feast-dev · feast-ci-bot · Jul 6, 2022 · Jul 1, 2022 · Jul 5, 2022 · Jul 5, 2022
@@ -0,0 +1,13 @@
+from .batch_materialization_engine import (
+    BatchMaterializationEngine,
+    MaterializationJob,
+    MaterializationTask,
+)
+from .local_engine import LocalMaterializationEngine
+
+__all__ = [
+    "MaterializationJob",
+    "MaterializationTask",
+    "BatchMaterializationEngine",
+    "LocalMaterializationEngine",
+]
@@ -0,0 +1,61 @@
+import dataclasses
+from abc import ABC, abstractmethod
+from datetime import datetime
+from typing import Callable, List, Optional, Union
+
+from tqdm import tqdm
+
+from feast.batch_feature_view import BatchFeatureView
+from feast.infra.offline_stores.offline_store import OfflineStore
+from feast.infra.online_stores.online_store import OnlineStore
+from feast.repo_config import RepoConfig
+from feast.stream_feature_view import StreamFeatureView
+
+
+@dataclasses.dataclass
+class MaterializationTask:
+    project: str
+    feature_view: Union[BatchFeatureView, StreamFeatureView]
+    start_time: datetime
+    end_time: datetime
+    tqdm_builder: Callable[[int], tqdm]
+
+
+class MaterializationJob(ABC):
+    task: MaterializationTask
+
+    @abstractmethod
+    def status(self) -> str:
+        ...
+
+    @abstractmethod
+    def should_be_retried(self) -> bool:
+        ...
+
+    @abstractmethod
+    def job_id(self) -> str:
+        ...
+
+    @abstractmethod
+    def url(self) -> Optional[str]:
+        ...
+
+
+class BatchMaterializationEngine(ABC):
+    def __init__(
+        self,
+        *,
+        repo_config: RepoConfig,
+        offline_store: OfflineStore,
+        online_store: OnlineStore,
+        **kwargs,
+    ):
+        self.repo_config = repo_config
+        self.offline_store = offline_store
+        self.online_store = online_store
+
+    @abstractmethod
+    def materialize(
+        self, registry, tasks: List[MaterializationTask]
+    ) -> List[MaterializationJob]:
+        ...
@@ -0,0 +1,302 @@
+from datetime import datetime
+from typing import Callable, Dict, List, Literal, Optional, Tuple, Union
+
+import dask.dataframe as dd
+import pandas as pd
+import pyarrow as pa
+from tqdm import tqdm
+
+from feast import (
+    BatchFeatureView,
+    Entity,
+    FeatureView,
+    RepoConfig,
+    StreamFeatureView,
+    ValueType,
+)
+from feast.feature_view import DUMMY_ENTITY_ID
+from feast.infra.offline_stores.offline_store import OfflineStore
+from feast.infra.online_stores.online_store import OnlineStore
+from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto
+from feast.protos.feast.types.Value_pb2 import Value as ValueProto
+from feast.repo_config import FeastConfigBaseModel
+from feast.type_map import python_values_to_proto_values
+
+from .batch_materialization_engine import (
+    BatchMaterializationEngine,
+    MaterializationJob,
+    MaterializationTask,
+)
+
+DEFAULT_BATCH_SIZE = 10_000
+
+
+class LocalMaterializationEngineConfig(FeastConfigBaseModel):
+    """Batch Materialization Engine config for local in-process engine"""
+
+    type: Literal["local"] = "local"
+    """ Type selector"""
+
+
+class LocalMaterializationJob(MaterializationJob):
+    def __init__(self, job_id: str) -> None:
+        super().__init__()
+        self._job_id: str = job_id
+
+    def status(self) -> str:
+        return "success"
+
+    def should_be_retried(self) -> bool:
+        return False
+
+    def job_id(self) -> str:
+        return self.job_id()
+
+    def url(self) -> Optional[str]:
+        return None
+
+
+class LocalMaterializationEngine(BatchMaterializationEngine):
+    def __init__(
+        self,
+        *,
+        repo_config: RepoConfig,
+        offline_store: OfflineStore,
+        online_store: OnlineStore,
+        **kwargs,
+    ):
+        super().__init__(
+            repo_config=repo_config,
+            offline_store=offline_store,
+            online_store=online_store,
+            **kwargs,
+        )
+
+    def materialize(
+        self, registry, tasks: List[MaterializationTask]
+    ) -> List[MaterializationJob]:
+        return [
+            self.materialize_one(
+                registry,
+                task.feature_view,
+                task.start_time,
+                task.end_time,
+                task.project,
+                task.tqdm_builder,
