import os

import uuid

from datetime import datetime, timedelta, timezone

from pathlib import Path

from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union

import dask

import dask.dataframe as dd

import pandas as pd

import pyarrow

import pyarrow.dataset

import pyarrow.parquet

import pytz

from feast.data_source import DataSource

from feast.errors import (

FeastJoinKeysDuringMaterialization,

SavedDatasetLocationAlreadyExists,

)

from feast.feature_logging import LoggingConfig, LoggingSource

from feast.feature_view import DUMMY_ENTITY_ID, DUMMY_ENTITY_VAL, FeatureView

from feast.infra.offline_stores.file_source import (

FileLoggingDestination,

FileSource,

SavedDatasetFileStorage,

)

from feast.infra.offline_stores.offline_store import (

OfflineStore,

RetrievalJob,

RetrievalMetadata,

)

from feast.infra.offline_stores.offline_utils import (

DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL,

get_pyarrow_schema_from_batch_source,

)

from feast.infra.registry.base_registry import BaseRegistry

from feast.on_demand_feature_view import OnDemandFeatureView

from feast.repo_config import FeastConfigBaseModel, RepoConfig

from feast.saved_dataset import SavedDatasetStorage

from feast.utils import _get_requested_feature_views_to_features_dict, make_tzaware

# DaskRetrievalJob will cast string objects to string[pyarrow] from dask version 2023.7.1

# This is not the desired behavior for our use case, so we set the convert-string option to False

# See (https://github.com/dask/dask/issues/10881#issuecomment-1923327936)

dask.config.set({"dataframe.convert-string": False})

class DaskOfflineStoreConfig(FeastConfigBaseModel):

"""Offline store config for dask store"""

type: Union[Literal["dask"], Literal["file"]] = "dask"

""" Offline store type selector"""

class DaskRetrievalJob(RetrievalJob):

def __init__(

self,

evaluation_function: Callable,

full_feature_names: bool,

repo_path: str,

on_demand_feature_views: Optional[List[OnDemandFeatureView]] = None,

metadata: Optional[RetrievalMetadata] = None,

):

"""Initialize a lazy historical retrieval job"""

# The evaluation function executes a stored procedure to compute a historical retrieval.

self.evaluation_function = evaluation_function

self._full_feature_names = full_feature_names

self._on_demand_feature_views = on_demand_feature_views or []

self._metadata = metadata

self.repo_path = repo_path

@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, timeout: Optional[int] = None) -> pd.DataFrame:

# Only execute the evaluation function to build the final historical retrieval dataframe at the last moment.

df = self.evaluation_function().compute()

df = df.reset_index(drop=True)

return df

def _to_arrow_internal(self, timeout: Optional[int] = None):

# Only execute the evaluation function to build the final historical retrieval dataframe at the last moment.

df = self.evaluation_function().compute()

return pyarrow.Table.from_pandas(df)

def persist(

self,

storage: SavedDatasetStorage,

allow_overwrite: Optional[bool] = False,

timeout: Optional[int] = None,

):

assert isinstance(storage, SavedDatasetFileStorage)

# Check if the specified location already exists.

if not allow_overwrite and os.path.exists(storage.file_options.uri):

raise SavedDatasetLocationAlreadyExists(location=storage.file_options.uri)

absolute_path = FileSource.get_uri_for_file_path(

repo_path=self.repo_path, uri=storage.file_options.uri

)

filesystem, path = FileSource.create_filesystem_and_path(

str(absolute_path),

storage.file_options.s3_endpoint_override,

)

if path.endswith(".parquet"):

pyarrow.parquet.write_table(

self.to_arrow(), where=path, filesystem=filesystem

)

else:

# otherwise assume destination is directory

pyarrow.parquet.write_to_dataset(

self.to_arrow(), root_path=path, filesystem=filesystem

)

@property

def metadata(self) -> Optional[RetrievalMetadata]:

return self._metadata

def supports_remote_storage_export(self) -> bool:

return False

class DaskOfflineStore(OfflineStore):

@staticmethod

def get_historical_features(

config: RepoConfig,

feature_views: List[FeatureView],

feature_refs: List[str],

entity_df: Optional[Union[pd.DataFrame, dd.DataFrame, str]],

registry: BaseRegistry,

project: str,

full_feature_names: bool = False,

**kwargs,

) -> RetrievalJob:

assert isinstance(config.offline_store, DaskOfflineStoreConfig)

for fv in feature_views:

assert isinstance(fv.batch_source, FileSource)

