# Copyright 2025 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 asyncio

import functools

import os

import sys

import threading

import time

import traceback

from collections import defaultdict

from concurrent.futures import ThreadPoolExecutor

from contextlib import asynccontextmanager

from datetime import datetime

from importlib import resources as importlib_resources

from types import SimpleNamespace

from typing import Any, DefaultDict, Dict, List, NamedTuple, Optional, Set, Union

import pandas as pd

from dateutil import parser

from fastapi import (

Depends,

FastAPI,

Query,

Request,

Response,

WebSocket,

WebSocketDisconnect,

status,

)

from fastapi.concurrency import run_in_threadpool

from fastapi.logger import logger

from fastapi.responses import JSONResponse

from fastapi.staticfiles import StaticFiles

from pydantic import BaseModel, Field, field_validator

import feast

from feast import metrics as feast_metrics

from feast import proto_json, utils

from feast.constants import DEFAULT_FEATURE_SERVER_REGISTRY_TTL

from feast.data_source import PushMode

from feast.errors import (

FeastError,

FeatureViewNotFoundException,

)

from feast.feast_object import FeastObject

from feast.feature_server_utils import convert_response_to_dict

from feast.feature_view import FeatureViewState

from feast.feature_view_utils import get_feature_view_from_feature_store

from feast.filter_models import ComparisonFilter, CompoundFilter

from feast.infra.feature_servers.base_config import MetricsConfig

from feast.permissions.action import WRITE, AuthzedAction

from feast.permissions.security_manager import (

assert_permissions,

get_security_manager,

is_auth_necessary,

)

from feast.permissions.server.rest import inject_user_details

from feast.permissions.server.utils import (

ServerType,

init_auth_manager,

init_security_manager,

str_to_auth_manager_type,

)

from feast.vector_store_utils import VectorStoreRegistry, build_vector_store_object

# TODO: deprecate this in favor of push features

class WriteToFeatureStoreRequest(BaseModel):

feature_view_name: str

df: dict

allow_registry_cache: bool = True

transform_on_write: bool = True

class PushFeaturesRequest(BaseModel):

push_source_name: str

df: dict

allow_registry_cache: bool = True

to: str = "online"

transform_on_write: bool = True

class MaterializeRequest(BaseModel):

start_ts: Optional[str] = None

end_ts: Optional[str] = None

feature_views: Optional[List[str]] = None

disable_event_timestamp: bool = False

full_feature_names: bool = False

version: Optional[str] = Field(

None,

description=(

"Optional version to materialize (e.g. 'v2'). Requires feature_views "

"with exactly one entry and registry.enable_online_feature_view_versioning."

),

)

class MaterializeIncrementalRequest(BaseModel):

end_ts: str

feature_views: Optional[List[str]] = None

full_feature_names: bool = False

version: Optional[str] = Field(

None,

description=(

"Optional version to materialize (e.g. 'v2'). Requires feature_views "

"with exactly one entry and registry.enable_online_feature_view_versioning."

),

)

class GetOnlineFeaturesRequest(BaseModel):

entities: Dict[str, List[Any]]

feature_service: Optional[str] = None

features: List[str] = []

full_feature_names: bool = False

include_feature_view_version_metadata: bool = False

class GetOnlineDocumentsRequest(BaseModel):

feature_service: Optional[str] = None

features: List[str] = []

full_feature_names: bool = False

include_feature_view_version_metadata: bool = False

top_k: Optional[int] = None

query: Optional[List[float]] = None

query_string: Optional[str] = None

distance_metric: Optional[str] = None

api_version: Optional[int] = 1

filters: Optional[Union[ComparisonFilter, CompoundFilter]] = None

class OpenAISearchMetadata(BaseModel):

features_to_retrieve: Optional[List[str]] = None

content_field: Optional[str] = None

class OpenAIRankingOptions(BaseModel):

ranker: Optional[str] = None

score_threshold: Optional[float] = None

class OpenAISearchRequest(BaseModel):

query: Union[str, List[str]]

filters: Optional[Union[ComparisonFilter, CompoundFilter]] = None

max_num_results: Optional[int] = 10

ranking_options: Optional[OpenAIRankingOptions] = None

rewrite_query: Optional[bool] = None

metadata: Optional[OpenAISearchMetadata] = None

class OpenAIVectorStoreObject(BaseModel):

id: str

object: str = "vector_store"

name: str

status: str = "completed"

created_at: int = 0

class FeatureVectorResponse(BaseModel):

values: List[Any] = []

statuses: List[str] = []

event_timestamps: List[str] = []

class FeatureViewMetadataResponse(BaseModel):

name: str = ""

version: int = 0

class OnlineFeaturesMetadataResponse(BaseModel):

feature_names: List[str] = []

feature_view_metadata: List[FeatureViewMetadataResponse] = []

