feat: Modified default example with different data types

Signed-off-by: ntkathole <nikhilkathole2683@gmail.com>
feast-dev · ntkathole · Feb 26, 2026 · Feb 16, 2026 · Feb 17, 2026 · Feb 26, 2026
commit 2323181a15a1cb881d091cfb21718a332aa8c400
@@ -136,10 +136,38 @@ def create_driver_hourly_stats_df(drivers, start_date, end_date) -> pd.DataFrame
     df_all_drivers["conv_rate"] = np.random.random(size=rows).astype(np.float32)
     df_all_drivers["acc_rate"] = np.random.random(size=rows).astype(np.float32)
     df_all_drivers["avg_daily_trips"] = np.random.randint(0, 1000, size=rows).astype(
-        np.int32
+        np.int64
     )
     df_all_drivers["created"] = pd.to_datetime(pd.Timestamp.now(tz=None).round("ms"))
 
+    # Complex type columns for Map, Json, and Struct examples
+    import json as _json
+
+    df_all_drivers["driver_metadata"] = [
+        {
+            "vehicle_type": np.random.choice(["sedan", "suv", "truck"]),
+            "rating": str(round(np.random.uniform(3.0, 5.0), 1)),
+        }
+        for _ in range(len(df_all_drivers))
+    ]
+    df_all_drivers["driver_config"] = [
+        _json.dumps(
+            {
+                "max_distance_km": int(np.random.randint(10, 200)),
+                "preferred_zones": list(
+                    np.random.choice(
+                        ["north", "south", "east", "west"], size=2, replace=False
+                    )
+                ),
+            }
+        )
+        for _ in range(len(df_all_drivers))
+    ]
+    df_all_drivers["driver_profile"] = [
+        {"name": f"driver_{driver_id}", "age": str(int(np.random.randint(25, 60)))}
+        for driver_id in df_all_drivers["driver_id"]
+    ]
+
     # Create duplicate rows that should be filtered by created timestamp
     # TODO: These duplicate rows area indirectly being filtered out by the point in time join already. We need to
     #  inject a bad row at a timestamp where we know it will get joined to the entity dataframe, and then test that

@@ -69,6 +69,15 @@ def pa_to_trino_value_type(pa_type_as_str: str) -> str:
     if pa_type_as_str.startswith("decimal"):
         return trino_type.format(pa_type_as_str)
 
+    if pa_type_as_str.startswith("map<"):
+        return trino_type.format("varchar")
+
+    if pa_type_as_str == "large_string":
+        return trino_type.format("varchar")
+
+    if pa_type_as_str.startswith("struct<"):
+        return trino_type.format("varchar")
+
     type_map = {
         "null": "null",
         "bool": "boolean",

@@ -17,7 +17,7 @@
 from feast.feature_logging import LoggingConfig
 from feast.infra.offline_stores.file_source import FileLoggingDestination
 from feast.on_demand_feature_view import on_demand_feature_view
-from feast.types import Float32, Float64, Int64
+from feast.types import Float32, Float64, Int64, Json, Map, String, Struct
 
 # Define a project for the feature repo
 project = Project(name="%PROJECT_NAME%", description="A project for driver statistics")
@@ -52,12 +52,26 @@
         Field(name="conv_rate", dtype=Float32),
         Field(name="acc_rate", dtype=Float32),
         Field(name="avg_daily_trips", dtype=Int64, description="Average daily trips"),
+        Field(
+            name="driver_metadata",
+            dtype=Map,
+            description="Driver metadata as key-value pairs",
+        ),
+        Field(
+            name="driver_config", dtype=Json, description="Driver configuration as JSON"
+        ),
+        Field(
+            name="driver_profile",
+            dtype=Struct({"name": String, "age": String}),
+            description="Driver profile as a typed struct",
+        ),
     ],
     online=True,
     source=driver_stats_source,
     # Tags are user defined key/value pairs that are attached to each
     # feature view
     tags={"team": "driver_performance"},
+    enable_validation=True,
 )
 
 # Define a request data source which encodes features / information only
@@ -119,6 +133,9 @@ def transformed_conv_rate(inputs: pd.DataFrame) -> pd.DataFrame:
         Field(name="conv_rate", dtype=Float32),
         Field(name="acc_rate", dtype=Float32),
         Field(name="avg_daily_trips", dtype=Int64),
+        Field(name="driver_metadata", dtype=Map),
+        Field(name="driver_config", dtype=Json),
+        Field(name="driver_profile", dtype=Struct({"name": String, "age": String})),
     ],
     online=True,
     source=driver_stats_push_source,  # Changed from above

@@ -1,3 +1,4 @@
+import json
 import subprocess
 from datetime import datetime
 
@@ -45,6 +46,11 @@ def run_demo():
             "conv_rate": [1.0],
             "acc_rate": [1.0],
             "avg_daily_trips": [1000],
+            "driver_metadata": [{"vehicle_type": "truck", "rating": "5.0"}],
+            "driver_config": [
+                json.dumps({"max_distance_km": 500, "preferred_zones": ["north"]})
+            ],
+            "driver_profile": [{"name": "driver_1001_updated", "age": "30"}],
         }
     )
     print(event_df)
@@ -115,6 +121,9 @@ def fetch_online_features(store, source: str = ""):
     else:
         features_to_fetch = [
             "driver_hourly_stats:acc_rate",
+            "driver_hourly_stats:driver_metadata",
+            "driver_hourly_stats:driver_config",
+            "driver_hourly_stats:driver_profile",
             "transformed_conv_rate:conv_rate_plus_val1",
             "transformed_conv_rate:conv_rate_plus_val2",
         ]

@@ -1319,6 +1319,12 @@ def pa_to_redshift_value_type(pa_type: "pyarrow.DataType") -> str:
     if pa_type_as_str.startswith("map<"):
         return "super"
 
+    if pa_type_as_str == "large_string":
+        return "super"
+
+    if pa_type_as_str.startswith("struct<"):
+        return "super"
+
     # We have to take into account how arrow types map to parquet types as well.
     # For example, null type maps to int32 in parquet, so we have to use int4 in Redshift.
     # Other mappings have also been adjusted accordingly.
@@ -1636,6 +1642,18 @@ def pa_to_athena_value_type(pa_type: "pyarrow.DataType") -> str:
     if pa_type_as_str.startswith("python_values_to_proto_values"):
         return pa_type_as_str
 
+    if pa_type_as_str.startswith("list"):
+        return "array<string>"
+
+    if pa_type_as_str.startswith("map<"):
+        return "string"
+
+    if pa_type_as_str == "large_string":
+        return "string"
+
+    if pa_type_as_str.startswith("struct<"):
+        return "string"
+
     # We have to take into account how arrow types map to parquet types as well.
     # For example, null type maps to int32 in parquet, so we have to use int4 in Redshift.
     # Other mappings have also been adjusted accordingly.