# Allow non-entity mode using start/end timestamps to enable bounded retrievals without an input entity_df.

# This synthesizes a minimal entity_df solely to drive the existing join and metadata plumbing without

# incurring source scans here; actual pushdowns can be layered in follow-ups if needed.

start_date: Optional[datetime] = kwargs.get("start_date", None)

end_date: Optional[datetime] = kwargs.get("end_date", None)

non_entity_mode = entity_df is None

if non_entity_mode:

# Default end_date to current time (UTC) to keep behavior predictable without extra parameters.

end_date = (

make_tzaware(end_date) if end_date else datetime.now(timezone.utc)

)

# When start_date is not provided, choose a conservative lower bound using max TTL, otherwise fall back.

if start_date is None:

max_ttl_seconds = 0

for fv in feature_views:

if fv.ttl and isinstance(fv.ttl, timedelta):

max_ttl_seconds = max(

max_ttl_seconds, int(fv.ttl.total_seconds())

)

if max_ttl_seconds > 0:

start_date = end_date - timedelta(seconds=max_ttl_seconds)

else:

# Keep default window bounded to avoid unbounded scans by default.

start_date = end_date - timedelta(days=30)

start_date = make_tzaware(start_date)

# Minimal synthetic entity_df: one timestamp row; join keys are not materialized here on purpose to avoid

# accidental dependence on specific feature view schemas at this layer.

entity_df = pd.DataFrame(

{DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL: [end_date]}

)

else:

if not isinstance(entity_df, pd.DataFrame) and not isinstance(

entity_df, dd.DataFrame

):

raise ValueError(

f"Please provide an entity_df of type pd.DataFrame or dask.dataframe.DataFrame instead of type {type(entity_df)}"

)

entity_df_event_timestamp_col = DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL # local modifiable copy of global variable

if entity_df_event_timestamp_col not in entity_df.columns:

datetime_columns = entity_df.select_dtypes(

include=["datetime", "datetimetz"]

).columns

if len(datetime_columns) == 1:

print(

f"Using {datetime_columns[0]} as the event timestamp. To specify a column explicitly, please name it {DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL}."

)

entity_df_event_timestamp_col = datetime_columns[0]

else:

raise ValueError(

f"Please provide an entity_df with a column named {DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL} representing the time of events."

)

(

feature_views_to_features,

on_demand_feature_views_to_features,

) = _get_requested_feature_views_to_features_dict(

feature_refs,

feature_views,

registry.list_on_demand_feature_views(config.project),

)

entity_df_event_timestamp_range = (

(start_date, end_date)

if non_entity_mode

else _get_entity_df_event_timestamp_range(

entity_df, entity_df_event_timestamp_col

)

)

# Create lazy function that is only called from the RetrievalJob object

def evaluate_historical_retrieval():

# Create a copy of entity_df to prevent modifying the original

entity_df_with_features = entity_df.copy()

entity_df_event_timestamp_col_type = entity_df_with_features.dtypes[

entity_df_event_timestamp_col

]

# TODO: need to figure out why the value of entity_df_event_timestamp_col_type.tz is pytz.UTC

if (

not hasattr(entity_df_event_timestamp_col_type, "tz")

or entity_df_event_timestamp_col_type.tz != pytz.UTC

):

# Make sure all event timestamp fields are tz-aware. We default tz-naive fields to UTC

entity_df_with_features[entity_df_event_timestamp_col] = (

entity_df_with_features[entity_df_event_timestamp_col].apply(

lambda x: x

if x.tzinfo is not None

else x.replace(tzinfo=timezone.utc)

)

)

# Convert event timestamp column to datetime and normalize time zone to UTC

# This is necessary to avoid issues with pd.merge_asof

if isinstance(entity_df_with_features, dd.DataFrame):

entity_df_with_features[entity_df_event_timestamp_col] = (

dd.to_datetime(

entity_df_with_features[entity_df_event_timestamp_col],

utc=True,

)

)

else:

entity_df_with_features[entity_df_event_timestamp_col] = (

pd.to_datetime(

entity_df_with_features[entity_df_event_timestamp_col],

utc=True,

)

)

# Sort event timestamp values

entity_df_with_features = entity_df_with_features.sort_values(

entity_df_event_timestamp_col

)

all_join_keys = []

# Load feature view data from sources and join them incrementally

for feature_view, features in feature_views_to_features.items():

timestamp_field = feature_view.batch_source.timestamp_field

created_timestamp_column = (

feature_view.batch_source.created_timestamp_column

)

