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feat: Offline Store historical features retrieval based on datetime range in Ray #5738

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Merged
ntkathole merged 5 commits into from
Dec 30, 2025
Merged

feat: Offline Store historical features retrieval based on datetime range in Ray #5738

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1b5aebe
feat: Offline Store historical features retrieval based on datetime r…
aniketpalu Nov 25, 2025
de8b2c5
Reforamatted code to fix lint issues
aniketpalu Nov 25, 2025
c25280c
preserve event_timestamp in non-entity mode for correct point-in-time…
aniketpalu Dec 26, 2025
0c20ac0
Minor lint changes
aniketpalu Dec 26, 2025
f589956
Added test cases for datetime range based feature retrieval in Ray
aniketpalu Dec 29, 2025
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Reforamatted code to fix lint issues 
Signed-off-by: Aniket Paluskar <apaluska@redhat.com>
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@aniketpalu @ntkathole
aniketpalu authored and ntkathole committed Dec 30, 2025
commit de8b2c50e23e51722f8906a4570d151143dec54d
12 changes: 9 additions & 3 deletions sdk/python/feast/infra/offline_stores/contrib/ray_offline_store/ray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1203,15 +1203,19 @@ def _compute_non_entity_dates_ray(
end_date_opt: Optional[datetime],
) -> Tuple[datetime, datetime]:
# Why: derive bounded time window when no entity_df is provided using explicit dates or max TTL fallback
end_date = make_tzaware(end_date_opt) if end_date_opt else make_tzaware(datetime.utcnow())
end_date = (
make_tzaware(end_date_opt) if end_date_opt else make_tzaware(datetime.utcnow())
)
if start_date_opt is None:
max_ttl_seconds = 0
for fv in feature_views:
if getattr(fv, "ttl", None):
try:
ttl_val = fv.ttl
if isinstance(ttl_val, timedelta):
max_ttl_seconds = max(max_ttl_seconds, int(ttl_val.total_seconds()))
max_ttl_seconds = max(
max_ttl_seconds, int(ttl_val.total_seconds())
)
except Exception:
pass
start_date = (
Expand Down Expand Up @@ -1323,7 +1327,9 @@ def _align_and_union_entities_ray(
entity_ds = aligned[0]
for ds in aligned[1:]:
entity_ds = entity_ds.union(ds)
return entity_ds.map_batches(_make_distinct_by_keys(all_join_keys), batch_format="pandas")
return entity_ds.map_batches(
_make_distinct_by_keys(all_join_keys), batch_format="pandas"
)


def _add_asof_ts_ray(ds: Dataset, end_date: datetime) -> Dataset:
Expand Down