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# Copyright 2020 The Feast Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from abc import ABC, abstractmethod
from enum import Enum
from typing import TYPE_CHECKING, Dict, Optional
if TYPE_CHECKING:
from feast.protos.feast.core.DataFormat_pb2 import TableFormat as TableFormatProto
class TableFormatType(Enum):
"""Enum for supported table formats"""
DELTA = "delta"
ICEBERG = "iceberg"
HUDI = "hudi"
class TableFormat(ABC):
"""
Abstract base class for table formats.
Table formats encapsulate metadata and configuration specific to different
table storage formats like Iceberg, Delta Lake, Hudi, etc. They provide
a unified interface for configuring table-specific properties that are
used when reading from or writing to these advanced table formats.
This base class defines the contract that all table format implementations
must follow, including serialization/deserialization capabilities and
property management.
Attributes:
format_type (TableFormatType): The type of table format (iceberg, delta, hudi).
properties (Dict[str, str]): Dictionary of format-specific properties.
Examples:
Table formats are typically used with data sources to specify
advanced table metadata and reading options:
>>> from feast.table_format import IcebergFormat
>>> iceberg_format = IcebergFormat(
... catalog="my_catalog",
... namespace="my_namespace"
... )
>>> iceberg_format.set_property("snapshot-id", "123456789")
"""
def __init__(
self, format_type: TableFormatType, properties: Optional[Dict[str, str]] = None
):
self.format_type = format_type
self.properties = properties or {}
@abstractmethod
def to_dict(self) -> Dict:
"""Convert table format to dictionary representation"""
pass
@classmethod
@abstractmethod
def from_dict(cls, data: Dict) -> "TableFormat":
"""Create table format from dictionary representation"""
pass
def get_property(self, key: str, default: Optional[str] = None) -> Optional[str]:
"""Get a table format property"""
return self.properties.get(key, default)
def set_property(self, key: str, value: str) -> None:
"""Set a table format property"""
self.properties[key] = value
class IcebergFormat(TableFormat):
"""
Apache Iceberg table format configuration.
Iceberg is an open table format for huge analytic datasets. This class provides
configuration for Iceberg-specific properties including catalog configuration,
namespace settings, and table-level properties for reading and writing Iceberg tables.
Args:
catalog (Optional[str]): Name of the Iceberg catalog to use. The catalog manages
table metadata and provides access to tables.
namespace (Optional[str]): Namespace (schema/database) within the catalog where
the table is located.
properties (Optional[Dict[str, str]]): Properties for configuring Iceberg
catalog and table operations (e.g., warehouse location, snapshot-id,
as-of-timestamp, file format, compression, partitioning).
Attributes:
catalog (str): The Iceberg catalog name.
namespace (str): The namespace within the catalog.
properties (Dict[str, str]): Iceberg configuration properties.
Examples:
Basic Iceberg configuration:
>>> iceberg_format = IcebergFormat(
... catalog="my_catalog",
... namespace="my_database"
... )
Advanced configuration with properties:
>>> iceberg_format = IcebergFormat(
... catalog="spark_catalog",
... namespace="lakehouse",
... properties={
... "warehouse": "s3://my-bucket/warehouse",
... "catalog-impl": "org.apache.iceberg.spark.SparkCatalog",
... "format-version": "2",
... "write.parquet.compression-codec": "snappy"
... }
... )
Reading from a specific snapshot:
>>> iceberg_format = IcebergFormat(catalog="my_catalog", namespace="db")
>>> iceberg_format.set_property("snapshot-id", "123456789")
Time travel queries:
>>> iceberg_format.set_property("as-of-timestamp", "1648684800000")
"""
def __init__(
self,
catalog: Optional[str] = None,
namespace: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
):
super().__init__(TableFormatType.ICEBERG, properties)
self.catalog = catalog
self.namespace = namespace
