.

This module provides functionality to map dbt model metadata to Feast objects

including DataSource, Entity, and FeatureView.

"""

from datetime import timedelta

from typing import Any, Dict, List, Optional, Union

from feast.dbt.parser import DbtModel

from feast.entity import Entity

from feast.feature_view import FeatureView

from feast.field import Field

from feast.types import (

Array,

Bool,

Bytes,

FeastType,

Float32,

Float64,

Int32,

Int64,

String,

UnixTimestamp,

)

from feast.value_type import ValueType

# Mapping from FeastType to ValueType for entity value inference

FEAST_TYPE_TO_VALUE_TYPE: Dict[FeastType, ValueType] = {

String: ValueType.STRING,

Int32: ValueType.INT32,

Int64: ValueType.INT64,

Float32: ValueType.FLOAT,

Float64: ValueType.DOUBLE,

Bool: ValueType.BOOL,

Bytes: ValueType.BYTES,

UnixTimestamp: ValueType.UNIX_TIMESTAMP,

}

def feast_type_to_value_type(feast_type: FeastType) -> ValueType:

"""Convert a FeastType to its corresponding ValueType for entities."""

return FEAST_TYPE_TO_VALUE_TYPE.get(feast_type, ValueType.STRING)

# Comprehensive mapping from dbt/warehouse types to Feast types

# Covers BigQuery, Snowflake, Redshift, PostgreSQL, and common SQL types

DBT_TO_FEAST_TYPE_MAP: Dict[str, FeastType] = {

# String types

"STRING": String,

"TEXT": String,

"VARCHAR": String,

"CHAR": String,

"CHARACTER": String,

"NVARCHAR": String,

"NCHAR": String,

"CHARACTER VARYING": String,

# Integer types

"INT": Int64,

"INT32": Int32,

"INT64": Int64,

"INTEGER": Int64,

"BIGINT": Int64,

"SMALLINT": Int32,

"TINYINT": Int32,

"BYTEINT": Int32,

"NUMBER": Int64, # Snowflake - default to Int64, precision handling below

"NUMERIC": Int64,

"DECIMAL": Int64,

# Float types

"FLOAT": Float32,

"FLOAT32": Float32,

"FLOAT64": Float64,

"DOUBLE": Float64,

"DOUBLE PRECISION": Float64,

"REAL": Float32,

# Boolean types

"BOOL": Bool,

"BOOLEAN": Bool,

# Timestamp types

"TIMESTAMP": UnixTimestamp,

"TIMESTAMP_NTZ": UnixTimestamp,

"TIMESTAMP_LTZ": UnixTimestamp,

"TIMESTAMP_TZ": UnixTimestamp,

"DATETIME": UnixTimestamp,

"DATE": UnixTimestamp,

"TIME": UnixTimestamp,

# Binary types

"BYTES": Bytes,

"BINARY": Bytes,

"VARBINARY": Bytes,

"BLOB": Bytes,

}

def map_dbt_type_to_feast_type(dbt_type: str) -> FeastType:

"""

Map a dbt data type to a Feast type.

Handles various database type formats including:

- Simple types: STRING, INT64, FLOAT

- Parameterized types: VARCHAR(255), NUMBER(10,2), DECIMAL(18,0)

- Array types: ARRAY<STRING>, ARRAY<INT64>

Args:

dbt_type: The dbt/database data type string

Returns:

The corresponding Feast type

"""

if not dbt_type:

return String

# Normalize the type string

normalized = dbt_type.upper().strip()

# Handle ARRAY types: ARRAY<element_type>

if normalized.startswith("ARRAY<") and normalized.endswith(">"):

element_type_str = normalized[6:-1].strip()

element_type = map_dbt_type_to_feast_type(element_type_str)

# Array only supports primitive types

valid_array_types = {

String,

Int32,

Int64,

Float32,

Float64,

Bool,

Bytes,

UnixTimestamp,

}

if element_type in valid_array_types:

return Array(element_type)

return Array(String) # Fallback for complex nested types

# Handle parameterized types: VARCHAR(255), NUMBER(10,2), etc.

# Extract base type by removing parentheses and parameters

base_type = normalized.split("(")[0].strip()

# Handle Snowflake NUMBER with precision

if base_type == "NUMBER" and "(" in normalized:

try:

# Parse precision and scale: NUMBER(precision, scale)

params = normalized.split("(")[1].rstrip(")").split(",")

precision = int(params[0].strip())

scale = int(params[1].strip()) if len(params) > 1 else 0

if scale > 0:

# Has decimal places, use Float64

return Float64

elif precision <= 9:

return Int32

elif precision <= 18:

return Int64

else:

# Precision > 18, may exceed Int64 range

return Float64

except (ValueError, IndexError):

return Int64

# Look up in mapping table

if base_type in DBT_TO_FEAST_TYPE_MAP:

return DBT_TO_FEAST_TYPE_MAP[base_type]

# Default to String for unknown types

return String

class DbtToFeastMapper:

"""

Maps dbt models to Feast objects.