@field_validator("feature_names", mode="before")

@classmethod

def _unwrap_feature_list(cls, v: Any) -> Any:

"""Accept both the proto_json-patched flat list and the raw

protobuf ``{"val": [...]}`` dict produced by ``MessageToDict``

when the monkey-patch is absent or ineffective."""

if isinstance(v, dict) and "val" in v:

return v["val"]

return v

class OnlineFeaturesResponse(BaseModel):

metadata: Optional[OnlineFeaturesMetadataResponse] = None

results: List[FeatureVectorResponse] = []

status: Optional[bool] = None

class ChatMessage(BaseModel):

role: str

content: str

class ChatRequest(BaseModel):

messages: List[ChatMessage]

def _parse_feature_info(

features: Union[List[str], "feast.FeatureService"],

) -> tuple:

"""Return ``(feature_view_names, feature_count)`` from resolved features.

``features`` is either a list of ``"feature_view:feature"`` strings or

a ``FeatureService`` with ``feature_view_projections``.

Returns:

(fv_names, feat_count) where fv_names is a list of unique feature

view name strings and feat_count is the total number of features.

"""

from feast.feature_service import FeatureService

from feast.utils import _parse_feature_ref

if isinstance(features, FeatureService):

projections = features.feature_view_projections

fv_names = [p.name for p in projections]

feat_count = sum(len(p.features) for p in projections)

elif isinstance(features, list):

feat_count = len(features)

fv_names = list({_parse_feature_ref(ref)[0] for ref in features if ":" in ref})

else:

fv_names = []

feat_count = 0

return fv_names, feat_count

def _resolve_feature_counts(

features: Union[List[str], "feast.FeatureService"],

) -> tuple:

"""Return ``(feature_count_str, feature_view_count_str)`` for Prometheus labels."""

fv_names, feat_count = _parse_feature_info(features)

return str(feat_count), str(len(fv_names))

def bin_feature_count(count: int, bins: List[int]) -> str:

"""Map a raw feature count to an inclusive range label."""

if count == 0:

return "0"

lower = 1

for upper in bins:

if count <= upper:

return f"{lower}-{upper}"

lower = upper + 1

return f"{lower}+"

def _emit_online_audit(

request: GetOnlineFeaturesRequest,

features: Union[List[str], "feast.FeatureService"],

entity_count: int,

status: str,

latency_ms: float,

):

"""Best-effort audit log emission for online feature requests."""

if not feast_metrics._config.audit_logging:

return

try:

from feast.permissions.security_manager import get_security_manager

requestor_id = "anonymous"

sm = get_security_manager()

if sm and sm.current_user:

requestor_id = sm.current_user.username or "anonymous"

fv_names, feat_count = _parse_feature_info(features)

feast_metrics.emit_online_audit_log(

requestor_id=requestor_id,

entity_keys=list(request.entities.keys()),

entity_count=entity_count,

feature_views=fv_names,

feature_count=feat_count,

status=status,

latency_ms=latency_ms,

)

except Exception:

logger.warning("Failed to emit online audit log", exc_info=True)

async def _get_features(

request: Union[GetOnlineFeaturesRequest, GetOnlineDocumentsRequest],

store: "feast.FeatureStore",

):

if request.feature_service:

feature_service = await run_in_threadpool(

store.get_feature_service, request.feature_service, allow_cache=True

)

assert_permissions(

resource=feature_service, actions=[AuthzedAction.READ_ONLINE]

)

features = feature_service # type: ignore

elif is_auth_necessary(get_security_manager()):

all_feature_views, all_on_demand_feature_views = await run_in_threadpool(

utils._get_feature_views_to_use,

store.registry,

store.project,

request.features,

allow_cache=True,

hide_dummy_entity=False,

)

for feature_view in all_feature_views:

assert_permissions(

resource=feature_view, actions=[AuthzedAction.READ_ONLINE]

)

for od_feature_view in all_on_demand_feature_views:

assert_permissions(

resource=od_feature_view, actions=[AuthzedAction.READ_ONLINE]

)

features = request.features # type: ignore

else:

features = request.features # type: ignore

return features

async def load_static_artifacts(app: FastAPI, store):

"""

Load static artifacts (models, lookup tables, etc.) into app.state.

