snowflake.py

import contextlib
import os
import uuid
from datetime import datetime
from pathlib import Path
from typing import (
    Any,
    Callable,
    ContextManager,
    Dict,
    Iterator,
    List,
    Optional,
    Tuple,
    Union,
    cast,
)

import numpy as np
import pandas as pd
import pyarrow
import pyarrow as pa
from pydantic import Field
from pydantic.typing import Literal
from pytz import utc

from feast import OnDemandFeatureView
from feast.data_source import DataSource
from feast.errors import InvalidEntityType
from feast.feature_logging import LoggingConfig, LoggingSource
from feast.feature_view import DUMMY_ENTITY_ID, DUMMY_ENTITY_VAL, FeatureView
from feast.infra.offline_stores import offline_utils
from feast.infra.offline_stores.offline_store import (
    OfflineStore,
    RetrievalJob,
    RetrievalMetadata,
)
from feast.infra.offline_stores.snowflake_source import (
    SavedDatasetSnowflakeStorage,
    SnowflakeLoggingDestination,
    SnowflakeSource,
)
from feast.infra.utils.snowflake_utils import (
    execute_snowflake_statement,
    get_snowflake_conn,
    write_pandas,
    write_parquet,
)
from feast.registry import BaseRegistry
from feast.repo_config import FeastConfigBaseModel, RepoConfig
from feast.saved_dataset import SavedDatasetStorage
from feast.usage import log_exceptions_and_usage

try:
    from snowflake.connector import SnowflakeConnection
except ImportError as e:
    from feast.errors import FeastExtrasDependencyImportError

    raise FeastExtrasDependencyImportError("snowflake", str(e))


class SnowflakeOfflineStoreConfig(FeastConfigBaseModel):
    """Offline store config for Snowflake"""

    type: Literal["snowflake.offline"] = "snowflake.offline"
    """ Offline store type selector"""

    config_path: Optional[str] = (
        Path(os.environ["HOME"]) / ".snowsql/config"
    ).__str__()
    """ Snowflake config path -- absolute path required (Cant use ~)"""

    account: Optional[str] = None
    """ Snowflake deployment identifier -- drop .snowflakecomputing.com"""

    user: Optional[str] = None
    """ Snowflake user name """

    password: Optional[str] = None
    """ Snowflake password """

    role: Optional[str] = None
    """ Snowflake role name"""

    warehouse: Optional[str] = None
    """ Snowflake warehouse name """

    database: Optional[str] = None
    """ Snowflake database name """

    schema_: Optional[str] = Field(None, alias="schema")
    """ Snowflake schema name """

    storage_integration_name: Optional[str] = None
    """ Storage integration name in snowflake """

    blob_export_location: Optional[str] = None
    """ Location (in S3, Google storage or Azure storage) where data is offloaded """

    class Config:
        allow_population_by_field_name = True


class SnowflakeOfflineStore(OfflineStore):
    @staticmethod
    @log_exceptions_and_usage(offline_store="snowflake")
    def pull_latest_from_table_or_query(
        config: RepoConfig,
        data_source: DataSource,
        join_key_columns: List[str],
        feature_name_columns: List[str],
        timestamp_field: str,
        created_timestamp_column: Optional[str],
        start_date: datetime,
        end_date: datetime,
    ) -> RetrievalJob:
        assert isinstance(data_source, SnowflakeSource)
        assert isinstance(config.offline_store, SnowflakeOfflineStoreConfig)

        from_expression = (
            data_source.get_table_query_string()
        )  # returns schema.table as a string

        if join_key_columns:
            partition_by_join_key_string = '"' + '", "'.join(join_key_columns) + '"'
            partition_by_join_key_string = (
                "PARTITION BY " + partition_by_join_key_string
            )
        else:
            partition_by_join_key_string = ""

        timestamp_columns = [timestamp_field]
        if created_timestamp_column:
            timestamp_columns.append(created_timestamp_column)

        timestamp_desc_string = '"' + '" DESC, "'.join(timestamp_columns) + '" DESC'
        field_string = (
            '"'
            + '", "'.join(join_key_columns + feature_name_columns + timestamp_columns)
            + '"'
        )

        if data_source.snowflake_options.warehouse:
            config.offline_store.warehouse = data_source.snowflake_options.warehouse

        snowflake_conn = get_snowflake_conn(config.offline_store)

