postgres.py

import contextlib
from dataclasses import asdict
from datetime import datetime
from typing import (
    Any,
    Callable,
    ContextManager,
    Dict,
    Iterator,
    KeysView,
    List,
    Optional,
    Tuple,
    Union,
)

import numpy as np
import pandas as pd
import pyarrow as pa
from jinja2 import BaseLoader, Environment
from psycopg2 import sql
from pydantic.typing import Literal
from pytz import utc

from feast.data_source import DataSource
from feast.errors import InvalidEntityType
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.contrib.postgres_offline_store.postgres_source import (
    SavedDatasetPostgreSQLStorage,
)
from feast.infra.offline_stores.offline_store import (
    OfflineStore,
    RetrievalJob,
    RetrievalMetadata,
)
from feast.infra.utils.postgres.connection_utils import (
    _get_conn,
    df_to_postgres_table,
    get_query_schema,
)
from feast.infra.utils.postgres.postgres_config import PostgreSQLConfig
from feast.on_demand_feature_view import OnDemandFeatureView
from feast.registry import Registry
from feast.repo_config import RepoConfig
from feast.saved_dataset import SavedDatasetStorage
from feast.type_map import pg_type_code_to_arrow
from feast.usage import log_exceptions_and_usage

from .postgres_source import PostgreSQLSource


class PostgreSQLOfflineStoreConfig(PostgreSQLConfig):
    type: Literal["postgres"] = "postgres"


class PostgreSQLOfflineStore(OfflineStore):
    @staticmethod
    @log_exceptions_and_usage(offline_store="postgres")
    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, PostgreSQLSource)
        from_expression = data_source.get_table_query_string()

        partition_by_join_key_string = ", ".join(_append_alias(join_key_columns, "a"))
        if partition_by_join_key_string != "":
            partition_by_join_key_string = (
                "PARTITION BY " + partition_by_join_key_string
            )
        timestamps = [timestamp_field]
        if created_timestamp_column:
            timestamps.append(created_timestamp_column)
        timestamp_desc_string = " DESC, ".join(_append_alias(timestamps, "a")) + " DESC"
        a_field_string = ", ".join(
            _append_alias(join_key_columns + feature_name_columns + timestamps, "a")
        )
        b_field_string = ", ".join(
            _append_alias(join_key_columns + feature_name_columns + timestamps, "b")
        )

        query = f"""
            SELECT
                {b_field_string}
                {f", {repr(DUMMY_ENTITY_VAL)} AS {DUMMY_ENTITY_ID}" if not join_key_columns else ""}
            FROM (
                SELECT {a_field_string},
                ROW_NUMBER() OVER({partition_by_join_key_string} ORDER BY {timestamp_desc_string}) AS _feast_row
                FROM ({from_expression}) a
                WHERE a."{timestamp_field}" BETWEEN '{start_date}'::timestamptz AND '{end_date}'::timestamptz
            ) b
            WHERE _feast_row = 1
            """

        return PostgreSQLRetrievalJob(
            query=query,
            config=config,
            full_feature_names=False,
            on_demand_feature_views=None,
        )

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

        entity_schema = _get_entity_schema(entity_df, 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,
            config,
        )

        @contextlib.contextmanager
        def query_generator() -> Iterator[str]:
            table_name = offline_utils.get_temp_entity_table_name()

            _upload_entity_df(config, entity_df, 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
            )

            query_context = offline_utils.get_feature_view_query_context(
                feature_refs,
                feature_views,
                registry,
                project,
                entity_df_event_timestamp_range,
            )

            query_context_dict = [asdict(context) for context in query_context]
            # Hack for query_context.entity_selections to support uppercase in columns
            for context in query_context_dict:
                context["entity_selections"] = [
                    f'''"{entity_selection.replace(' AS ', '" AS "')}\"'''
                    for entity_selection in context["entity_selections"]
                ]

            try:
                yield build_point_in_time_query(
                    query_context_dict,
                    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,
                )
            finally:
                if table_name:
                    with _get_conn(config.offline_store) as conn, conn.cursor() as cur:
                        cur.execute(
                            sql.SQL(
                                """
                                DROP TABLE IF EXISTS {};
                                """
                            ).format(sql.Identifier(table_name)),
                        )

        return PostgreSQLRetrievalJob(
            query=query_generator,
            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
    @log_exceptions_and_usage(offline_store="postgres")
    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, PostgreSQLSource)
        from_expression = data_source.get_table_query_string()

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

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

        query = f"""
            SELECT {field_string}
            FROM {from_expression} AS paftoq_alias
            WHERE "{timestamp_field}" BETWEEN '{start_date}'::timestamptz AND '{end_date}'::timestamptz
        """

        return PostgreSQLRetrievalJob(
            query=query,
            config=config,
            full_feature_names=False,
            on_demand_feature_views=None,
        )


class PostgreSQLRetrievalJob(RetrievalJob):
    def __init__(
        self,
        query: Union[str, Callable[[], ContextManager[str]]],
        config: RepoConfig,
        full_feature_names: bool,
        on_demand_feature_views: Optional[List[OnDemandFeatureView]],
        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.config = config
        self._full_feature_names = full_feature_names
        self._on_demand_feature_views = on_demand_feature_views or []
        self._metadata = metadata