This function can be extended to load various types of static artifacts:

- Small ML models (scikit-learn, small neural networks)

- Lookup tables and reference data

- Configuration parameters

- Pre-computed embeddings

Note: Not recommended for large language models - use dedicated

model serving solutions (vLLM, TGI, etc.) for those.

"""

try:

# Import here to avoid loading heavy dependencies unless needed

import importlib.util

import inspect

from pathlib import Path

# Look for static artifacts loading in the feature repository

# This allows templates and users to define their own artifact loading

repo_path = Path(store.repo_path) if store.repo_path else Path.cwd()

artifacts_file = repo_path / "static_artifacts.py"

if artifacts_file.exists():

# Load and execute custom static artifacts loading

spec = importlib.util.spec_from_file_location(

"static_artifacts", artifacts_file

)

if spec and spec.loader:

artifacts_module = importlib.util.module_from_spec(spec)

spec.loader.exec_module(artifacts_module)

# Look for load_artifacts function

if hasattr(artifacts_module, "load_artifacts"):

load_func = artifacts_module.load_artifacts

if inspect.iscoroutinefunction(load_func):

await load_func(app)

else:

load_func(app)

logger.info("Loaded static artifacts from static_artifacts.py")

except Exception as e:

# Non-fatal error - feature server should still start

logger.warning(f"Failed to load static artifacts: {e}")

def _authorize_materialize_views(

store: "feast.FeatureStore",

feature_view_names: Optional[List[str]],

version: Optional[str] = None,

) -> List[str]:

"""Resolve + authorize feature views for materialization.

Returns the resolved list of FV names (all eligible FVs when

feature_view_names is None).

"""

parsed_version = store._validate_materialize_version(version, feature_view_names)

feature_views_to_materialize = store._get_feature_views_to_materialize(

feature_view_names, version=parsed_version

)

for fv in feature_views_to_materialize:

assert_permissions(

resource=fv,

actions=[AuthzedAction.WRITE_ONLINE],

)

return [fv.name for fv in feature_views_to_materialize]

def _check_already_materializing(

store: "feast.FeatureStore",

fv_names: List[str],

) -> Optional[JSONResponse]:

"""Return a 409 JSONResponse if any requested FV is already MATERIALIZING."""

conflicting: List[str] = []

for fv_name in fv_names:

try:

fv = store.registry.get_feature_view(

fv_name, store.project, allow_cache=False

)

if getattr(fv, "state", None) == FeatureViewState.MATERIALIZING:

conflicting.append(fv_name)

except (FeatureViewNotFoundException, KeyError):

pass

except Exception as e:

logger.warning(

f"Unexpected error checking MATERIALIZING state for {fv_name}: {e}"

)

if conflicting:

return JSONResponse(

status_code=409,

content={

"error": (

f"Cannot start async materialization — the following feature "

f"views are already in MATERIALIZING state: {conflicting}. "

f"Use ?force=true to override."

),

"feature_views": conflicting,

},

)

return None

def _update_fv_state(

store: "feast.FeatureStore",

fv_names: List[str],

state: FeatureViewState,

) -> None:

"""Set FV state in the registry for each named feature view."""

for fv_name in fv_names:

try:

fv = store.registry.get_feature_view(

fv_name, store.project, allow_cache=False

)

fv.state = state

store.registry.apply_feature_view(fv, store.project)

except (FeatureViewNotFoundException, KeyError):

logger.warning(f"Feature view {fv_name} not found; skip state={state}")

except Exception as e:

logger.warning(f"Failed to set state={state} for {fv_name}: {e}")

def _reset_stuck_materializing_to_generated(

store: "feast.FeatureStore",

fv_names: List[str],

) -> None:

"""Reset FVs currently in MATERIALIZING to GENERATED (force override)."""