        query = f"""
            SELECT
                {field_string}
                {f''', TRIM({repr(DUMMY_ENTITY_VAL)}::VARIANT,'"') AS "{DUMMY_ENTITY_ID}"''' if not join_key_columns else ""}
            FROM (
                SELECT {field_string},
                ROW_NUMBER() OVER({partition_by_join_key_string} ORDER BY {timestamp_desc_string}) AS "_feast_row"
                FROM {from_expression}
                WHERE "{timestamp_field}" BETWEEN TO_TIMESTAMP_NTZ({start_date.timestamp()}) AND TO_TIMESTAMP_NTZ({end_date.timestamp()})
            )
            WHERE "_feast_row" = 1
            """

        return SnowflakeRetrievalJob(
            query=query,
            snowflake_conn=snowflake_conn,
            config=config,
            full_feature_names=False,
            on_demand_feature_views=None,
        )

    @staticmethod
    @log_exceptions_and_usage(offline_store="snowflake")
    def pull_all_from_table_or_query(
        config: RepoConfig,
        data_source: DataSource,
        join_key_columns: List[str],
        feature_name_columns: List[str],
        timestamp_field: str,
        start_date: datetime,
        end_date: datetime,
    ) -> RetrievalJob:
        assert isinstance(data_source, SnowflakeSource)
        from_expression = data_source.get_table_query_string()

        field_string = (
            '"'
            + '", "'.join(join_key_columns + feature_name_columns + [timestamp_field])
            + '"'
        )

        if data_source.snowflake_options.warehouse:
            config.offline_store.warehouse = data_source.snowflake_options.warehouse

        snowflake_conn = get_snowflake_conn(config.offline_store)

        start_date = start_date.astimezone(tz=utc)
        end_date = end_date.astimezone(tz=utc)

        query = f"""
            SELECT {field_string}
            FROM {from_expression}
            WHERE "{timestamp_field}" BETWEEN TIMESTAMP '{start_date}' AND TIMESTAMP '{end_date}'
        """

        return SnowflakeRetrievalJob(
            query=query,
            snowflake_conn=snowflake_conn,
            config=config,
            full_feature_names=False,
        )

    @staticmethod
    @log_exceptions_and_usage(offline_store="snowflake")
    def get_historical_features(
        config: RepoConfig,
        feature_views: List[FeatureView],
        feature_refs: List[str],
        entity_df: Union[pd.DataFrame, str],
        registry: BaseRegistry,
        project: str,
        full_feature_names: bool = False,
    ) -> RetrievalJob:
        assert isinstance(config.offline_store, SnowflakeOfflineStoreConfig)

        snowflake_conn = get_snowflake_conn(config.offline_store)

        entity_schema = _get_entity_schema(entity_df, snowflake_conn, config)

        entity_df_event_timestamp_col = (
            offline_utils.infer_event_timestamp_from_entity_df(entity_schema)
        )

        entity_df_event_timestamp_range = _get_entity_df_event_timestamp_range(
            entity_df,
            entity_df_event_timestamp_col,
            snowflake_conn,
        )

        @contextlib.contextmanager
        def query_generator() -> Iterator[str]:

            table_name = offline_utils.get_temp_entity_table_name()

            _upload_entity_df(entity_df, snowflake_conn, config, table_name)

            expected_join_keys = offline_utils.get_expected_join_keys(
                project, feature_views, registry
            )

            offline_utils.assert_expected_columns_in_entity_df(
                entity_schema, expected_join_keys, entity_df_event_timestamp_col
            )

            # Build a query context containing all information required to template the Snowflake SQL query
            query_context = offline_utils.get_feature_view_query_context(
                feature_refs,
                feature_views,
                registry,
                project,
                entity_df_event_timestamp_range,
            )

            query_context = _fix_entity_selections_identifiers(query_context)

            # Generate the Snowflake SQL query from the query context
            query = offline_utils.build_point_in_time_query(
                query_context,
                left_table_query_string=table_name,
                entity_df_event_timestamp_col=entity_df_event_timestamp_col,
                entity_df_columns=entity_schema.keys(),
                query_template=MULTIPLE_FEATURE_VIEW_POINT_IN_TIME_JOIN,
                full_feature_names=full_feature_names,
            )

            yield query

        return SnowflakeRetrievalJob(
            query=query_generator,
            snowflake_conn=snowflake_conn,
            config=config,
            full_feature_names=full_feature_names,
            on_demand_feature_views=OnDemandFeatureView.get_requested_odfvs(
                feature_refs, project, registry
            ),
            metadata=RetrievalMetadata(
                features=feature_refs,
                keys=list(entity_schema.keys() - {entity_df_event_timestamp_col}),
                min_event_timestamp=entity_df_event_timestamp_range[0],
                max_event_timestamp=entity_df_event_timestamp_range[1],
            ),
        )

