Source code for feast.infra.offline_stores.redshift

import contextlib
import uuid
from datetime import datetime
from typing import Callable, ContextManager, Dict, Iterator, List, Optional, Union

import numpy as np
import pandas as pd
import pyarrow as pa
from pydantic import StrictStr
from pydantic.typing import Literal

from feast import OnDemandFeatureView, RedshiftSource
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.offline_store import OfflineStore, RetrievalJob
from feast.infra.utils import aws_utils
from feast.registry import Registry
from feast.repo_config import FeastConfigBaseModel, RepoConfig


[docs]class RedshiftOfflineStoreConfig(FeastConfigBaseModel): """ Offline store config for AWS Redshift """ type: Literal["redshift"] = "redshift" """ Offline store type selector""" cluster_id: StrictStr """ Redshift cluster identifier """ region: StrictStr """ Redshift cluster's AWS region """ user: StrictStr """ Redshift user name """ database: StrictStr """ Redshift database name """ s3_staging_location: StrictStr """ S3 path for importing & exporting data to Redshift """ iam_role: StrictStr """ IAM Role for Redshift, granting it access to S3 """
[docs]class RedshiftOfflineStore(OfflineStore):
[docs] @staticmethod def pull_latest_from_table_or_query( config: RepoConfig, data_source: DataSource, join_key_columns: List[str], feature_name_columns: List[str], event_timestamp_column: str, created_timestamp_column: Optional[str], start_date: datetime, end_date: datetime, ) -> RetrievalJob: assert isinstance(data_source, RedshiftSource) assert isinstance(config.offline_store, RedshiftOfflineStoreConfig) from_expression = data_source.get_table_query_string() partition_by_join_key_string = ", ".join(join_key_columns) if partition_by_join_key_string != "": partition_by_join_key_string = ( "PARTITION BY " + partition_by_join_key_string ) timestamp_columns = [event_timestamp_column] 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 ) redshift_client = aws_utils.get_redshift_data_client( config.offline_store.region ) s3_resource = aws_utils.get_s3_resource(config.offline_store.region) query = f""" SELECT {field_string} {f", {repr(DUMMY_ENTITY_VAL)} 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 {event_timestamp_column} BETWEEN TIMESTAMP '{start_date}' AND TIMESTAMP '{end_date}' ) WHERE _feast_row = 1 """ # When materializing a single feature view, we don't need full feature names. On demand transforms aren't materialized return RedshiftRetrievalJob( query=query, redshift_client=redshift_client, s3_resource=s3_resource, config=config, full_feature_names=False, on_demand_feature_views=None, )
@staticmethod 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: assert isinstance(config.offline_store, RedshiftOfflineStoreConfig) redshift_client = aws_utils.get_redshift_data_client( config.offline_store.region ) s3_resource = aws_utils.get_s3_resource(config.offline_store.region) @contextlib.contextmanager def query_generator() -> Iterator[str]: table_name = offline_utils.get_temp_entity_table_name() entity_schema = _upload_entity_df_and_get_entity_schema( entity_df, redshift_client, config, s3_resource, table_name ) entity_df_event_timestamp_col = offline_utils.infer_event_timestamp_from_entity_df( entity_schema ) 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 Redshift SQL query query_context = offline_utils.get_feature_view_query_context( feature_refs, feature_views, registry, project, ) # Generate the Redshift 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, ) try: yield query finally: # Always clean up the uploaded Redshift table aws_utils.execute_redshift_statement( redshift_client, config.offline_store.cluster_id, config.offline_store.database, config.offline_store.user, f"DROP TABLE IF EXISTS {table_name}", ) return RedshiftRetrievalJob( query=query_generator, redshift_client=redshift_client, s3_resource=s3_resource, config=config, full_feature_names=full_feature_names, on_demand_feature_views=OnDemandFeatureView.get_requested_odfvs( feature_refs, project, registry ), )
[docs]class RedshiftRetrievalJob(RetrievalJob): def __init__( self, query: Union[str, Callable[[], ContextManager[str]]], redshift_client, s3_resource, config: RepoConfig, full_feature_names: bool, on_demand_feature_views: Optional[List[OnDemandFeatureView]], ): """Initialize RedshiftRetrievalJob object. Args: query: Redshift SQL query to execute. Either a string, or a generator function that handles the artifact cleanup. redshift_client: boto3 redshift-data client s3_resource: boto3 s3 resource object config: Feast repo config full_feature_names: Whether to add the feature view prefixes to the feature names on_demand_feature_views: A list of on demand transforms to apply at retrieval time """ 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._redshift_client = redshift_client self._s3_resource = s3_resource self._config = config self._s3_path = ( self._config.offline_store.s3_staging_location + "/unload/" + str(uuid.uuid4()) ) self._full_feature_names = full_feature_names self._on_demand_feature_views = on_demand_feature_views @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: return aws_utils.unload_redshift_query_to_df( self._redshift_client, self._config.offline_store.cluster_id, self._config.offline_store.database, self._config.offline_store.user, self._s3_resource, self._s3_path, self._config.offline_store.iam_role, query, ) def _to_arrow_internal(self) -> pa.Table: with self._query_generator() as query: return aws_utils.unload_redshift_query_to_pa( self._redshift_client, self._config.offline_store.cluster_id, self._config.offline_store.database, self._config.offline_store.user, self._s3_resource, self._s3_path, self._config.offline_store.iam_role, query, )
