feast-dev · feast-ci-bot · Jul 14, 2022 · Jul 8, 2022 · Jul 8, 2022 · Jul 12, 2022
@@ -106,6 +106,15 @@ def update_infra(
 
             self._deploy_feature_server(project, image_uri)
 
+        if self.batch_engine:
+            self.batch_engine.update(
+                project,
+                tables_to_delete,
+                tables_to_keep,
+                entities_to_delete,
+                entities_to_keep,
+            )
+
     def _deploy_feature_server(self, project: str, image_uri: str):
         _logger.info("Deploying feature server...")
 
@@ -198,8 +207,7 @@ def _deploy_feature_server(self, project: str, image_uri: str):
     def teardown_infra(
         self, project: str, tables: Sequence[FeatureView], entities: Sequence[Entity],
     ) -> None:
-        if self.online_store:
-            self.online_store.teardown(self.repo_config, tables, entities)
+        super(AwsProvider, self).teardown_infra(project, tables, entities)
 
         if (
             self.repo_config.feature_server is not None

@@ -0,0 +1,25 @@
+FROM public.ecr.aws/lambda/python:3.9
+
+RUN yum install -y git
+
+
+# Copy app handler code
+COPY sdk/python/feast/infra/materialization/lambda/app.py ${LAMBDA_TASK_ROOT}
+
+# Copy necessary parts of the Feast codebase
+COPY sdk/python sdk/python
+COPY protos protos
+COPY go go
+COPY setup.py setup.py
+COPY pyproject.toml pyproject.toml
+COPY README.md README.md
+
+# Install Feast for AWS with Lambda dependencies
+# We need this mount thingy because setuptools_scm needs access to the
+# git dir to infer the version of feast we're installing.
+# https://github.com/pypa/setuptools_scm#usage-from-docker
+# I think it also assumes that this dockerfile is being built from the root of the directory.
+RUN --mount=source=.git,target=.git,type=bind pip3 install --no-cache-dir -e '.[aws,redis]'
+
+# Set the CMD to your handler (could also be done as a parameter override outside of the Dockerfile)
+CMD [ "app.handler" ]
@@ -0,0 +1,11 @@
+from .lambda_engine import (
+    LambdaMaterializationEngine,
+    LambdaMaterializationEngineConfig,
+    LambdaMaterializationJob,
+)
+
+__all__ = [
+    "LambdaMaterializationEngineConfig",
+    "LambdaMaterializationJob",
+    "LambdaMaterializationEngine",
+]
@@ -0,0 +1,82 @@
+import base64
+import json
+import sys
+import tempfile
+import traceback
+from pathlib import Path
+
+import pyarrow.parquet as pq
+
+from feast import FeatureStore
+from feast.constants import FEATURE_STORE_YAML_ENV_NAME
+from feast.infra.materialization.local_engine import DEFAULT_BATCH_SIZE
+from feast.utils import _convert_arrow_to_proto, _run_pyarrow_field_mapping
+
+
+def handler(event, context):
+    """Provide an event that contains the following keys:
+
+      - operation: one of the operations in the operations dict below
+      - tableName: required for operations that interact with DynamoDB
+      - payload: a parameter to pass to the operation being performed
+    """
+    print("Received event: " + json.dumps(event, indent=2), flush=True)
+
+    try:
+
+        config_base64 = event[FEATURE_STORE_YAML_ENV_NAME]
+
+        config_bytes = base64.b64decode(config_base64)
+
+        # Create a new unique directory for writing feature_store.yaml
+        repo_path = Path(tempfile.mkdtemp())
+
+        with open(repo_path / "feature_store.yaml", "wb") as f:
+            f.write(config_bytes)
+
+        # Initialize the feature store
+        store = FeatureStore(repo_path=str(repo_path.resolve()))
+
+        view_name = event["view_name"]
+        view_type = event["view_type"]
+        path = event["path"]
+
+        bucket = path[len("s3://") :].split("/", 1)[0]
+        key = path[len("s3://") :].split("/", 1)[1]
+        print(f"Inferred Bucket: `{bucket}` Key: `{key}`", flush=True)
+
+        if view_type == "batch":
+            # TODO: This probably needs to be become `store.get_batch_feature_view` at some point.