# Build a list of entity columns to join on (from the right table)

join_keys = []

for entity_column in feature_view.entity_columns:

join_key = feature_view.projection.join_key_map.get(

entity_column.name, entity_column.name

)

join_keys.append(join_key)

right_entity_key_columns = [

timestamp_field,

created_timestamp_column,

] + join_keys

right_entity_key_columns = [c for c in right_entity_key_columns if c]

all_join_keys = list(set(all_join_keys + join_keys))

df_to_join = _read_datasource(

feature_view.batch_source, config.repo_path

)

df_to_join, timestamp_field = _field_mapping(

df_to_join,

feature_view,

features,

right_entity_key_columns,

entity_df_event_timestamp_col,

timestamp_field,

full_feature_names,

)

# In non-entity mode, if the synthetic entity_df lacks join keys, cross join to build a snapshot

# of all entities as-of the requested timestamp, then rely on TTL and deduplication to select

# the appropriate latest rows per entity.

current_join_keys = join_keys

if non_entity_mode:

current_join_keys = []

df_to_join = _merge(

entity_df_with_features, df_to_join, current_join_keys

)

df_to_join = _normalize_timestamp(

df_to_join, timestamp_field, created_timestamp_column

)

df_to_join = _filter_ttl(

df_to_join,

feature_view,

entity_df_event_timestamp_col,

timestamp_field,

)

df_to_join = _drop_duplicates(

df_to_join,

all_join_keys,

timestamp_field,

created_timestamp_column,

entity_df_event_timestamp_col,

)

entity_df_with_features = _drop_columns(

df_to_join, features, timestamp_field, created_timestamp_column

)

# Ensure that we delete dataframes to free up memory

del df_to_join

return entity_df_with_features.persist()

job = DaskRetrievalJob(

evaluation_function=evaluate_historical_retrieval,

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(set(entity_df.columns) - {entity_df_event_timestamp_col}),

min_event_timestamp=entity_df_event_timestamp_range[0],

max_event_timestamp=entity_df_event_timestamp_range[1],

),

repo_path=str(config.repo_path),

)

return job

@staticmethod

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: Optional[datetime] = None,

end_date: Optional[datetime] = None,

) -> RetrievalJob:

assert isinstance(config.offline_store, DaskOfflineStoreConfig)

assert isinstance(data_source, FileSource)

def evaluate_func():

df = DaskOfflineStore.evaluate_offline_job(

config=config,

data_source=data_source,

join_key_columns=join_key_columns,

timestamp_field=timestamp_field,

created_timestamp_column=created_timestamp_column,

start_date=start_date,

end_date=end_date,

)

ts_columns = (

[timestamp_field, created_timestamp_column]

if created_timestamp_column

else [timestamp_field]

)

columns_to_extract = set(

join_key_columns + feature_name_columns + ts_columns

)

if join_key_columns:

df = df.drop_duplicates(

join_key_columns, keep="last", ignore_index=True

)

else:

df[DUMMY_ENTITY_ID] = DUMMY_ENTITY_VAL

columns_to_extract.add(DUMMY_ENTITY_ID)

return df[list(columns_to_extract)].persist()

# When materializing a single feature view, we don't need full feature names. On demand transforms aren't materialized

return DaskRetrievalJob(

evaluation_function=evaluate_func,

full_feature_names=False,

repo_path=str(config.repo_path),

)

@staticmethod

def evaluate_offline_job(

config: RepoConfig,

data_source: FileSource,

join_key_columns: List[str],

timestamp_field: str,

created_timestamp_column: Optional[str] = None,

start_date: Optional[datetime] = None,

end_date: Optional[datetime] = None,

) -> dd.DataFrame:

# Create lazy function that is only called from the RetrievalJob object

source_df = _read_datasource(data_source, config.repo_path)

source_df = _normalize_timestamp(

source_df, timestamp_field, created_timestamp_column

)

source_columns = set(source_df.columns)

if not set(join_key_columns).issubset(source_columns):

raise FeastJoinKeysDuringMaterialization(

data_source.path, set(join_key_columns), source_columns

)

# try-catch block is added to deal with this issue https://github.com/dask/dask/issues/8939.

# TODO(kevjumba): remove try catch when fix is merged upstream in Dask.

try:

if created_timestamp_column:

source_df = source_df.sort_values(

by=created_timestamp_column,

)

source_df = source_df.sort_values(by=timestamp_field)

except ZeroDivisionError:

# Use 1 partition to get around case where everything in timestamp column is the same so the partition algorithm doesn't

# try to divide by zero.

if created_timestamp_column:

source_df = source_df.sort_values(

by=created_timestamp_column, npartitions=1

)

source_df = source_df.sort_values(by=timestamp_field, npartitions=1)

# TODO: The old implementation is inclusive of start_date and exclusive of end_date.

# Which is inconsistent with other offline stores.

if start_date or end_date:

if start_date and end_date:

source_df = source_df[

source_df[timestamp_field].between(

start_date, end_date, inclusive="both"

)

]

elif start_date:

source_df = source_df[source_df[timestamp_field] >= start_date]

elif end_date:

source_df = source_df[source_df[timestamp_field] <= end_date]

source_df = source_df.persist()

return source_df

@staticmethod

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,

created_timestamp_column: Optional[str] = None,

start_date: Optional[datetime] = None,

end_date: Optional[datetime] = None,

) -> RetrievalJob:

assert isinstance(config.offline_store, DaskOfflineStoreConfig)

assert isinstance(data_source, FileSource)

def evaluate_func():

df = DaskOfflineStore.evaluate_offline_job(

config=config,

data_source=data_source,

join_key_columns=join_key_columns,

timestamp_field=timestamp_field,

created_timestamp_column=created_timestamp_column,

start_date=start_date,

end_date=end_date,

)

ts_columns = (

[timestamp_field, created_timestamp_column]

if created_timestamp_column

else [timestamp_field]

)

columns_to_extract = set(

join_key_columns + feature_name_columns + ts_columns

)

if not join_key_columns:

df[DUMMY_ENTITY_ID] = DUMMY_ENTITY_VAL

columns_to_extract.add(DUMMY_ENTITY_ID)

# TODO: Decides if we want to field mapping for pull_latest_from_table_or_query

# This is default for other offline store.

df = df[list(columns_to_extract)]

df.persist()

return df

# When materializing a single feature view, we don't need full feature names. On demand transforms aren't materialized

return DaskRetrievalJob(

evaluation_function=evaluate_func,

full_feature_names=False,

repo_path=str(config.repo_path),

)

@staticmethod

def write_logged_features(

config: RepoConfig,

data: Union[pyarrow.Table, Path],

source: LoggingSource,

logging_config: LoggingConfig,

registry: BaseRegistry,

):

assert isinstance(config.offline_store, DaskOfflineStoreConfig)

destination = logging_config.destination

assert isinstance(destination, FileLoggingDestination)

if isinstance(data, Path):

# Since this code will be mostly used from Go-created thread, it's better to avoid producing new threads

data = pyarrow.parquet.read_table(data, use_threads=False, pre_buffer=False)

if config.repo_path is not None and not Path(destination.path).is_absolute():

absolute_path = config.repo_path / destination.path

else:

absolute_path = Path(destination.path)

filesystem, path = FileSource.create_filesystem_and_path(

str(absolute_path),

destination.s3_endpoint_override,

)

pyarrow.dataset.write_dataset(

data,

base_dir=path,

basename_template=f"{uuid.uuid4().hex}-{{i}}.parquet",

partitioning=destination.partition_by,

filesystem=filesystem,

use_threads=False,

format=pyarrow.dataset.ParquetFileFormat(),

existing_data_behavior="overwrite_or_ignore",

)

@staticmethod

def offline_write_batch(

config: RepoConfig,

feature_view: FeatureView,

table: pyarrow.Table,

progress: Optional[Callable[[int], Any]],

):

assert isinstance(config.offline_store, DaskOfflineStoreConfig)

assert isinstance(feature_view.batch_source, FileSource)

pa_schema, column_names = 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}."

)

file_options = feature_view.batch_source.file_options

absolute_path = FileSource.get_uri_for_file_path(

repo_path=config.repo_path, uri=file_options.uri

)

filesystem, path = FileSource.create_filesystem_and_path(

str(absolute_path), file_options.s3_endpoint_override

)

prev_table = pyarrow.parquet.read_table(

path, filesystem=filesystem, memory_map=True

)

if table.schema != prev_table.schema:

table = table.cast(prev_table.schema)

new_table = pyarrow.concat_tables([table, prev_table])

writer = pyarrow.parquet.ParquetWriter(

path, table.schema, filesystem=filesystem

)

writer.write_table(new_table)

writer.close()

def _get_entity_df_event_timestamp_range(

entity_df: Union[pd.DataFrame, str],

entity_df_event_timestamp_col: str,

) -> Tuple[datetime, datetime]:

if not isinstance(entity_df, pd.DataFrame):

raise ValueError(

f"Please provide an entity_df of type {type(pd.DataFrame)} instead of type {type(entity_df)}"

