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sqlite.py
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848 lines (750 loc) · 29.2 KB
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# Copyright 2021 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 itertools
import logging
import os
import sqlite3
import sys
from datetime import date, datetime, timezone
from pathlib import Path
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Tuple,
Union,
cast,
)
from pydantic import StrictStr
from feast import Entity
from feast.feature_view import FeatureView
from feast.field import Field
from feast.infra.infra_object import SQLITE_INFRA_OBJECT_CLASS_TYPE, InfraObject
from feast.infra.key_encoding_utils import (
deserialize_entity_key,
serialize_entity_key,
serialize_f32,
)
from feast.infra.online_stores.online_store import OnlineStore
from feast.infra.online_stores.vector_store import VectorStoreConfig
from feast.protos.feast.core.InfraObject_pb2 import InfraObject as InfraObjectProto
from feast.protos.feast.core.Registry_pb2 import Registry as RegistryProto
from feast.protos.feast.core.SqliteTable_pb2 import SqliteTable as SqliteTableProto
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, RepoConfig
from feast.type_map import feast_value_type_to_python_type
from feast.types import FEAST_VECTOR_TYPES, PrimitiveFeastType
from feast.utils import (
_build_retrieve_online_document_record,
_get_feature_view_vector_field_metadata,
_serialize_vector_to_float_list,
to_naive_utc,
)
def adapt_date_iso(val: date):
"""Adapt datetime.date to ISO 8601 date."""
return val.isoformat()
def adapt_datetime_iso(val: datetime):
"""Adapt datetime.datetime to timezone-naive ISO 8601 date."""
return val.isoformat()
def adapt_datetime_epoch(val: datetime):
"""Adapt datetime.datetime to Unix timestamp."""
return int(val.timestamp())
sqlite3.register_adapter(date, adapt_date_iso)
sqlite3.register_adapter(datetime, adapt_datetime_iso)
sqlite3.register_adapter(datetime, adapt_datetime_epoch)
def convert_date(val: bytes):
"""Convert ISO 8601 date to datetime.date object."""
return date.fromisoformat(val.decode())
def convert_datetime(val: bytes):
"""Convert ISO 8601 datetime to datetime.datetime object."""
return datetime.fromisoformat(val.decode())
def convert_timestamp(val: bytes):
"""Convert Unix epoch timestamp to datetime.datetime object."""
return datetime.fromtimestamp(int(val))
sqlite3.register_converter("date", convert_date)
sqlite3.register_converter("datetime", convert_datetime)
sqlite3.register_converter("timestamp", convert_timestamp)
class SqliteOnlineStoreConfig(FeastConfigBaseModel, VectorStoreConfig):
"""Online store config for local (SQLite-based) store"""
type: Literal["sqlite", "feast.infra.online_stores.sqlite.SqliteOnlineStore"] = (
"sqlite"
)
""" Online store type selector"""
path: StrictStr = "data/online.db"
""" (optional) Path to sqlite db """
text_search_enabled: bool = False
class SqliteOnlineStore(OnlineStore):
"""
SQLite implementation of the online store interface. Not recommended for production usage.
Attributes:
_conn: SQLite connection.
"""
_conn: Optional[sqlite3.Connection] = None
@staticmethod
def _get_db_path(config: RepoConfig) -> str:
assert (
config.online_store.type == "sqlite"
or config.online_store.type.endswith("SqliteOnlineStore")
)
if config.repo_path and not Path(config.online_store.path).is_absolute():
db_path = str(config.repo_path / config.online_store.path)
else:
db_path = config.online_store.path
return db_path
def _get_conn(self, config: RepoConfig):
enable_sqlite_vec = (
sys.version_info[0:2] == (3, 10) and config.online_store.vector_enabled
)
if not self._conn:
db_path = self._get_db_path(config)
self._conn = _initialize_conn(db_path, enable_sqlite_vec)
return self._conn
def online_write_batch(
self,
config: RepoConfig,
table: FeatureView,
data: List[
Tuple[
EntityKeyProto,
Dict[str, ValueProto],
datetime,
Optional[datetime],
]
],
progress: Optional[Callable[[int], Any]],
) -> None:
conn = self._get_conn(config)
project = config.project
feature_type_dict = {f.name: f.dtype for f in table.features}
with conn:
for entity_key, values, timestamp, created_ts in data:
entity_key_bin = serialize_entity_key(
entity_key,
entity_key_serialization_version=config.entity_key_serialization_version,
)
timestamp = to_naive_utc(timestamp)
if created_ts is not None:
created_ts = to_naive_utc(created_ts)
table_name = _table_id(
project,
table,
config.registry.enable_online_feature_view_versioning,
)
for feature_name, val in values.items():
if config.online_store.vector_enabled:
if (
feature_type_dict.get(feature_name, None)
in FEAST_VECTOR_TYPES
):
vector_field_length = getattr(
_get_feature_view_vector_field_metadata(table),
"vector_length",
512,
)
val_bin = serialize_f32(
val.float_list_val.val, vector_field_length
) # type: ignore
else:
val_bin = feast_value_type_to_python_type(val)
conn.execute(
f"""
INSERT INTO {table_name} (entity_key, feature_name, value, vector_value, event_ts, created_ts)
VALUES (?, ?, ?, ?, ?, ?)
