feat(feature_flags): add modulo range condition for segmented experimentation support

aws_lambda_powertools/utilities/feature_flags/time_conditions.py → aws_lambda_powertools/utilities/feature_flags/comparators.py

@@ -1,9 +1,9 @@
 from datetime import datetime, tzinfo
-from typing import Dict, Optional
+from typing import Any, Dict, Optional
 
 from dateutil.tz import gettz
 
-from .schema import HOUR_MIN_SEPARATOR, TimeValues
+from .schema import HOUR_MIN_SEPARATOR, ModuloRangeValues, TimeValues
 
 
 def _get_now_from_timezone(timezone: Optional[tzinfo]) -> datetime:
@@ -16,23 +16,23 @@ def _get_now_from_timezone(timezone: Optional[tzinfo]) -> datetime:
     return datetime.now(timezone)
 
 
-def compare_days_of_week(action: str, values: Dict) -> bool:
-    timezone_name = values.get(TimeValues.TIMEZONE.value, "UTC")
+def compare_days_of_week(context_value: Any, condition_value: Dict) -> bool:
+    timezone_name = condition_value.get(TimeValues.TIMEZONE.value, "UTC")
 
     # %A = Weekday as locale’s full name.
     current_day = _get_now_from_timezone(gettz(timezone_name)).strftime("%A").upper()
 
-    days = values.get(TimeValues.DAYS.value, [])
+    days = condition_value.get(TimeValues.DAYS.value, [])
     return current_day in days
 
 
-def compare_datetime_range(action: str, values: Dict) -> bool:
-    timezone_name = values.get(TimeValues.TIMEZONE.value, "UTC")
+def compare_datetime_range(context_value: Any, condition_value: Dict) -> bool:
+    timezone_name = condition_value.get(TimeValues.TIMEZONE.value, "UTC")
     timezone = gettz(timezone_name)
     current_time: datetime = _get_now_from_timezone(timezone)
 
-    start_date_str = values.get(TimeValues.START.value, "")
-    end_date_str = values.get(TimeValues.END.value, "")
+    start_date_str = condition_value.get(TimeValues.START.value, "")
+    end_date_str = condition_value.get(TimeValues.END.value, "")
 
     # Since start_date and end_date doesn't include timezone information, we mark the timestamp
     # with the same timezone as the current_time. This way all the 3 timestamps will be on
@@ -42,12 +42,12 @@ def compare_datetime_range(action: str, values: Dict) -> bool:
     return start_date <= current_time <= end_date
 
 
-def compare_time_range(action: str, values: Dict) -> bool:
-    timezone_name = values.get(TimeValues.TIMEZONE.value, "UTC")
+def compare_time_range(context_value: Any, condition_value: Dict) -> bool:
+    timezone_name = condition_value.get(TimeValues.TIMEZONE.value, "UTC")
     current_time: datetime = _get_now_from_timezone(gettz(timezone_name))
 
-    start_hour, start_min = values.get(TimeValues.START.value, "").split(HOUR_MIN_SEPARATOR)
-    end_hour, end_min = values.get(TimeValues.END.value, "").split(HOUR_MIN_SEPARATOR)
+    start_hour, start_min = condition_value.get(TimeValues.START.value, "").split(HOUR_MIN_SEPARATOR)
+    end_hour, end_min = condition_value.get(TimeValues.END.value, "").split(HOUR_MIN_SEPARATOR)
 
     start_time = current_time.replace(hour=int(start_hour), minute=int(start_min))
     end_time = current_time.replace(hour=int(end_hour), minute=int(end_min))
@@ -71,3 +71,14 @@ def compare_time_range(action: str, values: Dict) -> bool:
     else:
         # In normal circumstances, we need to assert **both** conditions
         return start_time <= current_time <= end_time
+
+
+def compare_modulo_range(context_value: int, condition_value: Dict) -> bool:
+    """
+    Returns for a given context 'a' and modulo condition 'b' -> b.start <= a % b.base <= b.end
+    """
+    base = condition_value.get(ModuloRangeValues.BASE.value, 1)
+    start = condition_value.get(ModuloRangeValues.START.value, 1)
+    end = condition_value.get(ModuloRangeValues.END.value, 1)
+
+    return start <= context_value % base <= end