+            )
+            for task in tasks
+        ]
+
+    def materialize_one(
+        self,
+        registry,
+        feature_view: Union[BatchFeatureView, StreamFeatureView],
+        start_date: datetime,
+        end_date: datetime,
+        project: str,
+        tqdm_builder: Callable[[int], tqdm],
+    ):
+        entities = []
+        for entity_name in feature_view.entities:
+            entities.append(registry.get_entity(entity_name, project))
+
+        (
+            join_key_columns,
+            feature_name_columns,
+            timestamp_field,
+            created_timestamp_column,
+        ) = _get_column_names(feature_view, entities)
+
+        offline_job = self.offline_store.pull_latest_from_table_or_query(
+            config=self.repo_config,
+            data_source=feature_view.batch_source,
+            join_key_columns=join_key_columns,
+            feature_name_columns=feature_name_columns,
+            timestamp_field=timestamp_field,
+            created_timestamp_column=created_timestamp_column,
+            start_date=start_date,
+            end_date=end_date,
+        )
+
+        table = offline_job.to_arrow()
+
+        if feature_view.batch_source.field_mapping is not None:
+            table = _run_field_mapping(table, feature_view.batch_source.field_mapping)
+
+        join_key_to_value_type = {
+            entity.name: entity.dtype.to_value_type()
+            for entity in feature_view.entity_columns
+        }
+
+        with tqdm_builder(table.num_rows) as pbar:
+            for batch in table.to_batches(DEFAULT_BATCH_SIZE):
+                rows_to_write = _convert_arrow_to_proto(
+                    batch, feature_view, join_key_to_value_type
+                )
+                self.online_store.online_write_batch(
+                    self.repo_config,
+                    feature_view,
+                    rows_to_write,
+                    lambda x: pbar.update(x),
+                )
+        job_id = f"{feature_view.name}-{start_date}-{end_date}"
+        return LocalMaterializationJob(job_id=job_id)
+
+
+def _get_column_names(
+    feature_view: FeatureView, entities: List[Entity]
+) -> Tuple[List[str], List[str], str, Optional[str]]:
+    """
+    If a field mapping exists, run it in reverse on the join keys,
+    feature names, event timestamp column, and created timestamp column
+    to get the names of the relevant columns in the offline feature store table.
+
+    Returns:
+        Tuple containing the list of reverse-mapped join_keys,
+        reverse-mapped feature names, reverse-mapped event timestamp column,
+        and reverse-mapped created timestamp column that will be passed into
+        the query to the offline store.
+    """
+    # if we have mapped fields, use the original field names in the call to the offline store
+    timestamp_field = feature_view.batch_source.timestamp_field
+    feature_names = [feature.name for feature in feature_view.features]
+    created_timestamp_column = feature_view.batch_source.created_timestamp_column
+    join_keys = [
+        entity.join_key for entity in entities if entity.join_key != DUMMY_ENTITY_ID
+    ]
+    if feature_view.batch_source.field_mapping is not None:
+        reverse_field_mapping = {
+            v: k for k, v in feature_view.batch_source.field_mapping.items()
+        }
+        timestamp_field = (
+            reverse_field_mapping[timestamp_field]
+            if timestamp_field in reverse_field_mapping.keys()
+            else timestamp_field
+        )
+        created_timestamp_column = (
+            reverse_field_mapping[created_timestamp_column]
+            if created_timestamp_column
+            and created_timestamp_column in reverse_field_mapping.keys()
+            else created_timestamp_column
+        )
+        join_keys = [
+            reverse_field_mapping[col] if col in reverse_field_mapping.keys() else col
+            for col in join_keys
+        ]
+        feature_names = [
+            reverse_field_mapping[col] if col in reverse_field_mapping.keys() else col
+            for col in feature_names
+        ]
+
+    # We need to exclude join keys and timestamp columns from the list of features, after they are mapped to
+    # their final column names via the `field_mapping` field of the source.