stuck: List[str] = []

for fv_name in fv_names:

try:

fv = store.registry.get_feature_view(

fv_name, store.project, allow_cache=False

)

if getattr(fv, "state", None) == FeatureViewState.MATERIALIZING:

stuck.append(fv_name)

except (FeatureViewNotFoundException, KeyError):

pass

except Exception as e:

logger.warning(f"Unexpected error while force-resetting {fv_name}: {e}")

if stuck:

_update_fv_state(store, stuck, FeatureViewState.GENERATED)

logger.info(

"Force reset MATERIALIZING → GENERATED for feature views: %s", stuck

)

def _parse_materialize_timestamps(

request: "MaterializeRequest",

) -> tuple:

"""Parse and validate start/end timestamps from a MaterializeRequest."""

if request.disable_event_timestamp:

now = datetime.now()

return datetime(1970, 1, 1), now

if not request.start_ts or not request.end_ts:

raise ValueError(

"start_ts and end_ts are required when disable_event_timestamp is False"

)

try:

start_date = utils.make_tzaware(parser.parse(request.start_ts))

end_date = utils.make_tzaware(parser.parse(request.end_ts))

except (ValueError, TypeError) as e:

raise ValueError(f"Invalid timestamp format: {e}") from e

if start_date >= end_date:

raise ValueError(f"start_ts ({start_date}) must be before end_ts ({end_date})")

return start_date, end_date

def get_app(

store: "feast.FeatureStore",

registry_ttl_sec: int = DEFAULT_FEATURE_SERVER_REGISTRY_TTL,

):

"""

Creates a FastAPI app that can be used to start a feature server.

Args:

store: The FeatureStore to use for serving features

registry_ttl_sec: The TTL in seconds for the registry cache

Returns:

A FastAPI app

Example:

```python

from feast import FeatureStore

store = FeatureStore(repo_path="feature_repo")

app = get_app(store)

```

The app provides the following endpoints:

- `/get-online-features`: Get online features

- `/search`: Vector similarity search (RAG)

- `/retrieve-online-documents`: Deprecated alias for `/search`

- `/v1/vector_stores`: List vector stores (GET)

- `/v1/vector_stores/{vector_store_id}`: Get a vector store (GET)

- `/v1/vector_stores/{vector_store_id}/search`: OpenAI-compatible vector search

- `/push`: Push features to the feature store

- `/write-to-online-store`: Write to the online store

- `/health`: Health check

- `/materialize`: Materialize features

- `/materialize-incremental`: Materialize features incrementally

- `/chat`: Chat UI

- `/ws/chat`: WebSocket endpoint for chat

MCP Support:

- If MCP is enabled in feature server configuration, MCP endpoints will be added automatically

"""

proto_json.patch()

# Asynchronously refresh registry, notifying shutdown and canceling the active timer if the app is shutting down

shutting_down = False

active_timer: Optional[threading.Timer] = None

# --- Offline write batching config and batcher ---

fs_cfg = getattr(store.config, "feature_server", None)

batching_cfg = None

if fs_cfg is not None:

enabled = getattr(fs_cfg, "offline_push_batching_enabled", False)

batch_size = getattr(fs_cfg, "offline_push_batching_batch_size", None)

batch_interval_seconds = getattr(

fs_cfg, "offline_push_batching_batch_interval_seconds", None

)

if enabled is True:

size_ok = isinstance(batch_size, int) and not isinstance(batch_size, bool)

interval_ok = isinstance(batch_interval_seconds, int) and not isinstance(

batch_interval_seconds, bool

)

if size_ok and interval_ok:

batching_cfg = SimpleNamespace(

enabled=True,

batch_size=batch_size,

batch_interval_seconds=batch_interval_seconds,

)

else:

logger.warning(

"Offline write batching enabled but missing or invalid numeric values; "

"disabling batching (batch_size=%r, batch_interval_seconds=%r)",

batch_size,

batch_interval_seconds,

)

offline_batcher: Optional[OfflineWriteBatcher] = None

if batching_cfg is not None and batching_cfg.enabled is True:

offline_batcher = OfflineWriteBatcher(store=store, cfg=batching_cfg)

logger.debug("Offline write batching is ENABLED")

else:

logger.debug("Offline write batching is DISABLED")