)

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)

return (

entity_df_event_timestamp.min().to_pydatetime(),

entity_df_event_timestamp.max().to_pydatetime(),

)

def _read_datasource(data_source, repo_path) -> dd.DataFrame:

storage_options = (

{

"client_kwargs": {

"endpoint_url": data_source.file_options.s3_endpoint_override

}

}

if data_source.file_options.s3_endpoint_override

else None

)

path = FileSource.get_uri_for_file_path(

repo_path=repo_path,

uri=data_source.file_options.uri,

)

return dd.read_parquet(

path,

storage_options=storage_options,

)

def _run_dask_field_mapping(

table: dd.DataFrame,

field_mapping: Dict[str, str],

):

if field_mapping:

# run field mapping in the forward direction

table = table.rename(columns=field_mapping)

table = table.persist()

return table

def _field_mapping(

df_to_join: dd.DataFrame,

feature_view: FeatureView,

features: List[str],

right_entity_key_columns: List[str],

entity_df_event_timestamp_col: str,

timestamp_field: str,

full_feature_names: bool,

) -> Tuple[dd.DataFrame, str]:

# Rename columns by the field mapping dictionary if it exists

if feature_view.batch_source.field_mapping:

df_to_join = _run_dask_field_mapping(

df_to_join, feature_view.batch_source.field_mapping

)

# Rename entity columns by the join_key_map dictionary if it exists

if feature_view.projection.join_key_map:

df_to_join = _run_dask_field_mapping(

df_to_join, feature_view.projection.join_key_map

)

# Build a list of all the features we should select from this source

feature_names = []

columns_map = {}

for feature in features:

# Modify the separator for feature refs in column names to double underscore. We are using

# double underscore as separator for consistency with other databases like BigQuery,

# where there are very few characters available for use as separators

if full_feature_names:

formatted_feature_name = (

f"{feature_view.projection.name_to_use()}__{feature}"

)

else:

formatted_feature_name = feature

# Add the feature name to the list of columns

feature_names.append(formatted_feature_name)

columns_map[feature] = formatted_feature_name

# Ensure that the source dataframe feature column includes the feature view name as a prefix

df_to_join = _run_dask_field_mapping(df_to_join, columns_map)

# Select only the columns we need to join from the feature dataframe

df_to_join = df_to_join[right_entity_key_columns + feature_names]

df_to_join = df_to_join.persist()

# Make sure to not have duplicated columns

if entity_df_event_timestamp_col == timestamp_field:

df_to_join = _run_dask_field_mapping(

df_to_join,

{timestamp_field: f"__{timestamp_field}"},

)

timestamp_field = f"__{timestamp_field}"

return df_to_join.persist(), timestamp_field

def _merge(

entity_df_with_features: dd.DataFrame,

df_to_join: dd.DataFrame,

join_keys: List[str],

) -> dd.DataFrame:

# tmp join keys needed for cross join with null join table view

tmp_join_keys = []

if not join_keys:

entity_df_with_features["__tmp"] = 1

df_to_join["__tmp"] = 1

tmp_join_keys = ["__tmp"]

# Get only data with requested entities

df_to_join = dd.merge(

entity_df_with_features,

df_to_join,

left_on=join_keys or tmp_join_keys,

right_on=join_keys or tmp_join_keys,

suffixes=("", "__"),

how="left",

)

if tmp_join_keys:

df_to_join = df_to_join.drop(tmp_join_keys, axis=1).persist()

else:

df_to_join = df_to_join.persist()

return df_to_join

def _normalize_timestamp(

df_to_join: dd.DataFrame,

timestamp_field: str,

created_timestamp_column: Optional[str] = None,

) -> dd.DataFrame:

df_to_join_types = df_to_join.dtypes

timestamp_field_type = df_to_join_types[timestamp_field]

if created_timestamp_column:

created_timestamp_column_type = df_to_join_types[created_timestamp_column]

# TODO: need to figure out why the value of timestamp_field_type.tz is pytz.UTC

if not hasattr(timestamp_field_type, "tz") or timestamp_field_type.tz != pytz.UTC:

# if you are querying for the event timestamp field, we have to deduplicate

if len(df_to_join[timestamp_field].shape) > 1:

df_to_join, dups = _df_column_uniquify(df_to_join)

df_to_join = df_to_join.drop(columns=dups)