ON CONFLICT(entity_key, feature_name) DO UPDATE SET
value = excluded.value,
vector_value = excluded.vector_value,
event_ts = excluded.event_ts,
created_ts = excluded.created_ts;
""",
(
entity_key_bin, # entity_key
feature_name, # feature_name
val.SerializeToString(), # value
val_bin, # vector_value
timestamp, # event_ts
created_ts, # created_ts
),
)
else:
conn.execute(
f"""
INSERT INTO {table_name} (entity_key, feature_name, value, event_ts, created_ts)
VALUES (?, ?, ?, ?, ?)
ON CONFLICT(entity_key, feature_name) DO UPDATE SET
value = excluded.value,
event_ts = excluded.event_ts,
created_ts = excluded.created_ts;
""",
(
entity_key_bin, # entity_key
feature_name, # feature_name
val.SerializeToString(), # value
timestamp, # event_ts
created_ts, # created_ts
),
)
if progress:
progress(1)
def online_read(
self,
config: RepoConfig,
table: FeatureView,
entity_keys: List[EntityKeyProto],
requested_features: Optional[List[str]] = None,
) -> List[Tuple[Optional[datetime], Optional[Dict[str, ValueProto]]]]:
conn = self._get_conn(config)
cur = conn.cursor()
result: List[Tuple[Optional[datetime], Optional[Dict[str, ValueProto]]]] = []
serialized_entity_keys = [
serialize_entity_key(
entity_key,
entity_key_serialization_version=config.entity_key_serialization_version,
)
for entity_key in entity_keys
]
# Fetch all entities in one go
cur.execute(
f"SELECT entity_key, feature_name, value, event_ts "
f"FROM {_table_id(config.project, table, config.registry.enable_online_feature_view_versioning)} "
f"WHERE entity_key IN ({','.join('?' * len(entity_keys))}) "
f"ORDER BY entity_key",
serialized_entity_keys,
)
rows = cur.fetchall()
rows = {
k: list(group) for k, group in itertools.groupby(rows, key=lambda r: r[0])
}
for entity_key_bin in serialized_entity_keys:
res = {}
res_ts = None
for _, feature_name, val_bin, ts in rows.get(entity_key_bin, []):
val = ValueProto()
val.ParseFromString(val_bin)
res[feature_name] = val
ts = cast(datetime, ts)
if ts.tzinfo is not None:
res_ts = ts.astimezone(timezone.utc)
else:
res_ts = ts.replace(tzinfo=timezone.utc)
if not res:
result.append((None, None))
else:
result.append((res_ts, res))
return result
def update(
self,
config: RepoConfig,
tables_to_delete: Sequence[FeatureView],
tables_to_keep: Sequence[FeatureView],
entities_to_delete: Sequence[Entity],
entities_to_keep: Sequence[Entity],
partial: bool,
):
conn = self._get_conn(config)
project = config.project
versioning = config.registry.enable_online_feature_view_versioning
for table in tables_to_keep:
conn.execute(
f"CREATE TABLE IF NOT EXISTS {_table_id(project, table, versioning)} (entity_key BLOB, feature_name TEXT, value BLOB, vector_value BLOB, event_ts timestamp, created_ts timestamp, PRIMARY KEY(entity_key, feature_name))"
)
conn.execute(
f"CREATE INDEX IF NOT EXISTS {_table_id(project, table, versioning)}_ek ON {_table_id(project, table, versioning)} (entity_key);"
)
for table in tables_to_delete:
conn.execute(
f"DROP TABLE IF EXISTS {_table_id(project, table, versioning)}"
)
def plan(
self, config: RepoConfig, desired_registry_proto: RegistryProto
) -> List[InfraObject]:
project = config.project
infra_objects: List[InfraObject] = [
SqliteTable(
path=self._get_db_path(config),
name=_table_id(
project,
FeatureView.from_proto(view),
config.registry.enable_online_feature_view_versioning,
),
)
for view in [