# Dedicated pool for async materialize so long Spark/offline waits do not

# starve the default executor used by online serving and run_in_threadpool.

_mat_workers_raw = os.environ.get("FEAST_MATERIALIZE_MAX_WORKERS", "2")

try:

materialize_max_workers = max(1, int(_mat_workers_raw))

except ValueError:

logger.warning(

"Invalid FEAST_MATERIALIZE_MAX_WORKERS=%r; using default 2",

_mat_workers_raw,

)

materialize_max_workers = 2

materialize_executor = ThreadPoolExecutor(

max_workers=materialize_max_workers,

thread_name_prefix="feast-materialize",

)

def stop_refresh():

nonlocal shutting_down

shutting_down = True

if active_timer:

active_timer.cancel()

vs_registry = VectorStoreRegistry(store)

def async_refresh():

if shutting_down:

return

store.refresh_registry()

vs_registry.refresh()

if registry_ttl_sec:

nonlocal active_timer

active_timer = threading.Timer(registry_ttl_sec, async_refresh)

active_timer.start()

# --- Audit logging setup ---

audit_logging_cfg = getattr(fs_cfg, "audit_logging", None)

audit_logger_instance = None

if audit_logging_cfg is not None and getattr(audit_logging_cfg, "enabled", False):

from feast.audit.audit_logger import create_audit_logger_from_config

audit_logger_instance = create_audit_logger_from_config(audit_logging_cfg)

if audit_logger_instance:

logger.info(

"Structured audit logging is ENABLED (sink=%s)",

getattr(audit_logging_cfg, "sink", "stdout"),

)

@asynccontextmanager

async def lifespan(app: FastAPI):

# Load static artifacts before initializing store

await load_static_artifacts(app, store)

if audit_logger_instance is not None:

app.state.audit_logger = audit_logger_instance

await store.initialize()

async_refresh()

try:

yield

finally:

stop_refresh()

if offline_batcher is not None:

offline_batcher.shutdown()

# wait=False: do not block process exit on in-flight materialize

# (same fire-and-forget contract as returning 202 mid-job).

materialize_executor.shutdown(wait=False)

if audit_logger_instance is not None:

audit_logger_instance.close()

await store.close()

app = FastAPI(lifespan=lifespan)

# Add audit logging middleware when enabled (REST only;

# MCP audit is handled at the protocol layer in mcp_server.py)

if audit_logger_instance is not None:

from feast.audit.audit_middleware import AuditLoggingMiddleware

app.add_middleware(AuditLoggingMiddleware)

fs_cfg = getattr(store.config, "feature_server", None)

metrics_cfg = getattr(fs_cfg, "metrics", None)

default_feature_count_bins = MetricsConfig().feature_count_bins

feature_count_bins = (

getattr(metrics_cfg, "feature_count_bins", default_feature_count_bins)

if metrics_cfg is not None

else default_feature_count_bins

)

@app.post(

"/get-online-features",

dependencies=[Depends(inject_user_details)],

response_model=OnlineFeaturesResponse,

)

async def get_online_features(request: GetOnlineFeaturesRequest) -> Any:

with feast_metrics.track_request_latency(

"/get-online-features",

) as metrics_ctx:

features = await _get_features(request, store)

feat_count, fv_count = _resolve_feature_counts(features)

metrics_ctx.feature_count = bin_feature_count(

int(feat_count),

feature_count_bins,

)

metrics_ctx.feature_view_count = fv_count

entity_count = len(next(iter(request.entities.values()), []))

feast_metrics.track_online_features_entities(entity_count)

read_params = dict(

features=features,

entity_rows=request.entities,

full_feature_names=request.full_feature_names,

include_feature_view_version_metadata=request.include_feature_view_version_metadata,