Supports creating DataSource, Entity, and FeatureView objects from

dbt model metadata.

Example::

mapper = DbtToFeastMapper(data_source_type="bigquery")

data_source = mapper.create_data_source(model)

feature_view = mapper.create_feature_view(

model, data_source, entity_column="driver_id"

)

Args:

data_source_type: Type of data source ('bigquery', 'snowflake', 'file')

timestamp_field: Default timestamp field name

ttl_days: Default TTL in days for feature views

"""

def __init__(

self,

data_source_type: str = "bigquery",

timestamp_field: str = "event_timestamp",

ttl_days: int = 1,

self.data_source_type = data_source_type.lower()

self.timestamp_field = timestamp_field

self.ttl_days = ttl_days

def _infer_entity_value_type(self, model: DbtModel, entity_col: str) -> ValueType:

"""Infer entity ValueType from dbt model column type."""

for column in model.columns:

if column.name == entity_col:

feast_type = map_dbt_type_to_feast_type(column.data_type)

return feast_type_to_value_type(feast_type)

return ValueType.UNKNOWN

def create_data_source(

self,

model: DbtModel,

timestamp_field: Optional[str] = None,

created_timestamp_column: Optional[str] = None,

) -> Any:

"""

Create a Feast DataSource from a dbt model.

Args:

model: The DbtModel to create a DataSource from

timestamp_field: Override the default timestamp field

created_timestamp_column: Column for created timestamp (dedup)

Returns:

A Feast DataSource (BigQuerySource, SnowflakeSource, or FileSource)

Raises:

ValueError: If data_source_type is not supported

"""

ts_field = timestamp_field or self.timestamp_field

# Build tags from dbt metadata

tags = {"dbt.model": model.name}

for tag in model.tags:

tags[f"dbt.tag.{tag}"] = "true"

if self.data_source_type == "bigquery":

from feast.infra.offline_stores.bigquery_source import BigQuerySource

return BigQuerySource(

name=f"{model.name}_source",

table=model.full_table_name,

timestamp_field=ts_field,

created_timestamp_column=created_timestamp_column or "",

description=model.description,

tags=tags,

)

elif self.data_source_type == "snowflake":

from feast.infra.offline_stores.snowflake_source import SnowflakeSource

return SnowflakeSource(

name=f"{model.name}_source",

database=model.database,

schema=model.schema,

table=model.alias,

timestamp_field=ts_field,

created_timestamp_column=created_timestamp_column or "",

description=model.description,

tags=tags,

)

elif self.data_source_type == "file":

from feast.infra.offline_stores.file_source import FileSource

# For file sources, use the model name as a placeholder path

return FileSource(

name=f"{model.name}_source",

path=f"/data/{model.name}.parquet",

timestamp_field=ts_field,

created_timestamp_column=created_timestamp_column or "",

description=model.description,

tags=tags,

)

else:

raise ValueError(

f"Unsupported data_source_type: {self.data_source_type}. "

f"Supported types: bigquery, snowflake, file"

)

def create_entity(

self,

name: str,

join_keys: Optional[List[str]] = None,

description: str = "",

tags: Optional[Dict[str, str]] = None,

value_type: ValueType = ValueType.STRING,

) -> Entity:

"""

Create a Feast Entity.

Args:

name: Entity name

join_keys: List of join key column names (defaults to [name])

description: Entity description

tags: Optional tags

value_type: Value type for the entity (default: STRING)

Returns:

A Feast Entity

"""

return Entity(

name=name,

join_keys=join_keys or [name],

value_type=value_type,

description=description,

tags=tags or {},

)

def create_feature_view(

self,

model: DbtModel,

source: Any,

entity_columns: Union[str, List[str]],

entities: Optional[Union[Entity, List[Entity]]] = None,

timestamp_field: Optional[str] = None,

ttl_days: Optional[int] = None,

exclude_columns: Optional[List[str]] = None,

online: bool = True,

) -> FeatureView:

"""

Create a Feast FeatureView from a dbt model.