    @staticmethod
    def write_logged_features(
        config: RepoConfig,
        data: Union[pyarrow.Table, Path],
        source: LoggingSource,
        logging_config: LoggingConfig,
        registry: BaseRegistry,
    ):
        assert isinstance(logging_config.destination, SnowflakeLoggingDestination)

        snowflake_conn = get_snowflake_conn(config.offline_store)

        if isinstance(data, Path):
            write_parquet(
                snowflake_conn,
                data,
                source.get_schema(registry),
                table_name=logging_config.destination.table_name,
                auto_create_table=True,
            )
        else:
            write_pandas(
                snowflake_conn,
                data.to_pandas(),
                table_name=logging_config.destination.table_name,
                auto_create_table=True,
            )

    @staticmethod
    def offline_write_batch(
        config: RepoConfig,
        feature_view: FeatureView,
        table: pyarrow.Table,
        progress: Optional[Callable[[int], Any]],
    ):
        if not feature_view.batch_source:
            raise ValueError(
                "feature view does not have a batch source to persist offline data"
            )
        if not isinstance(config.offline_store, SnowflakeOfflineStoreConfig):
            raise ValueError(
                f"offline store config is of type {type(config.offline_store)} when snowflake type required"
            )
        if not isinstance(feature_view.batch_source, SnowflakeSource):
            raise ValueError(
                f"feature view batch source is {type(feature_view.batch_source)} not snowflake source"
            )

        pa_schema, column_names = offline_utils.get_pyarrow_schema_from_batch_source(
            config, feature_view.batch_source
        )
        if column_names != table.column_names:
            raise ValueError(
                f"The input pyarrow table has schema {table.schema} with the incorrect columns {table.column_names}. "
                f"The schema is expected to be {pa_schema} with the columns (in this exact order) to be {column_names}."
            )

        if table.schema != pa_schema:
            table = table.cast(pa_schema)

        snowflake_conn = get_snowflake_conn(config.offline_store)

        write_pandas(
            snowflake_conn,
            table.to_pandas(),
            table_name=feature_view.batch_source.table,
            auto_create_table=True,
        )


class SnowflakeRetrievalJob(RetrievalJob):
    def __init__(
        self,
        query: Union[str, Callable[[], ContextManager[str]]],
        snowflake_conn: SnowflakeConnection,
        config: RepoConfig,
        full_feature_names: bool,
        on_demand_feature_views: Optional[List[OnDemandFeatureView]] = None,
        metadata: Optional[RetrievalMetadata] = None,
    ):

        if not isinstance(query, str):
            self._query_generator = query
        else:

            @contextlib.contextmanager
            def query_generator() -> Iterator[str]:
                assert isinstance(query, str)
                yield query

            self._query_generator = query_generator

        self.snowflake_conn = snowflake_conn
        self.config = config
        self._full_feature_names = full_feature_names
        self._on_demand_feature_views = (
            on_demand_feature_views if on_demand_feature_views else []
        )
        self._metadata = metadata
        self.export_path: Optional[str]
        if self.config.offline_store.blob_export_location:
            self.export_path = f"{self.config.offline_store.blob_export_location}/{self.config.project}/{uuid.uuid4()}"
        else:
            self.export_path = None

    @property
    def full_feature_names(self) -> bool:
        return self._full_feature_names