# Make sure all timestamp fields are tz-aware. We default tz-naive fields to UTC

df_to_join[timestamp_field] = df_to_join[timestamp_field].apply(

lambda x: x if x.tzinfo else x.replace(tzinfo=timezone.utc),

meta=(timestamp_field, "datetime64[ns, UTC]"),

)

# TODO: need to figure out why the value of created_timestamp_column_type.tz is pytz.UTC

if created_timestamp_column and (

not hasattr(created_timestamp_column_type, "tz")

or created_timestamp_column_type.tz != pytz.UTC

):

if len(df_to_join[created_timestamp_column].shape) > 1:

# if you are querying for the created timestamp field, we have to deduplicate

df_to_join, dups = _df_column_uniquify(df_to_join)

df_to_join = df_to_join.drop(columns=dups)

df_to_join[created_timestamp_column] = df_to_join[

created_timestamp_column

].apply(

lambda x: x if x.tzinfo else x.replace(tzinfo=timezone.utc),

meta=(timestamp_field, "datetime64[ns, UTC]"),

)

return df_to_join.persist()

def _filter_ttl(

df_to_join: dd.DataFrame,

feature_view: FeatureView,

entity_df_event_timestamp_col: str,

timestamp_field: str,

) -> dd.DataFrame:

# Filter rows by defined timestamp tolerance

if feature_view.ttl and feature_view.ttl.total_seconds() != 0:

df_to_join = df_to_join[

# do not drop entity rows if one of the sources returns NaNs

df_to_join[timestamp_field].isna()

| (

(

df_to_join[timestamp_field]

>= df_to_join[entity_df_event_timestamp_col] - feature_view.ttl

)

& (

df_to_join[timestamp_field]

<= df_to_join[entity_df_event_timestamp_col]

)

)

]

df_to_join = df_to_join.persist()

else:

df_to_join = df_to_join[

# do not drop entity rows if one of the sources returns NaNs

df_to_join[timestamp_field].isna()

| (df_to_join[timestamp_field] <= df_to_join[entity_df_event_timestamp_col])

]

df_to_join = df_to_join.persist()

return df_to_join

def _drop_duplicates(

df_to_join: dd.DataFrame,

all_join_keys: List[str],

timestamp_field: str,

created_timestamp_column: str,

entity_df_event_timestamp_col: str,

) -> dd.DataFrame:

column_order = df_to_join.columns

# try-catch block is added to deal with this issue https://github.com/dask/dask/issues/8939.

# TODO(kevjumba): remove try catch when fix is merged upstream in Dask.

try:

if created_timestamp_column:

df_to_join = df_to_join.sort_values(

by=created_timestamp_column, na_position="first"

)

df_to_join = df_to_join.persist()

df_to_join = df_to_join.sort_values(by=timestamp_field, na_position="first")

df_to_join = df_to_join.persist()

except ZeroDivisionError:

# Use 1 partition to get around case where everything in timestamp column is the same so the partition algorithm doesn't

# try to divide by zero.

if created_timestamp_column:

df_to_join = df_to_join[column_order].sort_values(

by=created_timestamp_column, na_position="first", npartitions=1

)

df_to_join = df_to_join.persist()

df_to_join = df_to_join[column_order].sort_values(

by=timestamp_field, na_position="first", npartitions=1

)

df_to_join = df_to_join.persist()

df_to_join = df_to_join.drop_duplicates(

all_join_keys + [entity_df_event_timestamp_col],

keep="last",

ignore_index=True,

)

return df_to_join.persist()

def _drop_columns(

df_to_join: dd.DataFrame,

features: List[str],

timestamp_field: str,

created_timestamp_column: str,

) -> dd.DataFrame:

entity_df_with_features = df_to_join

timestamp_columns = [

timestamp_field,

created_timestamp_column,

]

for column in timestamp_columns:

if column and column not in features:

entity_df_with_features = entity_df_with_features.drop(

[column], axis=1

).persist()

return entity_df_with_features

def _df_column_uniquify(df: dd.DataFrame) -> Tuple[dd.DataFrame, List[str]]:

df_columns = df.columns

new_columns = []

duplicate_cols = []

for item in df_columns:

counter = 0

newitem = item

while newitem in new_columns:

counter += 1

newitem = "{}_{}".format(item, counter)

if counter > 0:

duplicate_cols.append(newitem)

new_columns.append(newitem)

df.columns = new_columns

return df, duplicate_cols