# Add catalog and namespace to properties if provided
if catalog:
self.properties["iceberg.catalog"] = catalog
if namespace:
self.properties["iceberg.namespace"] = namespace
def to_dict(self) -> Dict:
return {
"format_type": self.format_type.value,
"catalog": self.catalog,
"namespace": self.namespace,
"properties": self.properties,
}
@classmethod
def from_dict(cls, data: Dict) -> "IcebergFormat":
return cls(
catalog=data.get("catalog"),
namespace=data.get("namespace"),
properties=data.get("properties", {}),
)
def to_proto(self) -> "TableFormatProto":
"""Convert to protobuf TableFormat message"""
from feast.protos.feast.core.DataFormat_pb2 import (
TableFormat as TableFormatProto,
)
iceberg_proto = TableFormatProto.IcebergFormat(
catalog=self.catalog or "",
namespace=self.namespace or "",
properties=self.properties,
)
return TableFormatProto(iceberg_format=iceberg_proto)
@classmethod
def from_proto(cls, proto: "TableFormatProto") -> "IcebergFormat":
"""Create from protobuf TableFormat message"""
iceberg_proto = proto.iceberg_format
return cls(
catalog=iceberg_proto.catalog if iceberg_proto.catalog else None,
namespace=iceberg_proto.namespace if iceberg_proto.namespace else None,
properties=dict(iceberg_proto.properties),
)
class DeltaFormat(TableFormat):
"""
Delta Lake table format configuration.
Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark
and big data workloads. This class provides configuration for Delta-specific properties
including table properties, checkpoint locations, and versioning options.
Args:
checkpoint_location (Optional[str]): Location for storing Delta transaction logs
and checkpoints. Required for streaming operations.
properties (Optional[Dict[str, str]]): Properties for configuring Delta table
behavior (e.g., auto-optimize, vacuum settings, data skipping).
Attributes:
checkpoint_location (str): Path to checkpoint storage location.
properties (Dict[str, str]): Delta table configuration properties.
Examples:
Basic Delta configuration:
>>> delta_format = DeltaFormat()
Configuration with table properties:
>>> delta_format = DeltaFormat(
... properties={
... "delta.autoOptimize.optimizeWrite": "true",
... "delta.autoOptimize.autoCompact": "true",
... "delta.tuneFileSizesForRewrites": "true"
... }
... )
Streaming configuration with checkpoint:
>>> delta_format = DeltaFormat(
... checkpoint_location="s3://my-bucket/checkpoints/my_table"
... )
Time travel - reading specific version:
>>> delta_format = DeltaFormat()
>>> delta_format.set_property("versionAsOf", "5")
Time travel - reading at specific timestamp:
>>> delta_format.set_property("timestampAsOf", "2023-01-01 00:00:00")
"""
def __init__(
self,
checkpoint_location: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
):
super().__init__(TableFormatType.DELTA, properties)
self.checkpoint_location = checkpoint_location
# Add checkpoint location to properties if provided
if checkpoint_location:
self.properties["delta.checkpointLocation"] = checkpoint_location
def to_dict(self) -> Dict:
return {
"format_type": self.format_type.value,
"checkpoint_location": self.checkpoint_location,
"properties": self.properties,
}
@classmethod
def from_dict(cls, data: Dict) -> "DeltaFormat":
return cls(
checkpoint_location=data.get("checkpoint_location"),
properties=data.get("properties", {}),
)
def to_proto(self) -> "TableFormatProto":
"""Convert to protobuf TableFormat message"""
from feast.protos.feast.core.DataFormat_pb2 import (
TableFormat as TableFormatProto,
)
delta_proto = TableFormatProto.DeltaFormat(
checkpoint_location=self.checkpoint_location or "",
properties=self.properties,
)
return TableFormatProto(delta_format=delta_proto)
@classmethod
def from_proto(cls, proto: "TableFormatProto") -> "DeltaFormat":
"""Create from protobuf TableFormat message"""
delta_proto = proto.delta_format
return cls(
checkpoint_location=delta_proto.checkpoint_location
if delta_proto.checkpoint_location
else None,
properties=dict(delta_proto.properties),
)
class HudiFormat(TableFormat):
"""
Apache Hudi table format configuration.
