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feat: [Backend] Data Quality Monitoring with native compute, multi-backend support, REST API, CLI #6202

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Merged
ntkathole merged 10 commits into from
Jun 9, 2026
Merged

feat: [Backend] Data Quality Monitoring with native compute, multi-backend support, REST API, CLI #6202

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d273343
feat: Statistical/Distribution metrics in Feast
jyejare Mar 31, 2026
65a6849
chore: Serving Time metrics and monitoring rollout to more backends
jyejare Apr 20, 2026
261c6e6
chore: Performance Improvements
jyejare Apr 20, 2026
f32628d
docs: Monitoring User Guide
jyejare Apr 21, 2026
e6fcd74
feat: Optional Monitoring and utils file added
jyejare Apr 21, 2026
1f07b23
test: Validate the query logic to validate metrics calculation
jyejare May 4, 2026
f1dbcb9
fix: Review comments and relevent code fixed
jyejare May 7, 2026
1a41858
fix: None values handled gracefully
jyejare May 19, 2026
707114e
chore: Update the monitoring backend based on frontent needs
jyejare May 26, 2026
50b45ac
fix: Address Devin AI and others review comments
jyejare May 28, 2026
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chore: Performance Improvements 
Signed-off-by: Jitendra Yejare <11752425+jyejare@users.noreply.github.com>
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@jyejare
jyejare committed Jun 9, 2026
commit 261c6e64841d01d3941f5100c7adc48cfe7d22c5
170 changes: 28 additions & 142 deletions sdk/python/feast/infra/offline_stores/bigquery.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,16 @@
RetrievalMetadata,
)
from feast.infra.registry.base_registry import BaseRegistry
from feast.monitoring.monitoring_utils import (
MON_TABLE_FEATURE,
MON_TABLE_FEATURE_SERVICE,
MON_TABLE_FEATURE_VIEW,
empty_categorical_metric,
empty_numeric_metric,
monitoring_table_meta,
normalize_monitoring_row,
opt_float,
)
from feast.on_demand_feature_view import OnDemandFeatureView
from feast.repo_config import FeastConfigBaseModel, RepoConfig
from feast.saved_dataset import SavedDatasetStorage
Expand Down Expand Up @@ -560,27 +570,6 @@ def clear_monitoring_baseline(
# BigQuery monitoring metrics (native)
# ------------------------------------------------------------------ #

_BQ_MON_FEATURE_TABLE = "feast_monitoring_feature_metrics"
_BQ_MON_VIEW_TABLE = "feast_monitoring_feature_view_metrics"
_BQ_MON_SERVICE_TABLE = "feast_monitoring_feature_service_metrics"

_BQ_EMPTY_METRIC_TEMPLATE: Dict[str, Any] = {
"feature_type": "numeric",
"row_count": 0,
"null_count": 0,
"null_rate": 0.0,
"mean": None,
"stddev": None,
"min_val": None,
"max_val": None,
"p50": None,
"p75": None,
"p90": None,
"p95": None,
"p99": None,
"histogram": None,
}


def _bq_monitoring_table_fqn(config: RepoConfig, table_name: str) -> str:
assert isinstance(config.offline_store, BigQueryOfflineStoreConfig)
Expand All @@ -594,99 +583,6 @@ def _bq_monitoring_table_fqn(config: RepoConfig, table_name: str) -> str:
return f"`{project_id}.{config.offline_store.dataset}.{table_name}`"


def _bq_opt_float(val: Any) -> Optional[float]:
return float(val) if val is not None else None