    @property
    def on_demand_feature_views(self) -> Optional[List[OnDemandFeatureView]]:
        return self._on_demand_feature_views

    def _to_df_internal(self) -> pd.DataFrame:
        with self._query_generator() as query:

            df = execute_snowflake_statement(
                self.snowflake_conn, query
            ).fetch_pandas_all()

        return df

    def _to_arrow_internal(self) -> pa.Table:
        with self._query_generator() as query:

            pa_table = execute_snowflake_statement(
                self.snowflake_conn, query
            ).fetch_arrow_all()

            if pa_table:

                return pa_table
            else:
                empty_result = execute_snowflake_statement(self.snowflake_conn, query)

                return pa.Table.from_pandas(
                    pd.DataFrame(columns=[md.name for md in empty_result.description])
                )

    def to_snowflake(self, table_name: str, temporary=False) -> None:
        """Save dataset as a new Snowflake table"""
        if self.on_demand_feature_views:
            transformed_df = self.to_df()

            write_pandas(
                self.snowflake_conn, transformed_df, table_name, auto_create_table=True
            )

            return None

        with self._query_generator() as query:
            query = f'CREATE {"TEMPORARY" if temporary else ""} TABLE IF NOT EXISTS "{table_name}" AS ({query});\n'

            execute_snowflake_statement(self.snowflake_conn, query)

    def to_sql(self) -> str:
        """
        Returns the SQL query that will be executed in Snowflake to build the historical feature table.
        """
        with self._query_generator() as query:
            return query

    def to_arrow_chunks(self, arrow_options: Optional[Dict] = None) -> Optional[List]:
        with self._query_generator() as query:

            arrow_batches = execute_snowflake_statement(
                self.snowflake_conn, query
            ).get_result_batches()

        return arrow_batches

    def persist(self, storage: SavedDatasetStorage):
        assert isinstance(storage, SavedDatasetSnowflakeStorage)
        self.to_snowflake(table_name=storage.snowflake_options.table)

    @property
    def metadata(self) -> Optional[RetrievalMetadata]:
        return self._metadata

    def supports_remote_storage_export(self) -> bool:
        return (
            self.config.offline_store.storage_integration_name
            and self.config.offline_store.blob_export_location
        )

    def to_remote_storage(self) -> List[str]:
        if not self.export_path:
            raise ValueError(
                "to_remote_storage() requires `blob_export_location` to be specified in config"
            )
        if not self.config.offline_store.storage_integration_name:
            raise ValueError(
                "to_remote_storage() requires `storage_integration_name` to be specified in config"
            )

        table = f"temporary_{uuid.uuid4().hex}"
        self.to_snowflake(table)

        copy_into_query = f"""copy into '{self.config.offline_store.blob_export_location}/{table}' from "{self.config.offline_store.database}"."{self.config.offline_store.schema_}"."{table}"\n
          storage_integration = {self.config.offline_store.storage_integration_name}\n
          file_format = (TYPE = PARQUET)\n
          DETAILED_OUTPUT = TRUE\n
          HEADER = TRUE;\n
        """

        cursor = execute_snowflake_statement(self.snowflake_conn, copy_into_query)
        all_rows = (
            cursor.fetchall()
        )  # This may be need pagination at some point in the future.
        file_name_column_index = [
            idx for idx, rm in enumerate(cursor.description) if rm.name == "FILE_NAME"
        ][0]
        return [f"{self.export_path}/{row[file_name_column_index]}" for row in all_rows]


def _get_entity_schema(
    entity_df: Union[pd.DataFrame, str],
    snowflake_conn: SnowflakeConnection,
    config: RepoConfig,
) -> Dict[str, np.dtype]:

    if isinstance(entity_df, pd.DataFrame):

        return dict(zip(entity_df.columns, entity_df.dtypes))

    else:

        query = f"SELECT * FROM ({entity_df}) LIMIT 1"
        limited_entity_df = execute_snowflake_statement(
            snowflake_conn, query
        ).fetch_pandas_all()

        return dict(zip(limited_entity_df.columns, limited_entity_df.dtypes))


def _upload_entity_df(
    entity_df: Union[pd.DataFrame, str],
    snowflake_conn: SnowflakeConnection,
    config: RepoConfig,
    table_name: str,
) -> None:

    if isinstance(entity_df, pd.DataFrame):
        # Write the data from the DataFrame to the table
        write_pandas(
            snowflake_conn,
            entity_df,
            table_name,
            auto_create_table=True,
            create_temp_table=True,
        )

        return None
    elif isinstance(entity_df, str):
        # If the entity_df is a string (SQL query), create a Snowflake table out of it,
        query = f'CREATE TEMPORARY TABLE "{table_name}" AS ({entity_df})'
        execute_snowflake_statement(snowflake_conn, query)

        return None
    else:
        raise InvalidEntityType(type(entity_df))


def _fix_entity_selections_identifiers(query_context) -> list:

    for i, qc in enumerate(query_context):
        for j, es in enumerate(qc.entity_selections):
            query_context[i].entity_selections[j] = f'"{es}"'.replace(" AS ", '" AS "')

    return query_context


def _get_entity_df_event_timestamp_range(
    entity_df: Union[pd.DataFrame, str],
    entity_df_event_timestamp_col: str,
    snowflake_conn: SnowflakeConnection,
) -> Tuple[datetime, datetime]:
    if isinstance(entity_df, pd.DataFrame):
        entity_df_event_timestamp = entity_df.loc[
            :, entity_df_event_timestamp_col
        ].infer_objects()
        if pd.api.types.is_string_dtype(entity_df_event_timestamp):
            entity_df_event_timestamp = pd.to_datetime(
                entity_df_event_timestamp, utc=True
            )
        entity_df_event_timestamp_range = (
            entity_df_event_timestamp.min().to_pydatetime(),
            entity_df_event_timestamp.max().to_pydatetime(),
        )
    elif isinstance(entity_df, str):
        # If the entity_df is a string (SQL query), determine range
        # from table
        query = f'SELECT MIN("{entity_df_event_timestamp_col}") AS "min_value", MAX("{entity_df_event_timestamp_col}") AS "max_value" FROM ({entity_df})'
        results = execute_snowflake_statement(snowflake_conn, query).fetchall()

        entity_df_event_timestamp_range = cast(Tuple[datetime, datetime], results[0])
    else:
        raise InvalidEntityType(type(entity_df))

    return entity_df_event_timestamp_range


MULTIPLE_FEATURE_VIEW_POINT_IN_TIME_JOIN = """
/*
 Compute a deterministic hash for the `left_table_query_string` that will be used throughout
 all the logic as the field to GROUP BY the data
*/
WITH "entity_dataframe" AS (
    SELECT *,
        "{{entity_df_event_timestamp_col}}" AS "entity_timestamp"
        {% for featureview in featureviews %}
            {% if featureview.entities %}
            ,(
                {% for entity in featureview.entities %}
                    CAST("{{entity}}" AS VARCHAR) ||
                {% endfor %}
                CAST("{{entity_df_event_timestamp_col}}" AS VARCHAR)
            ) AS "{{featureview.name}}__entity_row_unique_id"
            {% else %}
            ,CAST("{{entity_df_event_timestamp_col}}" AS VARCHAR) AS "{{featureview.name}}__entity_row_unique_id"
            {% endif %}
        {% endfor %}
    FROM "{{ left_table_query_string }}"
),

{% for featureview in featureviews %}

"{{ featureview.name }}__entity_dataframe" AS (
    SELECT
        {{ featureview.entities | map('tojson') | join(', ')}}{% if featureview.entities %},{% else %}{% endif %}
        "entity_timestamp",
        "{{featureview.name}}__entity_row_unique_id"
    FROM "entity_dataframe"
    GROUP BY
        {{ featureview.entities | map('tojson') | join(', ')}}{% if featureview.entities %},{% else %}{% endif %}
        "entity_timestamp",
        "{{featureview.name}}__entity_row_unique_id"
),

/*
 This query template performs the point-in-time correctness join for a single feature set table
 to the provided entity table.

 1. We first join the current feature_view to the entity dataframe that has been passed.
 This JOIN has the following logic:
    - For each row of the entity dataframe, only keep the rows where the `timestamp_field`
    is less than the one provided in the entity dataframe
    - If there a TTL for the current feature_view, also keep the rows where the `timestamp_field`
    is higher the the one provided minus the TTL
    - For each row, Join on the entity key and retrieve the `entity_row_unique_id` that has been
    computed previously

 The output of this CTE will contain all the necessary information and already filtered out most
 of the data that is not relevant.
*/

"{{ featureview.name }}__subquery" AS (
    SELECT
        "{{ featureview.timestamp_field }}" as "event_timestamp",
        {{'"' ~ featureview.created_timestamp_column ~ '" as "created_timestamp",' if featureview.created_timestamp_column else '' }}
        {{featureview.entity_selections | join(', ')}}{% if featureview.entity_selections %},{% else %}{% endif %}
        {% for feature in featureview.features %}
            "{{ feature }}" as {% if full_feature_names %}"{{ featureview.name }}__{{featureview.field_mapping.get(feature, feature)}}"{% else %}"{{ featureview.field_mapping.get(feature, feature) }}"{% endif %}{% if loop.last %}{% else %}, {% endif %}
        {% endfor %}
    FROM {{ featureview.table_subquery }}
    WHERE "{{ featureview.timestamp_field }}" <= '{{ featureview.max_event_timestamp }}'
    {% if featureview.ttl == 0 %}{% else %}
    AND "{{ featureview.timestamp_field }}" >= '{{ featureview.min_event_timestamp }}'
    {% endif %}
),