Apache Hudi is a data management framework used to simplify incremental data processing
and data pipeline development. This class provides configuration for Hudi-specific
properties including table type, record keys, and write operations.
Args:
table_type (Optional[str]): Type of Hudi table. Options are:
- "COPY_ON_WRITE": Stores data in columnar format (Parquet) and rewrites entire files
- "MERGE_ON_READ": Stores data using combination of columnar and row-based formats
record_key (Optional[str]): Field(s) that uniquely identify a record. Can be a single
field or comma-separated list for composite keys.
precombine_field (Optional[str]): Field used to determine the latest version of a record
when multiple updates exist (usually a timestamp or version field).
properties (Optional[Dict[str, str]]): Additional Hudi table properties for
configuring compaction, indexing, and other Hudi features.
Attributes:
table_type (str): The Hudi table type (COPY_ON_WRITE or MERGE_ON_READ).
record_key (str): The record key field(s).
precombine_field (str): The field used for record deduplication.
properties (Dict[str, str]): Additional Hudi configuration properties.
Examples:
Basic Hudi configuration:
>>> hudi_format = HudiFormat(
... table_type="COPY_ON_WRITE",
... record_key="user_id",
... precombine_field="timestamp"
... )
Configuration with composite record key:
>>> hudi_format = HudiFormat(
... table_type="MERGE_ON_READ",
... record_key="user_id,event_type",
... precombine_field="event_timestamp"
... )
Advanced configuration with table properties:
>>> hudi_format = HudiFormat(
... table_type="COPY_ON_WRITE",
... record_key="id",
... precombine_field="updated_at",
... properties={
... "hoodie.compaction.strategy": "org.apache.hudi.table.action.compact.strategy.LogFileSizeBasedCompactionStrategy",
... "hoodie.index.type": "BLOOM",
... "hoodie.bloom.index.parallelism": "100"
... }
... )
Reading incremental data:
>>> hudi_format = HudiFormat(table_type="COPY_ON_WRITE")
>>> hudi_format.set_property("hoodie.datasource.query.type", "incremental")
>>> hudi_format.set_property("hoodie.datasource.read.begin.instanttime", "20230101000000")
"""
def __init__(
self,
table_type: Optional[str] = None, # COPY_ON_WRITE or MERGE_ON_READ
record_key: Optional[str] = None,
precombine_field: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
):
super().__init__(TableFormatType.HUDI, properties)
self.table_type = table_type
self.record_key = record_key
self.precombine_field = precombine_field
# Add Hudi-specific properties if provided
if table_type:
self.properties["hoodie.datasource.write.table.type"] = table_type
if record_key:
self.properties["hoodie.datasource.write.recordkey.field"] = record_key
if precombine_field:
self.properties["hoodie.datasource.write.precombine.field"] = (
precombine_field
)
def to_dict(self) -> Dict:
return {
"format_type": self.format_type.value,
"table_type": self.table_type,
"record_key": self.record_key,
"precombine_field": self.precombine_field,
"properties": self.properties,
}
@classmethod
def from_dict(cls, data: Dict) -> "HudiFormat":
return cls(
table_type=data.get("table_type"),
record_key=data.get("record_key"),
precombine_field=data.get("precombine_field"),
properties=data.get("properties", {}),
)
def to_proto(self) -> "TableFormatProto":
"""Convert to protobuf TableFormat message"""
from feast.protos.feast.core.DataFormat_pb2 import (
TableFormat as TableFormatProto,
)
hudi_proto = TableFormatProto.HudiFormat(
table_type=self.table_type or "",
record_key=self.record_key or "",
precombine_field=self.precombine_field or "",
properties=self.properties,
)
return TableFormatProto(hudi_format=hudi_proto)
@classmethod
def from_proto(cls, proto: "TableFormatProto") -> "HudiFormat":
"""Create from protobuf TableFormat message"""
hudi_proto = proto.hudi_format
return cls(
table_type=hudi_proto.table_type if hudi_proto.table_type else None,
record_key=hudi_proto.record_key if hudi_proto.record_key else None,
precombine_field=hudi_proto.precombine_field
if hudi_proto.precombine_field
else None,
properties=dict(hudi_proto.properties),
)
def create_table_format(format_type: TableFormatType, **kwargs) -> TableFormat:
"""
Factory function to create appropriate TableFormat instance based on type.