*desired_registry_proto.feature_views,
*desired_registry_proto.stream_feature_views,
]
]
return infra_objects
def teardown(
self,
config: RepoConfig,
tables: Sequence[FeatureView],
entities: Sequence[Entity],
):
try:
os.unlink(self._get_db_path(config))
except FileNotFoundError:
pass
def retrieve_online_documents(
self,
config: RepoConfig,
table: FeatureView,
requested_features: List[str],
embedding: List[float],
top_k: int,
distance_metric: Optional[str] = None,
) -> List[
Tuple[
Optional[datetime],
Optional[EntityKeyProto],
Optional[ValueProto],
Optional[ValueProto],
Optional[ValueProto],
]
]:
"""
Args:
config: Feast configuration object
table: FeatureView object as the table to search
requested_features: The list of requested features to retrieve
embedding: The query embedding to search for
top_k: The number of items to return
Returns:
List of tuples containing the event timestamp, the document feature, the vector value, and the distance
"""
project = config.project
if not config.online_store.vector_enabled:
raise ValueError("sqlite-vss is not enabled in the online store config")
conn = self._get_conn(config)
cur = conn.cursor()
vector_field_length = getattr(
_get_feature_view_vector_field_metadata(table), "vector_length", 512
)
# Convert the embedding to a binary format instead of using SerializeToString()
query_embedding_bin = serialize_f32(embedding, vector_field_length)
table_name = _table_id(
project, table, config.registry.enable_online_feature_view_versioning
)
vector_field = _get_vector_field(table)
cur.execute(
f"""
CREATE VIRTUAL TABLE vec_table using vec0(
vector_value float[{vector_field_length}]
);
"""
)
# Currently I can only insert the embedding value without crashing SQLite, will report a bug
cur.execute(
f"""
INSERT INTO vec_table(rowid, vector_value)
select rowid, vector_value from {table_name}
where feature_name = "{vector_field}"
"""
)
cur.execute(
f"""
CREATE VIRTUAL TABLE IF NOT EXISTS vec_table using vec0(
vector_value float[{vector_field_length}]
);
"""
)
# Have to join this with the {table_name} to get the feature name and entity_key
# Also the `top_k` doesn't appear to be working for some reason
cur.execute(
f"""
select
fv.entity_key,
f.vector_value,
fv.value,
f.distance,
fv.event_ts
from (
select
rowid,
vector_value,
distance
from vec_table
where vector_value match ?
order by distance
limit ?
) f
left join {table_name} fv
on f.rowid = fv.rowid
""",
(query_embedding_bin, top_k),
)
rows = cur.fetchall()
result: List[
Tuple[
Optional[datetime],
Optional[EntityKeyProto],
Optional[ValueProto],
Optional[ValueProto],
Optional[ValueProto],
]
] = []
for entity_key, _, string_value, distance, event_ts in rows:
result.append(
_build_retrieve_online_document_record(
entity_key,
string_value if string_value else b"",
# This may be a bug
embedding,
distance,
event_ts,
config.entity_key_serialization_version,
)
)
return result
def retrieve_online_documents_v2(
self,
config: RepoConfig,
table: FeatureView,
requested_features: List[str],
query: Optional[List[float]],
top_k: int,
distance_metric: Optional[str] = None,
query_string: Optional[str] = None,
include_feature_view_version_metadata: bool = False,
) -> List[
Tuple[
Optional[datetime],
Optional[EntityKeyProto],
Optional[Dict[str, ValueProto]],
]
]:
"""
Retrieve documents using vector similarity search.