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

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

    def _to_df_internal(self) -> pd.DataFrame:
        # We use arrow format because it gives better control of the table schema
        return self._to_arrow_internal().to_pandas()

    def to_sql(self) -> str:
        with self._query_generator() as query:
            return query

    def _to_arrow_internal(self) -> pa.Table:
        with self._query_generator() as query:
            with _get_conn(self.config.offline_store) as conn, conn.cursor() as cur:
                conn.set_session(readonly=True)
                cur.execute(query)
                fields = [
                    (c.name, pg_type_code_to_arrow(c.type_code))
                    for c in cur.description
                ]
                data = cur.fetchall()
                schema = pa.schema(fields)
                # TODO: Fix...
                data_transposed: List[List[Any]] = []
                for col in range(len(fields)):
                    data_transposed.append([])
                    for row in range(len(data)):
                        data_transposed[col].append(data[row][col])

                table = pa.Table.from_arrays(
                    [pa.array(row) for row in data_transposed], schema=schema
                )
                return table

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

    def persist(self, storage: SavedDatasetStorage):
        assert isinstance(storage, SavedDatasetPostgreSQLStorage)

        df_to_postgres_table(
            config=self.config.offline_store,
            df=self.to_df(),
            table_name=storage.postgres_options._table,
        )


def _get_entity_df_event_timestamp_range(
    entity_df: Union[pd.DataFrame, str],
    entity_df_event_timestamp_col: str,
    config: RepoConfig,
) -> 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
        with _get_conn(config.offline_store) as conn, conn.cursor() as cur:
            cur.execute(
                f"SELECT MIN({entity_df_event_timestamp_col}) AS min, MAX({entity_df_event_timestamp_col}) AS max FROM ({entity_df}) as tmp_alias"
            ),
            res = cur.fetchone()
        entity_df_event_timestamp_range = (res[0], res[1])
    else:
        raise InvalidEntityType(type(entity_df))

    return entity_df_event_timestamp_range


def _append_alias(field_names: List[str], alias: str) -> List[str]:
    return [f'{alias}."{field_name}"' for field_name in field_names]


def build_point_in_time_query(
    feature_view_query_contexts: List[dict],
    left_table_query_string: str,
    entity_df_event_timestamp_col: str,
    entity_df_columns: KeysView[str],
    query_template: str,
    full_feature_names: bool = False,
) -> str:
    """Build point-in-time query between each feature view table and the entity dataframe for PostgreSQL"""
    template = Environment(loader=BaseLoader()).from_string(source=query_template)

    final_output_feature_names = list(entity_df_columns)
    final_output_feature_names.extend(
        [
            (
                f'{fv["name"]}__{fv["field_mapping"].get(feature, feature)}'
                if full_feature_names
                else fv["field_mapping"].get(feature, feature)
            )
            for fv in feature_view_query_contexts
            for feature in fv["features"]
        ]
    )

    # Add additional fields to dict
    template_context = {
        "left_table_query_string": left_table_query_string,
        "entity_df_event_timestamp_col": entity_df_event_timestamp_col,
        "unique_entity_keys": set(
            [entity for fv in feature_view_query_contexts for entity in fv["entities"]]
        ),
        "featureviews": feature_view_query_contexts,
        "full_feature_names": full_feature_names,
        "final_output_feature_names": final_output_feature_names,
    }

    query = template.render(template_context)
    return query


def _upload_entity_df(
    config: RepoConfig, entity_df: Union[pd.DataFrame, str], table_name: str
):
    if isinstance(entity_df, pd.DataFrame):
        # If the entity_df is a pandas dataframe, upload it to Postgres
        df_to_postgres_table(config.offline_store, entity_df, table_name)
    elif isinstance(entity_df, str):
        # If the entity_df is a string (SQL query), create a Postgres table out of it
        with _get_conn(config.offline_store) as conn, conn.cursor() as cur:
            cur.execute(f"CREATE TABLE {table_name} AS ({entity_df})")
    else:
        raise InvalidEntityType(type(entity_df))


def _get_entity_schema(
    entity_df: Union[pd.DataFrame, str],
    config: RepoConfig,
) -> Dict[str, np.dtype]:
    if isinstance(entity_df, pd.DataFrame):
        return dict(zip(entity_df.columns, entity_df.dtypes))

    elif isinstance(entity_df, str):
        df_query = f"({entity_df}) AS sub"
        return get_query_schema(config.offline_store, df_query)
    else:
        raise InvalidEntityType(type(entity_df))


# Copied from the Feast Redshift offline store implementation
# Note: Keep this in sync with sdk/python/feast/infra/offline_stores/redshift.py:
# MULTIPLE_FEATURE_VIEW_POINT_IN_TIME_JOIN
# https://github.com/feast-dev/feast/blob/master/sdk/python/feast/infra/offline_stores/redshift.py

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
        {% if featureview.entities %}"{{ featureview.entities | join('", "') }}",{% endif %}
        entity_timestamp,
        "{{featureview.name}}__entity_row_unique_id"
    FROM entity_dataframe
    GROUP BY
        {% if featureview.entities %}"{{ featureview.entities | join('", "')}}",{% 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 }} AS sub
    WHERE "{{ featureview.timestamp_field }}" <= (SELECT MAX(entity_timestamp) FROM entity_dataframe)
    {% if featureview.ttl == 0 %}{% else %}
    AND "{{ featureview.timestamp_field }}" >= (SELECT MIN(entity_timestamp) FROM entity_dataframe) - {{ featureview.ttl }} * interval '1' second
    {% 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 >= entity_dataframe.entity_timestamp - {{ featureview.ttl }} * interval '1' second
        {% 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 %}
    ) AS sub
    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"
) AS "{{featureview.name}}" USING ("{{featureview.name}}__entity_row_unique_id")
{% endfor %}
"""