This is a convenience function that creates the correct TableFormat subclass
based on the provided format type, passing through any additional keyword arguments
to the constructor.
Args:
format_type (TableFormatType): The type of table format to create.
**kwargs: Additional keyword arguments passed to the format constructor.
Returns:
TableFormat: An instance of the appropriate TableFormat subclass.
Raises:
ValueError: If an unsupported format_type is provided.
Examples:
Create an Iceberg format:
>>> iceberg_format = create_table_format(
... TableFormatType.ICEBERG,
... catalog="my_catalog",
... namespace="my_db"
... )
Create a Delta format:
>>> delta_format = create_table_format(
... TableFormatType.DELTA,
... checkpoint_location="s3://bucket/checkpoints"
... )
"""
if format_type == TableFormatType.ICEBERG:
return IcebergFormat(**kwargs)
elif format_type == TableFormatType.DELTA:
return DeltaFormat(**kwargs)
elif format_type == TableFormatType.HUDI:
return HudiFormat(**kwargs)
else:
raise ValueError(f"Unknown table format type: {format_type}")
def table_format_from_dict(data: Dict) -> TableFormat:
"""
Create TableFormat instance from dictionary representation.
This function deserializes a dictionary (typically from JSON or protobuf)
back into the appropriate TableFormat instance. The dictionary must contain
a 'format_type' field that indicates which format class to instantiate.
Args:
data (Dict): Dictionary containing table format configuration. Must include
'format_type' field with value 'iceberg', 'delta', or 'hudi'.
Returns:
TableFormat: An instance of the appropriate TableFormat subclass.
Raises:
ValueError: If format_type is not recognized.
KeyError: If format_type field is missing from data.
Examples:
Deserialize an Iceberg format:
>>> data = {
... "format_type": "iceberg",
... "catalog": "my_catalog",
... "namespace": "my_db"
... }
>>> iceberg_format = table_format_from_dict(data)
"""
if "format_type" not in data:
raise KeyError("Missing 'format_type' field in data")
format_type = data["format_type"]
if format_type == TableFormatType.ICEBERG.value:
return IcebergFormat.from_dict(data)
elif format_type == TableFormatType.DELTA.value:
return DeltaFormat.from_dict(data)
elif format_type == TableFormatType.HUDI.value:
return HudiFormat.from_dict(data)
else:
raise ValueError(f"Unknown table format type: {format_type}")
def table_format_from_json(json_str: str) -> TableFormat:
"""
Create TableFormat instance from JSON string.
This is a convenience function that parses a JSON string and creates
the appropriate TableFormat instance. Useful for loading table format
configurations from files or network requests.
Args:
json_str (str): JSON string containing table format configuration.
Returns:
TableFormat: An instance of the appropriate TableFormat subclass.
Raises:
json.JSONDecodeError: If the JSON string is invalid.
ValueError: If format_type is not recognized.
KeyError: If format_type field is missing.
Examples:
Load from JSON string:
>>> json_config = '{"format_type": "delta", "checkpoint_location": "s3://bucket/checkpoints"}'
>>> delta_format = table_format_from_json(json_config)
"""
data = json.loads(json_str)
return table_format_from_dict(data)
def table_format_from_proto(proto: "TableFormatProto") -> TableFormat:
"""
Create TableFormat instance from protobuf TableFormat message.
Args:
proto: TableFormat protobuf message
Returns:
TableFormat: An instance of the appropriate TableFormat subclass.
Raises:
ValueError: If the proto doesn't contain a recognized format.
"""
which_format = proto.WhichOneof("format")
if which_format == "iceberg_format":
return IcebergFormat.from_proto(proto)
elif which_format == "delta_format":
return DeltaFormat.from_proto(proto)
elif which_format == "hudi_format":
return HudiFormat.from_proto(proto)
else:
raise ValueError(f"Unknown table format in proto: {which_format}")