)

audit_start_ms = 0.0

audit_status = "success"

try:

if feast_metrics._config.audit_logging:

audit_start_ms = time.monotonic() * 1000

if store._get_provider().async_supported.online.read:

response = await store.get_online_features_async(**read_params) # type: ignore

else:

response = await run_in_threadpool(

lambda: store.get_online_features(**read_params) # type: ignore

)

except Exception:

audit_status = "error"

raise

finally:

if feast_metrics._config.audit_logging:

audit_latency_ms = time.monotonic() * 1000 - audit_start_ms

_emit_online_audit(

request, features, entity_count, audit_status, audit_latency_ms

)

response_dict = await run_in_threadpool(

convert_response_to_dict, response.proto

)

return JSONResponse(content=response_dict)

async def _search_online_documents(

request: GetOnlineDocumentsRequest,

*,

metrics_path: str,

) -> JSONResponse:

with feast_metrics.track_request_latency(metrics_path):

features = await _get_features(request, store)

read_params = dict(

features=features,

query=request.query,

top_k=request.top_k,

)

if request.api_version == 2 and request.query_string is not None:

read_params["query_string"] = request.query_string

if request.api_version == 2 and request.distance_metric is not None:

read_params["distance_metric"] = request.distance_metric

if request.api_version == 2 and request.filters is not None:

read_params["filters"] = request.filters

if request.api_version == 2:

read_params["include_feature_view_version_metadata"] = (

request.include_feature_view_version_metadata

)

response = await run_in_threadpool(

lambda: store.retrieve_online_documents_v2(**read_params) # type: ignore

)

else:

response = await run_in_threadpool(

lambda: store.retrieve_online_documents(**read_params) # type: ignore

)

response_dict = await run_in_threadpool(

convert_response_to_dict, response.proto

)

return JSONResponse(content=response_dict)

@app.post(

"/search",

dependencies=[Depends(inject_user_details)],

response_model=OnlineFeaturesResponse,

)

async def search(request: GetOnlineDocumentsRequest) -> JSONResponse:

"""Vector similarity search against online document embeddings."""

return await _search_online_documents(request, metrics_path="/search")

@app.post(

"/retrieve-online-documents",

dependencies=[Depends(inject_user_details)],

response_model=OnlineFeaturesResponse,

include_in_schema=False,

)

async def retrieve_online_documents(

request: GetOnlineDocumentsRequest,

) -> JSONResponse:

logger.warning(

"POST /retrieve-online-documents is deprecated; use POST /search instead."

)

return await _search_online_documents(

request, metrics_path="/retrieve-online-documents"

)

@app.get(

"/v1/vector_stores",

dependencies=[Depends(inject_user_details)],

)

async def list_vector_stores() -> JSONResponse:

permitted: list = []

for obj in vs_registry.list_vector_stores():

fv = vs_registry.resolve(obj["id"])

try:

assert_permissions(resource=fv, actions=[AuthzedAction.DESCRIBE])

permitted.append(obj)

except Exception:

pass

return JSONResponse(content={"object": "list", "data": permitted})

@app.get(

"/v1/vector_stores/{vector_store_id}",

dependencies=[Depends(inject_user_details)],

)

async def get_vector_store(vector_store_id: str) -> JSONResponse:

try:

fv = vs_registry.resolve(vector_store_id)

assert_permissions(resource=fv, actions=[AuthzedAction.DESCRIBE])

except FeatureViewNotFoundException:

return JSONResponse(

status_code=404,

content={

"error": {

"message": f"No vector store found with id '{vector_store_id}'",

"type": "not_found_error",

}

},

)

return JSONResponse(content=build_vector_store_object(store.project, fv))

@app.post(

"/v1/vector_stores/{vector_store_id}/search",

dependencies=[Depends(inject_user_details)],

)

async def vector_store_search(

vector_store_id: str,

request: OpenAISearchRequest,

) -> JSONResponse:

with feast_metrics.track_request_latency(

"/v1/vector_stores/{vector_store_id}/search"

):

try:

feature_view = vs_registry.resolve(vector_store_id)

assert_permissions(

resource=feature_view,

actions=[AuthzedAction.READ_ONLINE],

)

result = await store.openai_search(

vector_store_id=feature_view.name,

query=request.query,

vs_id=vector_store_id,

max_num_results=request.max_num_results or 10,

filters=request.filters,

ranking_options=(

request.ranking_options.model_dump()

if request.ranking_options

else None

),

rewrite_query=request.rewrite_query,

features_to_retrieve=(

request.metadata.features_to_retrieve

if request.metadata

else None

),

)

except FeatureViewNotFoundException:

return JSONResponse(

status_code=404,

content={

"error": {

"message": f"No vector store found with id '{vector_store_id}'",

"type": "not_found_error",

}

},

)

except ValueError as e:

return JSONResponse(

status_code=422,

content={

"error": {

"message": str(e),

"type": "invalid_request_error",

}

},

)

return JSONResponse(content=result)