Args:
config: Feast configuration object
table: FeatureView object as the table to search
requested_features: List of requested features to retrieve
query: Query embedding to search for (optional)
top_k: Number of items to return
distance_metric: Distance metric to use (optional)
query_string: The query string to search for using keyword search (bm25) (optional)
Returns:
List of tuples containing the event timestamp, entity key, and feature values
"""
online_store = config.online_store
if not isinstance(online_store, SqliteOnlineStoreConfig):
raise ValueError("online_store must be SqliteOnlineStoreConfig")
if not online_store.vector_enabled and not online_store.text_search_enabled:
raise ValueError(
"You must enable either vector search or text search in the online store config"
)
conn = self._get_conn(config)
cur = conn.cursor()
vector_field_length = getattr(
_get_feature_view_vector_field_metadata(table), "vector_length", 512
)
table_name = _table_id(
config.project, table, config.registry.enable_online_feature_view_versioning
)
vector_field = _get_vector_field(table)
if online_store.vector_enabled:
query_embedding_bin = serialize_f32(query, vector_field_length) # type: ignore
cur.execute(
f"""
CREATE VIRTUAL TABLE IF NOT EXISTS vec_table using vec0(
vector_value float[{vector_field_length}]
);
"""
)
cur.execute(
f"""
INSERT INTO vec_table (rowid, vector_value)
select rowid, vector_value from {table_name}
where feature_name = "{vector_field}"
"""
)
elif online_store.text_search_enabled:
string_field_list = [
f.name for f in table.features if f.dtype == PrimitiveFeastType.STRING
]
string_fields = ", ".join(string_field_list)
# TODO: swap this for a value configurable in each Field()
BM25_DEFAULT_WEIGHTS = ", ".join(
[
str(1.0)
for f in table.features
if f.dtype == PrimitiveFeastType.STRING
]
)
cur.execute(
f"""
CREATE VIRTUAL TABLE IF NOT EXISTS search_table using fts5(
entity_key, fv_rowid, {string_fields}, tokenize="porter unicode61"
);
"""
)
insert_query = _generate_bm25_search_insert_query(
table_name, string_field_list
)
cur.execute(insert_query)
else:
raise ValueError(
"Neither vector search nor text search are enabled in the online store config"
)
if online_store.vector_enabled:
cur.execute(
f"""
select
fv2.entity_key,
fv2.feature_name,
fv2.value,
fv.vector_value,
f.distance,
fv.event_ts,
fv.created_ts
from (
select
rowid,
vector_value,
distance
from vec_table
where vector_value match ?
order by distance
limit ?
) f
left join {table_name} fv
on f.rowid = fv.rowid
left join {table_name} fv2
on fv.entity_key = fv2.entity_key
where fv2.feature_name != "{vector_field}"
""",
(
query_embedding_bin,
top_k,
),
)
elif online_store.text_search_enabled:
cur.execute(
f"""
select
fv.entity_key,
fv.feature_name,
fv.value,
fv.vector_value,
f.distance,
fv.event_ts,
fv.created_ts
from {table_name} fv
inner join (
select
fv_rowid,
entity_key,
{string_fields},
bm25(search_table, {BM25_DEFAULT_WEIGHTS}) as distance
from search_table
where search_table match ? order by distance limit ?
) f
on f.entity_key = fv.entity_key
""",
(query_string, top_k),
)
else:
raise ValueError(
"Neither vector search nor text search are enabled in the online store config"
)
rows = cur.fetchall()
results: List[
Tuple[
Optional[datetime],
Optional[EntityKeyProto],
Optional[Dict[str, ValueProto]],
]
] = []
entity_dict: Dict[
str, Dict[str, Union[str, ValueProto, EntityKeyProto, datetime]]
] = {}
for (
entity_key,
feature_name,
value_bin,
vector_value,
distance,
event_ts,
created_ts,
) in rows:
entity_key_proto = deserialize_entity_key(
entity_key,
entity_key_serialization_version=config.entity_key_serialization_version,
)
if entity_key not in entity_dict:
entity_dict[entity_key] = {}
feature_val = ValueProto()
feature_val.ParseFromString(value_bin)
entity_dict[entity_key]["entity_key_proto"] = entity_key_proto
entity_dict[entity_key][feature_name] = feature_val
if online_store.vector_enabled:
entity_dict[entity_key][vector_field] = _serialize_vector_to_float_list(
vector_value
)
entity_dict[entity_key]["distance"] = ValueProto(float_val=distance)
entity_dict[entity_key]["event_ts"] = event_ts
entity_dict[entity_key]["created_ts"] = created_ts
for entity_key_value in entity_dict:
res_event_ts: Optional[datetime] = None
res_entity_key_proto: Optional[EntityKeyProto] = None
if isinstance(entity_dict[entity_key_value]["event_ts"], datetime):
res_event_ts = entity_dict[entity_key_value]["event_ts"] # type: ignore[assignment]
if isinstance(
entity_dict[entity_key_value]["entity_key_proto"], EntityKeyProto
):
res_entity_key_proto = entity_dict[entity_key_value]["entity_key_proto"] # type: ignore[assignment]
res_dict: Dict[str, ValueProto] = {
k: v
for k, v in entity_dict[entity_key_value].items()
if isinstance(v, ValueProto) and isinstance(k, str)
}
results.append(
(
res_event_ts,
res_entity_key_proto,
res_dict,
)
)
return results
def _initialize_conn(
db_path: str, enable_sqlite_vec: bool = False
) -> sqlite3.Connection:
Path(db_path).parent.mkdir(exist_ok=True)
db = sqlite3.connect(
db_path,
detect_types=sqlite3.PARSE_DECLTYPES | sqlite3.PARSE_COLNAMES,
check_same_thread=False,
)
if enable_sqlite_vec:
try:
import sqlite_vec # noqa: F401
except ModuleNotFoundError:
logging.warning("Cannot use sqlite_vec for vector search")
db.enable_load_extension(True)
sqlite_vec.load(db)
return db
def _table_id(project: str, table: FeatureView, enable_versioning: bool = False) -> str:
name = table.name
if enable_versioning:
# Prefer version_tag from the projection (set by version-qualified refs like @v2)
# over current_version_number (the FV's active version in metadata).
version = getattr(table.projection, "version_tag", None)
if version is None:
version = getattr(table, "current_version_number", None)
if version is not None and version > 0:
name = f"{table.name}_v{version}"
return f"{project}_{name}"
class SqliteTable(InfraObject):
"""
A Sqlite table managed by Feast.
Attributes:
path: The absolute path of the Sqlite file.
name: The name of the table.
conn: SQLite connection.
"""
path: str
conn: sqlite3.Connection
def __init__(self, path: str, name: str):
super().__init__(name)
self.path = path
self.conn = _initialize_conn(path)
def to_infra_object_proto(self) -> InfraObjectProto:
sqlite_table_proto = self.to_proto()
return InfraObjectProto(
infra_object_class_type=SQLITE_INFRA_OBJECT_CLASS_TYPE,
sqlite_table=sqlite_table_proto,
)
def to_proto(self) -> Any:
sqlite_table_proto = SqliteTableProto()
sqlite_table_proto.path = self.path
sqlite_table_proto.name = self.name
return sqlite_table_proto
@staticmethod
def from_infra_object_proto(infra_object_proto: InfraObjectProto) -> Any:
return SqliteTable(
path=infra_object_proto.sqlite_table.path,
name=infra_object_proto.sqlite_table.name,
)
@staticmethod
def from_proto(sqlite_table_proto: SqliteTableProto) -> Any:
return SqliteTable(
path=sqlite_table_proto.path,
name=sqlite_table_proto.name,
)
def update(self):
if sys.version_info[0:2] == (3, 10):
try:
import sqlite_vec # noqa: F401
self.conn.enable_load_extension(True)
sqlite_vec.load(self.conn)
except ModuleNotFoundError:
logging.warning("Cannot use sqlite_vec for vector search")
self.conn.execute(
f"""
CREATE TABLE IF NOT EXISTS {self.name} (
entity_key BLOB,
feature_name TEXT,
value BLOB,
vector_value BLOB,
event_ts timestamp,
created_ts timestamp,
PRIMARY KEY(entity_key, feature_name)
)
"""
)
self.conn.execute(
f"CREATE INDEX IF NOT EXISTS {self.name}_ek ON {self.name} (entity_key);"
)
def teardown(self):
self.conn.execute(f"DROP TABLE IF EXISTS {self.name}")
def _get_vector_field(table: FeatureView) -> str:
"""
Get the vector field from the feature view. There can be only one.
"""
vector_fields: List[Field] = [
f for f in table.features if getattr(f, "vector_index", None)
]
assert len(vector_fields) > 0, (
f"No vector field found, please update feature view = {table.name} to declare a vector field"
)
assert len(vector_fields) < 2, (
"Only one vector field is supported, please update feature view = {table.name} to declare one vector field"
)
vector_field: str = vector_fields[0].name
return vector_field
def _generate_bm25_search_insert_query(
table_name: str, string_field_list: List[str]
) -> str:
"""
Generates an SQL insertion query for the given table and string fields.
Args:
table_name (str): The name of the table to select data from.
string_field_list (List[str]): The list of string fields to be used in the insertion.
Returns:
str: The generated SQL insertion query.
"""
_string_fields = ", ".join(string_field_list)
query = f"INSERT INTO search_table (entity_key, fv_rowid, {_string_fields})\nSELECT\n\tDISTINCT fv0.entity_key,\n\tfv0.rowid as fv_rowid"
from_query = f"\nFROM (select rowid, * from {table_name} where feature_name = '{string_field_list[0]}') fv0"
for i, string_field in enumerate(string_field_list):
query += f"\n\t,fv{i}.value as {string_field}"
if i > 0:
from_query += (
f"\nLEFT JOIN (select rowid, * from {table_name} where feature_name = '{string_field}') fv{i}"
+ f"\n\tON fv0.entity_key = fv{i}.entity_key"
)
return query + from_query