+            feature_view = store.get_feature_view(view_name)
+        else:
+            feature_view = store.get_stream_feature_view(view_name)
+
+        print(f"Got Feature View: `{feature_view}`", flush=True)
+
+        table = pq.read_table(path)
+        if feature_view.batch_source.field_mapping is not None:
+            table = _run_pyarrow_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
+        }
+
+        written_rows = 0
+
+        for batch in table.to_batches(DEFAULT_BATCH_SIZE):
+            rows_to_write = _convert_arrow_to_proto(
+                batch, feature_view, join_key_to_value_type
+            )
+            store._provider.online_write_batch(
+                store.config, feature_view, rows_to_write, lambda x: None,
+            )
+            written_rows += len(rows_to_write)
+        return {"written_rows": written_rows}
+    except Exception as e:
+        print(f"Exception: {e}", flush=True)
+        print("Traceback:", flush=True)
+        print(traceback.format_exc(), flush=True)
+        sys.exit(1)
@@ -0,0 +1,238 @@
+import base64
+import json
+import logging
+from concurrent.futures import ThreadPoolExecutor, wait
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Callable, List, Literal, Optional, Sequence, Union
+
+import boto3
+from pydantic import StrictStr
+from tqdm import tqdm
+
+from feast.batch_feature_view import BatchFeatureView
+from feast.constants import FEATURE_STORE_YAML_ENV_NAME
+from feast.entity import Entity
+from feast.feature_view import FeatureView
+from feast.infra.materialization.batch_materialization_engine import (
+    BatchMaterializationEngine,
+    MaterializationJob,
+    MaterializationJobStatus,
+    MaterializationTask,
+)
+from feast.infra.offline_stores.offline_store import OfflineStore
+from feast.infra.online_stores.online_store import OnlineStore
+from feast.registry import BaseRegistry
+from feast.repo_config import FeastConfigBaseModel, RepoConfig
+from feast.stream_feature_view import StreamFeatureView
+from feast.utils import _get_column_names
+from feast.version import get_version
+
+DEFAULT_BATCH_SIZE = 10_000
+
+logger = logging.getLogger(__name__)
+
+
+class LambdaMaterializationEngineConfig(FeastConfigBaseModel):
+    """Batch Materialization Engine config for lambda based engine"""
+
+    type: Literal["lambda"] = "lambda"
+    """ Type selector"""
+
+    materialization_image: StrictStr
+    """ The URI of a container image in the Amazon ECR registry, which should be used for materialization. """
+
+    lambda_role: StrictStr
+    """ Role that should be used by the materialization lambda """
+
+
+@dataclass
+class LambdaMaterializationJob(MaterializationJob):
+    def __init__(self, job_id: str, status: MaterializationJobStatus) -> None:
+        super().__init__()
+        self._job_id: str = job_id
+        self._status = status
+        self._error = None
+
+    def status(self) -> MaterializationJobStatus:
+        return self._status
+
+    def error(self) -> Optional[BaseException]:
+        return self._error
+
+    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 LambdaMaterializationEngine(BatchMaterializationEngine):
+    """
+    WARNING: This engine should be considered "Alpha" functionality.
+    """
+
+    def update(
+        self,
+        project: str,
+        views_to_delete: Sequence[
+            Union[BatchFeatureView, StreamFeatureView, FeatureView]
+        ],
+        views_to_keep: Sequence[
+            Union[BatchFeatureView, StreamFeatureView, FeatureView]
+        ],
+        entities_to_delete: Sequence[Entity],
+        entities_to_keep: Sequence[Entity],
+    ):
+        # This should be setting up the lambda function.
+        r = self.lambda_client.create_function(
+            FunctionName=self.lambda_name,
+            PackageType="Image",
+            Role=self.repo_config.batch_engine.lambda_role,
+            Code={"ImageUri": self.repo_config.batch_engine.materialization_image},