@app.post("/push", dependencies=[Depends(inject_user_details)])

async def push(request: PushFeaturesRequest) -> Response:

with feast_metrics.track_request_latency("/push"):

df = pd.DataFrame(request.df)

actions = []

if request.to == "offline":

to = PushMode.OFFLINE

actions = [AuthzedAction.WRITE_OFFLINE]

elif request.to == "online":

to = PushMode.ONLINE

actions = [AuthzedAction.WRITE_ONLINE]

elif request.to == "online_and_offline":

to = PushMode.ONLINE_AND_OFFLINE

actions = WRITE

else:

raise ValueError(

f"{request.to} is not a supported push format. Please specify one of these ['online', 'offline', 'online_and_offline']."

)

from feast.data_source import PushSource

all_fvs = store.list_feature_views(

allow_cache=request.allow_registry_cache

) + store.list_stream_feature_views(

allow_cache=request.allow_registry_cache

)

fvs_with_push_sources = {

fv

for fv in all_fvs

if (

fv.stream_source is not None

and isinstance(fv.stream_source, PushSource)

and fv.stream_source.name == request.push_source_name

)

}

for feature_view in fvs_with_push_sources:

assert_permissions(resource=feature_view, actions=actions)

async def _push_with_to(push_to: PushMode) -> None:

"""

Helper for performing a single push operation.

NOTE:

- Feast providers **do not currently support async offline writes**.

- Therefore:

* ONLINE and ONLINE_AND_OFFLINE → may be async, depending on provider.async_supported.online.write

* OFFLINE → always synchronous, but executed via run_in_threadpool when called from HTTP handlers.

- The OfflineWriteBatcher handles offline writes directly in its own background thread, but the offline store writes are currently synchronous only.

"""

push_source_name = request.push_source_name

allow_registry_cache = request.allow_registry_cache

transform_on_write = request.transform_on_write

# Async currently only applies to online store writes (ONLINE / ONLINE_AND_OFFLINE paths) as theres no async for offline store

if push_to in (PushMode.ONLINE, PushMode.ONLINE_AND_OFFLINE) and (

store._get_provider().async_supported.online.write

):

await store.push_async(

push_source_name=push_source_name,

df=df,

allow_registry_cache=allow_registry_cache,

to=push_to,

transform_on_write=transform_on_write,

)

else:

await run_in_threadpool(

lambda: store.push(

push_source_name=push_source_name,

df=df,

allow_registry_cache=allow_registry_cache,

to=push_to,

transform_on_write=transform_on_write,

)

)

needs_online = to in (PushMode.ONLINE, PushMode.ONLINE_AND_OFFLINE)

needs_offline = to in (PushMode.OFFLINE, PushMode.ONLINE_AND_OFFLINE)

status_code = status.HTTP_200_OK

if offline_batcher is None or not needs_offline:

await _push_with_to(to)

else:

if needs_online:

await _push_with_to(PushMode.ONLINE)

offline_batcher.enqueue(

push_source_name=request.push_source_name,

df=df,

allow_registry_cache=request.allow_registry_cache,

transform_on_write=request.transform_on_write,

)

status_code = status.HTTP_202_ACCEPTED

feast_metrics.track_push(request.push_source_name, request.to)

return Response(status_code=status_code)

async def _get_feast_object(

feature_view_name: str, allow_registry_cache: bool

) -> FeastObject:

return await run_in_threadpool(

get_feature_view_from_feature_store,

store,

feature_view_name,

allow_registry_cache,

)

@app.post("/write-to-online-store", dependencies=[Depends(inject_user_details)])

async def write_to_online_store(request: WriteToFeatureStoreRequest) -> None:

df = pd.DataFrame(request.df)

feature_view_name = request.feature_view_name

allow_registry_cache = request.allow_registry_cache

resource = await _get_feast_object(feature_view_name, allow_registry_cache)

assert_permissions(resource=resource, actions=[AuthzedAction.WRITE_ONLINE])

await run_in_threadpool(

store.write_to_online_store,

feature_view_name=feature_view_name,

df=df,

allow_registry_cache=allow_registry_cache,

transform_on_write=request.transform_on_write,

)