def _bq_mon_table_meta(metric_type: str) -> Tuple[str, List[str], List[str]]:
if metric_type == "feature":
return (
_BQ_MON_FEATURE_TABLE,
[
"project_id",
"feature_view_name",
"feature_name",
"metric_date",
"granularity",
"data_source_type",
"computed_at",
"is_baseline",
"feature_type",
"row_count",
"null_count",
"null_rate",
"mean",
"stddev",
"min_val",
"max_val",
"p50",
"p75",
"p90",
"p95",
"p99",
"histogram",
],
[
"project_id",
"feature_view_name",
"feature_name",
"metric_date",
"granularity",
"data_source_type",
],
)
if metric_type == "feature_view":
return (
_BQ_MON_VIEW_TABLE,
[
"project_id",
"feature_view_name",
"metric_date",
"granularity",
"data_source_type",
"computed_at",
"is_baseline",
"total_row_count",
"total_features",
"features_with_nulls",
"avg_null_rate",
"max_null_rate",
],
[
"project_id",
"feature_view_name",
"metric_date",
"granularity",
"data_source_type",
],
)
if metric_type == "feature_service":
return (
_BQ_MON_SERVICE_TABLE,
[
"project_id",
"feature_service_name",
"metric_date",
"granularity",
"data_source_type",
"computed_at",
"is_baseline",
"total_feature_views",
"total_features",
"avg_null_rate",
"max_null_rate",
],
[
"project_id",
"feature_service_name",
"metric_date",
"granularity",
"data_source_type",
],
)
raise ValueError(f"Unknown metric_type '{metric_type}'")


def _bq_scalar_param_type(column: str) -> str:
if column == "is_baseline":
return "BOOL"
Expand Down Expand Up @@ -767,7 +663,7 @@ def _bq_save_monitoring_metrics(
metric_type: str,
metrics: List[Dict[str, Any]],
) -> None:
table_short, columns, pk_columns = _bq_mon_table_meta(metric_type)
table_short, columns, pk_columns = monitoring_table_meta(metric_type)
table_fqn = _bq_monitoring_table_fqn(config, table_short)
for row in metrics:
_bq_merge_row(config, table_fqn, columns, pk_columns, row)
Expand All @@ -781,7 +677,7 @@ def _bq_query_monitoring_metrics(
start_date: Optional[date] = None,
end_date: Optional[date] = None,
) -> List[Dict[str, Any]]:
table_short, columns, _ = _bq_mon_table_meta(metric_type)
table_short, columns, _ = monitoring_table_meta(metric_type)
table_fqn = _bq_monitoring_table_fqn(config, table_short)
project_id = (
config.offline_store.billing_project_id or config.offline_store.project_id
Expand Down Expand Up @@ -820,13 +716,7 @@ def _bq_query_monitoring_metrics(
results: List[Dict[str, Any]] = []
for r in job:
record = {columns[i]: r[i] for i in range(len(columns))}
if "histogram" in record and isinstance(record["histogram"], str):
record["histogram"] = json.loads(record["histogram"])
if "metric_date" in record and isinstance(record["metric_date"], date):
record["metric_date"] = record["metric_date"].isoformat()
if "computed_at" in record and isinstance(record["computed_at"], datetime):
record["computed_at"] = record["computed_at"].isoformat()
results.append(record)
results.append(normalize_monitoring_row(record))
return results