Args:

model: The DbtModel to create a FeatureView from

source: The DataSource for this FeatureView

entity_columns: Entity column name(s) - single string or list of strings

entities: Optional pre-created Entity or list of Entities

timestamp_field: Override the default timestamp field

ttl_days: Override the default TTL in days

exclude_columns: Additional columns to exclude from features

online: Whether to enable online serving

Returns:

A Feast FeatureView

"""

# Normalize to lists

entity_cols: List[str] = (

[entity_columns]

if isinstance(entity_columns, str)

else list(entity_columns)

)

entity_objs: List[Entity] = []

if entities is not None:

entity_objs = [entities] if isinstance(entities, Entity) else list(entities)

# Validate

if not entity_cols:

raise ValueError("At least one entity column must be specified")

if entity_objs and len(entity_cols) != len(entity_objs):

raise ValueError(

f"Number of entity_columns ({len(entity_cols)}) must match "

f"number of entities ({len(entity_objs)})"

)

ts_field = timestamp_field or self.timestamp_field

ttl = timedelta(days=ttl_days if ttl_days is not None else self.ttl_days)

# Columns to exclude from schema (timestamp + any explicitly excluded)

# Note: entity columns should NOT be excluded - FeatureView.__init__

# expects entity columns to be in the schema and will extract them

excluded = {ts_field}

if exclude_columns:

excluded.update(exclude_columns)

# Create schema from model columns (includes entity columns)

schema: List[Field] = []

for column in model.columns:

if column.name not in excluded:

feast_type = map_dbt_type_to_feast_type(column.data_type)

schema.append(

Field(

name=column.name,

dtype=feast_type,

description=column.description,

)

# Create entities if not provided

if not entity_objs:

entity_objs = []

for entity_col in entity_cols:

# Infer entity value type from model column

entity_value_type = self._infer_entity_value_type(model, entity_col)

ent = self.create_entity(

name=entity_col,

description=f"Entity for {model.name}",

value_type=entity_value_type,

)

entity_objs.append(ent)

# Build tags from dbt metadata

tags = {

"dbt.model": model.name,

"dbt.unique_id": model.unique_id,

}

for tag in model.tags:

tags[f"dbt.tag.{tag}"] = "true"

return FeatureView(

name=model.name,

source=source,

schema=schema,

entities=entity_objs,

ttl=ttl,

online=online,

description=model.description,

tags=tags,

)

def create_all_from_model(

self,

model: DbtModel,

entity_columns: Union[str, List[str]],

timestamp_field: Optional[str] = None,

ttl_days: Optional[int] = None,

exclude_columns: Optional[List[str]] = None,

online: bool = True,

) -> Dict[str, Union[List[Entity], Any, FeatureView]]:

"""

Create all Feast objects (DataSource, Entity, FeatureView) from a dbt model.

This is a convenience method that creates all necessary Feast objects

in one call.

Args:

model: The DbtModel to create objects from

entity_columns: Entity column name(s) - single string or list of strings

timestamp_field: Override the default timestamp field

ttl_days: Override the default TTL in days

exclude_columns: Additional columns to exclude from features

online: Whether to enable online serving

Returns:

Dict with keys 'entities', 'data_source', 'feature_view'

"""

# Normalize to list

entity_cols: List[str] = (

[entity_columns]

if isinstance(entity_columns, str)

else list(entity_columns)

)

# Create entities (plural)

entities_list = []

for entity_col in entity_cols:

entity_value_type = self._infer_entity_value_type(model, entity_col)

entity = self.create_entity(

name=entity_col,

description=f"Entity for {model.name}",

tags={"dbt.model": model.name},

value_type=entity_value_type,

)

entities_list.append(entity)

# Create data source

data_source = self.create_data_source(

model=model,

timestamp_field=timestamp_field,

)

# Create feature view

feature_view = self.create_feature_view(

model=model,

source=data_source,

entity_columns=entity_cols,

entities=entities_list,

timestamp_field=timestamp_field,

ttl_days=ttl_days,

exclude_columns=exclude_columns,

online=online,

)

return {

"entities": entities_list,

"data_source": data_source,

"feature_view": feature_view,

}

feast/sdk/python/feast/dbt/mapper.py at master · feast-dev/feast · GitHub

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