[docs] def to_s3(self) -> str: """ Export dataset to S3 in Parquet format and return path """ if self.on_demand_feature_views: transformed_df = self.to_df() aws_utils.upload_df_to_s3(self._s3_resource, self._s3_path, transformed_df) return self._s3_path with self._query_generator() as query: aws_utils.execute_redshift_query_and_unload_to_s3( self._redshift_client, self._config.offline_store.cluster_id, self._config.offline_store.database, self._config.offline_store.user, self._s3_path, self._config.offline_store.iam_role, query, ) return self._s3_path
[docs] def to_redshift(self, table_name: str) -> None: """ Save dataset as a new Redshift table """ if self.on_demand_feature_views: transformed_df = self.to_df() aws_utils.upload_df_to_redshift( self._redshift_client, self._config.offline_store.cluster_id, self._config.offline_store.database, self._config.offline_store.user, self._s3_resource, f"{self._config.offline_store.s3_staging_location}/features_df/{table_name}.parquet", self._config.offline_store.iam_role, table_name, transformed_df, ) return with self._query_generator() as query: query = f'CREATE TABLE "{table_name}" AS ({query});\n' aws_utils.execute_redshift_statement( self._redshift_client, self._config.offline_store.cluster_id, self._config.offline_store.database, self._config.offline_store.user, query, )
def _upload_entity_df_and_get_entity_schema( entity_df: Union[pd.DataFrame, str], redshift_client, config: RepoConfig, s3_resource, table_name: str, ) -> Dict[str, np.dtype]: if isinstance(entity_df, pd.DataFrame): # If the entity_df is a pandas dataframe, upload it to Redshift # and construct the schema from the original entity_df dataframe aws_utils.upload_df_to_redshift( redshift_client, config.offline_store.cluster_id, config.offline_store.database, config.offline_store.user, s3_resource, f"{config.offline_store.s3_staging_location}/entity_df/{table_name}.parquet", config.offline_store.iam_role, table_name, entity_df, ) return dict(zip(entity_df.columns, entity_df.dtypes)) elif isinstance(entity_df, str): # If the entity_df is a string (SQL query), create a Redshift table out of it, # get pandas dataframe consisting of 1 row (LIMIT 1) and generate the schema out of it aws_utils.execute_redshift_statement( redshift_client, config.offline_store.cluster_id, config.offline_store.database, config.offline_store.user, f"CREATE TABLE {table_name} AS ({entity_df})", ) limited_entity_df = RedshiftRetrievalJob( f"SELECT * FROM {table_name} LIMIT 1", redshift_client, s3_resource, config, full_feature_names=False, on_demand_feature_views=None, ).to_df() return dict(zip(limited_entity_df.columns, limited_entity_df.dtypes)) else: raise InvalidEntityType(type(entity_df)) # This query is based on sdk/python/feast/infra/offline_stores/bigquery.py:MULTIPLE_FEATURE_VIEW_POINT_IN_TIME_JOIN # There are couple of changes from BigQuery: # 1. Use VARCHAR instead of STRING type # 2. Use "t - x * interval '1' second" instead of "Timestamp_sub(...)" # 3. Replace `SELECT * EXCEPT (...)` with `SELECT *`, because `EXCEPT` is not supported by Redshift. # Instead, we drop the column later after creating the table out of the query. # We need to keep this query in sync with BigQuery. 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 | join(', ')}}{% if featureview.entities %},{% else %}{% endif %} entity_timestamp, {{featureview.name}}__entity_row_unique_id FROM entity_dataframe GROUP BY {{ featureview.entities | 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 `event_timestamp_column` 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 `event_timestamp_column` 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.event_timestamp_column }} 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 }}__{{feature}}{% else %}{{ feature }}{% endif %}{% if loop.last %}{% else %}, {% endif %} {% endfor %} FROM {{ featureview.table_subquery }} WHERE {{ featureview.event_timestamp_column }} <= (SELECT MAX(entity_timestamp) FROM entity_dataframe) {% if featureview.ttl == 0 %}{% else %} AND {{ featureview.event_timestamp_column }} >= (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 %} ) 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 }}__{{feature}}{% else %}{{ feature }}{% endif %} {% endfor %} FROM {{ featureview.name }}__cleaned ) USING ({{featureview.name}}__entity_row_unique_id) {% endfor %} """