"{{ featureview.name }}__base" AS (
    SELECT
        "subquery".*,
        "entity_dataframe"."entity_timestamp",
        "entity_dataframe"."{{featureview.name}}__entity_row_unique_id"
    FROM "{{ featureview.name }}__subquery" AS "subquery"
    INNER JOIN "{{ featureview.name }}__entity_dataframe" AS "entity_dataframe"
    ON TRUE
        AND "subquery"."event_timestamp" <= "entity_dataframe"."entity_timestamp"

        {% if featureview.ttl == 0 %}{% else %}
        AND "subquery"."event_timestamp" >= TIMESTAMPADD(second,-{{ featureview.ttl }},"entity_dataframe"."entity_timestamp")
        {% endif %}

        {% for entity in featureview.entities %}
        AND "subquery"."{{ entity }}" = "entity_dataframe"."{{ entity }}"
        {% endfor %}
),

/*
 2. If the `created_timestamp_column` has been set, we need to
 deduplicate the data first. This is done by calculating the
 `MAX(created_at_timestamp)` for each event_timestamp.
 We then join the data on the next CTE
*/
{% if featureview.created_timestamp_column %}
"{{ featureview.name }}__dedup" AS (
    SELECT
        "{{featureview.name}}__entity_row_unique_id",
        "event_timestamp",
        MAX("created_timestamp") AS "created_timestamp"
    FROM "{{ featureview.name }}__base"
    GROUP BY "{{featureview.name}}__entity_row_unique_id", "event_timestamp"
),
{% endif %}

/*
 3. The data has been filtered during the first CTE "*__base"
 Thus we only need to compute the latest timestamp of each feature.
*/
"{{ featureview.name }}__latest" AS (
    SELECT
        "event_timestamp",
        {% if featureview.created_timestamp_column %}"created_timestamp",{% endif %}
        "{{featureview.name}}__entity_row_unique_id"
    FROM
    (
        SELECT *,
            ROW_NUMBER() OVER(
                PARTITION BY "{{featureview.name}}__entity_row_unique_id"
                ORDER BY "event_timestamp" DESC{% if featureview.created_timestamp_column %},"created_timestamp" DESC{% endif %}
            ) AS "row_number"
        FROM "{{ featureview.name }}__base"
        {% if featureview.created_timestamp_column %}
            INNER JOIN "{{ featureview.name }}__dedup"
            USING ("{{featureview.name}}__entity_row_unique_id", "event_timestamp", "created_timestamp")
        {% endif %}
    )
    WHERE "row_number" = 1
),

/*
 4. Once we know the latest value of each feature for a given timestamp,
 we can join again the data back to the original "base" dataset
*/
"{{ featureview.name }}__cleaned" AS (
    SELECT "base".*
    FROM "{{ featureview.name }}__base" AS "base"
    INNER JOIN "{{ featureview.name }}__latest"
    USING(
        "{{featureview.name}}__entity_row_unique_id",
        "event_timestamp"
        {% if featureview.created_timestamp_column %}
            ,"created_timestamp"
        {% endif %}
    )
){% if loop.last %}{% else %}, {% endif %}


{% endfor %}
/*
 Joins the outputs of multiple time travel joins to a single table.
 The entity_dataframe dataset being our source of truth here.
 */

SELECT "{{ final_output_feature_names | join('", "')}}"
FROM "entity_dataframe"
{% for featureview in featureviews %}
LEFT JOIN (
    SELECT
        "{{featureview.name}}__entity_row_unique_id"
        {% for feature in featureview.features %}
            ,{% if full_feature_names %}"{{ featureview.name }}__{{featureview.field_mapping.get(feature, feature)}}"{% else %}"{{ featureview.field_mapping.get(feature, feature) }}"{% endif %}
        {% endfor %}
    FROM "{{ featureview.name }}__cleaned"
) "{{ featureview.name }}__cleaned" USING ("{{featureview.name}}__entity_row_unique_id")
{% endfor %}
"""