Expand All @@ -837,7 +727,7 @@ def _bq_clear_monitoring_baseline(
feature_name: Optional[str] = None,
data_source_type: Optional[str] = None,
) -> None:
table_fqn = _bq_monitoring_table_fqn(config, _BQ_MON_FEATURE_TABLE)
table_fqn = _bq_monitoring_table_fqn(config, MON_TABLE_FEATURE)
project_id = (
config.offline_store.billing_project_id or config.offline_store.project_id
)
Expand Down Expand Up @@ -884,7 +774,7 @@ def _bq_ensure_monitoring_tables(config: RepoConfig) -> None:
ds = config.offline_store.dataset
proj = config.offline_store.project_id or client.project
feature_ddl = f"""
CREATE TABLE IF NOT EXISTS `{proj}.{ds}.{_BQ_MON_FEATURE_TABLE}` (
CREATE TABLE IF NOT EXISTS `{proj}.{ds}.{MON_TABLE_FEATURE}` (
project_id STRING NOT NULL,
feature_view_name STRING NOT NULL,
feature_name STRING NOT NULL,
Expand All @@ -911,7 +801,7 @@ def _bq_ensure_monitoring_tables(config: RepoConfig) -> None:
PRIMARY KEY (project_id, feature_view_name, feature_name, metric_date, granularity, data_source_type) NOT ENFORCED
"""
view_ddl = f"""
CREATE TABLE IF NOT EXISTS `{proj}.{ds}.{_BQ_MON_VIEW_TABLE}` (
CREATE TABLE IF NOT EXISTS `{proj}.{ds}.{MON_TABLE_FEATURE_VIEW}` (
project_id STRING NOT NULL,
feature_view_name STRING NOT NULL,
metric_date DATE NOT NULL,
Expand All @@ -928,7 +818,7 @@ def _bq_ensure_monitoring_tables(config: RepoConfig) -> None:
PRIMARY KEY (project_id, feature_view_name, metric_date, granularity, data_source_type) NOT ENFORCED
"""
service_ddl = f"""
CREATE TABLE IF NOT EXISTS `{proj}.{ds}.{_BQ_MON_SERVICE_TABLE}` (
CREATE TABLE IF NOT EXISTS `{proj}.{ds}.{MON_TABLE_FEATURE_SERVICE}` (
project_id STRING NOT NULL,
feature_service_name STRING NOT NULL,
metric_date DATE NOT NULL,
Expand Down Expand Up @@ -1075,31 +965,31 @@ def _bq_numeric_stats(
job.result()
rows = list(job)
if not rows:
return [{**_BQ_EMPTY_METRIC_TEMPLATE, "feature_name": n} for n in feature_names]
return [empty_numeric_metric(n) for n in feature_names]
row = rows[0]
row_count = row["_row_count"] or 0
results: List[Dict[str, Any]] = []
for i, col in enumerate(feature_names):
base = f"c{i}_"
non_null = row[f"{base}nn"] or 0
null_count = int(row_count) - int(non_null)
min_v = _bq_opt_float(row[f"{base}min"])
max_v = _bq_opt_float(row[f"{base}max"])
min_v = opt_float(row[f"{base}min"])
max_v = opt_float(row[f"{base}max"])
result: Dict[str, Any] = {
"feature_name": col,
"feature_type": "numeric",
"row_count": int(row_count),
"null_count": null_count,
"null_rate": null_count / row_count if row_count > 0 else 0.0,
"mean": _bq_opt_float(row[f"{base}avg"]),
"stddev": _bq_opt_float(row[f"{base}stddev"]),
"mean": opt_float(row[f"{base}avg"]),
"stddev": opt_float(row[f"{base}stddev"]),
"min_val": min_v,
"max_val": max_v,
"p50": _bq_opt_float(row[f"{base}p50"]),
"p75": _bq_opt_float(row[f"{base}p75"]),
"p90": _bq_opt_float(row[f"{base}p90"]),
"p95": _bq_opt_float(row[f"{base}p95"]),
"p99": _bq_opt_float(row[f"{base}p99"]),
"p50": opt_float(row[f"{base}p50"]),
"p75": opt_float(row[f"{base}p75"]),
"p90": opt_float(row[f"{base}p90"]),
"p95": opt_float(row[f"{base}p95"]),
"p99": opt_float(row[f"{base}p99"]),
"histogram": None,
}
if min_v is not None and max_v is not None and non_null and int(non_null) > 0:
Expand Down Expand Up @@ -1151,11 +1041,7 @@ def _bq_categorical_stats(
job.result()
rows = list(job)
if not rows:
return {
**_BQ_EMPTY_METRIC_TEMPLATE,
"feature_name": col_name,
"feature_type": "categorical",
}
return empty_categorical_metric(col_name)
row_count = rows[0]["row_count"]
null_count = rows[0]["null_count"]
unique_count = rows[0]["unique_count"]
Expand Down
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