+    feature_names = [
+        name
+        for name in feature_names
+        if name not in join_keys
+        and name != timestamp_field
+        and name != created_timestamp_column
+    ]
+    return (
+        join_keys,
+        feature_names,
+        timestamp_field,
+        created_timestamp_column,
+    )
+
+
+def _run_field_mapping(table: pa.Table, field_mapping: Dict[str, str],) -> pa.Table:
+    # run field mapping in the forward direction
+    cols = table.column_names
+    mapped_cols = [
+        field_mapping[col] if col in field_mapping.keys() else col for col in cols
+    ]
+    table = table.rename_columns(mapped_cols)
+    return table
+
+
+def _run_dask_field_mapping(
+    table: dd.DataFrame, field_mapping: Dict[str, str],
+):
+    if field_mapping:
+        # run field mapping in the forward direction
+        table = table.rename(columns=field_mapping)
+        table = table.persist()
+
+    return table
+
+
+def _coerce_datetime(ts):
+    """
+    Depending on underlying time resolution, arrow to_pydict() sometimes returns pd
+    timestamp type (for nanosecond resolution), and sometimes you get standard python datetime
+    (for microsecond resolution).
+    While pd timestamp class is a subclass of python datetime, it doesn't always behave the
+    same way. We convert it to normal datetime so that consumers downstream don't have to deal
+    with these quirks.
+    """
+    if isinstance(ts, pd.Timestamp):
+        return ts.to_pydatetime()
+    else:
+        return ts
+
+
+def _convert_arrow_to_proto(
+    table: Union[pa.Table, pa.RecordBatch],
+    feature_view: FeatureView,
+    join_keys: Dict[str, ValueType],
+) -> List[Tuple[EntityKeyProto, Dict[str, ValueProto], datetime, Optional[datetime]]]:
+    # Avoid ChunkedArrays which guarentees `zero_copy_only` availiable.
+    if isinstance(table, pa.Table):
+        table = table.to_batches()[0]
+
+    columns = [
+        (field.name, field.dtype.to_value_type()) for field in feature_view.features
+    ] + list(join_keys.items())
+
+    proto_values_by_column = {
+        column: python_values_to_proto_values(
+            table.column(column).to_numpy(zero_copy_only=False), value_type
+        )
+        for column, value_type in columns
+    }
+
+    entity_keys = [
+        EntityKeyProto(
+            join_keys=join_keys,
+            entity_values=[proto_values_by_column[k][idx] for k in join_keys],
+        )
+        for idx in range(table.num_rows)
+    ]
+
+    # Serialize the features per row
+    feature_dict = {
+        feature.name: proto_values_by_column[feature.name]
+        for feature in feature_view.features
+    }
+    features = [dict(zip(feature_dict, vars)) for vars in zip(*feature_dict.values())]
+
+    # Convert event_timestamps
+    event_timestamps = [
+        _coerce_datetime(val)
+        for val in pd.to_datetime(
+            table.column(feature_view.batch_source.timestamp_field).to_numpy(
+                zero_copy_only=False
+            )
+        )
+    ]
+
+    # Convert created_timestamps if they exist
+    if feature_view.batch_source.created_timestamp_column:
+        created_timestamps = [
+            _coerce_datetime(val)
+            for val in pd.to_datetime(
+                table.column(
+                    feature_view.batch_source.created_timestamp_column
+                ).to_numpy(zero_copy_only=False)
+            )
+        ]
+    else:
+        created_timestamps = [None] * table.num_rows
+
+    return list(zip(entity_keys, features, event_timestamps, created_timestamps))