+            Timeout=600,
+            Tags={
+                "feast-owned": "True",
+                "project": project,
+                "feast-sdk-version": get_version(),
+            },
+        )
+        logger.info("Creating lambda function %s, %s", self.lambda_name, r)
+
+        logger.info("Waiting for function %s to be active", self.lambda_name)
+        waiter = self.lambda_client.get_waiter("function_active")
+        waiter.wait(FunctionName=self.lambda_name)
+
+    def teardown_infra(
+        self,
+        project: str,
+        fvs: Sequence[Union[BatchFeatureView, StreamFeatureView, FeatureView]],
+        entities: Sequence[Entity],
+    ):
+        # This should be tearing down the lambda function.
+        logger.info("Tearing down lambda %s", self.lambda_name)
+        r = self.lambda_client.delete_function(FunctionName=self.lambda_name)
+        logger.info("Finished tearing down lambda %s: %s", self.lambda_name, r)
+
+    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,
+        )
+        repo_path = self.repo_config.repo_path
+        assert repo_path
+        feature_store_path = repo_path / "feature_store.yaml"
+        self.feature_store_base64 = str(
+            base64.b64encode(bytes(feature_store_path.read_text(), "UTF-8")), "UTF-8"
+        )
+
+        self.lambda_name = f"feast-materialize-{self.repo_config.project}"
+        if len(self.lambda_name) > 64:
+            self.lambda_name = self.lambda_name[:64]
+        self.lambda_client = boto3.client("lambda")
+
+    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: BaseRegistry,
+        feature_view: Union[BatchFeatureView, StreamFeatureView, FeatureView],
+        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)
+
+        job_id = f"{feature_view.name}-{start_date}-{end_date}"
+
+        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,
+        )
+
+        paths = offline_job.to_remote_storage()
+        max_workers = len(paths) if len(paths) <= 20 else 20
+        executor = ThreadPoolExecutor(max_workers=max_workers)
+        futures = []
+
+        for path in paths:
+            payload = {
+                FEATURE_STORE_YAML_ENV_NAME: self.feature_store_base64,
+                "view_name": feature_view.name,
+                "view_type": "batch",
+                "path": path,
+            }
+            # Invoke a lambda to materialize this file.
+
+            logger.info("Invoking materialization for %s", path)
+            futures.append(
+                executor.submit(
+                    self.lambda_client.invoke,
+                    FunctionName=self.lambda_name,
+                    InvocationType="RequestResponse",
+                    Payload=json.dumps(payload),
+                )
+            )
+
+        done, not_done = wait(futures)
+        logger.info("Done: %s Not Done: %s", done, not_done)
+        for f in done:
+            response = f.result()
+            output = json.loads(response["Payload"].read())
+
+            logger.info(
+                f"Ingested task; request id {response['ResponseMetadata']['RequestId']}, "
+                f"rows written: {output['written_rows']}"
+            )
+
+        for f in not_done:
+            response = f.result()
+            logger.error(f"Ingestion failed: {response}")
+
+        return LambdaMaterializationJob(
+            job_id=job_id,
+            status=MaterializationJobStatus.SUCCEEDED
+            if not not_done
+            else MaterializationJobStatus.ERROR,
+        )
@@ -229,7 +229,8 @@ def online_read(
                 break
             batch_entity_ids = {
                 table_instance.name: {
-                    "Keys": [{"entity_id": entity_id} for entity_id in batch]
+                    "Keys": [{"entity_id": entity_id} for entity_id in batch],
+                    "ConsistentRead": True,
                 }
             }
             with tracing_span(name="remote_call"):