aws_lambda_powertools/utilities/feature_flags/feature_flags.py

@@ -5,12 +5,13 @@
 from ...shared.types import JSONType
 from . import schema
 from .base import StoreProvider
-from .exceptions import ConfigurationStoreError
-from .time_conditions import (
+from .comparators import (
     compare_datetime_range,
     compare_days_of_week,
+    compare_modulo_range,
     compare_time_range,
 )
+from .exceptions import ConfigurationStoreError
 
 
 class FeatureFlags:
@@ -65,6 +66,7 @@ def _match_by_action(self, action: str, condition_value: Any, context_value: Any
             schema.RuleAction.SCHEDULE_BETWEEN_TIME_RANGE.value: lambda a, b: compare_time_range(a, b),
             schema.RuleAction.SCHEDULE_BETWEEN_DATETIME_RANGE.value: lambda a, b: compare_datetime_range(a, b),
             schema.RuleAction.SCHEDULE_BETWEEN_DAYS_OF_WEEK.value: lambda a, b: compare_days_of_week(a, b),
+            schema.RuleAction.MODULO_RANGE.value: lambda a, b: compare_modulo_range(a, b),
         }
 
         try:

aws_lambda_powertools/utilities/feature_flags/schema.py

@@ -41,6 +41,7 @@ class RuleAction(str, Enum):
     SCHEDULE_BETWEEN_TIME_RANGE = "SCHEDULE_BETWEEN_TIME_RANGE"  # hour:min 24 hours clock
     SCHEDULE_BETWEEN_DATETIME_RANGE = "SCHEDULE_BETWEEN_DATETIME_RANGE"  # full datetime format, excluding timezone
     SCHEDULE_BETWEEN_DAYS_OF_WEEK = "SCHEDULE_BETWEEN_DAYS_OF_WEEK"  # MONDAY, TUESDAY, .... see TimeValues enum
+    MODULO_RANGE = "MODULO_RANGE"
 
 
 class TimeKeys(Enum):
@@ -71,6 +72,16 @@ class TimeValues(Enum):
     SATURDAY = "SATURDAY"
 
 
+class ModuloRangeValues(Enum):
+    """
+    Possible values when using modulo range rule
+    """
+
+    BASE = "BASE"
+    START = "START"
+    END = "END"
+
+
 class SchemaValidator(BaseValidator):
     """Validates feature flag schema configuration
 
@@ -341,6 +352,9 @@ def validate_condition_value(condition: Dict[str, Any], rule_name: str):
             )
         elif action == RuleAction.SCHEDULE_BETWEEN_DAYS_OF_WEEK.value:
             ConditionsValidator._validate_schedule_between_days_of_week(value, rule_name)
+        # modulo range condition needs validation on base, start, and end attributes
+        elif action == RuleAction.MODULO_RANGE.value:
+            ConditionsValidator._validate_modulo_range(value, rule_name)
 
     @staticmethod
     def _validate_datetime_value(datetime_str: str, rule_name: str):
@@ -434,3 +448,22 @@ def _validate_schedule_between_time_and_datetime_ranges(
         # try to see if the timezone string corresponds to any known timezone
         if not tz.gettz(timezone):
             raise SchemaValidationError(f"'TIMEZONE' value must represent a valid IANA timezone, rule={rule_name}")
+
+    @staticmethod
+    def _validate_modulo_range(value: Any, rule_name: str):
+        error_str = f"condition with a 'MODULO_RANGE' action must have a condition value type dictionary with 'BASE', 'START' and 'END' keys, rule={rule_name}"  # noqa: E501
+        if not isinstance(value, dict):
+            raise SchemaValidationError(error_str)
+
+        base = value.get(ModuloRangeValues.BASE.value)
+        start = value.get(ModuloRangeValues.START.value)
+        end = value.get(ModuloRangeValues.END.value)
+        if base is None or start is None or end is None:
+            raise SchemaValidationError(error_str)
+        if not isinstance(base, int) or not isinstance(start, int) or not isinstance(end, int):
+            raise SchemaValidationError(f"'BASE', 'START' and 'END' must be integers, rule={rule_name}")
+
+        if not 0 <= start <= end <= base - 1:
+            raise SchemaValidationError(
+                f"condition with 'MODULO_RANGE' action must satisfy 0 <= START <= END <= BASE-1, rule={rule_name}"
+            )

docs/utilities/feature_flags.md

@@ -262,6 +262,54 @@ You can also have features enabled only at specific days, for example: enable ch
     When using `SCHEDULE_BETWEEN_DATETIME_RANGE`, use timestamps without timezone information, and
     specify the timezone manually. This way, you'll avoid hitting problems with day light savings.
 
+### Modulo Range Segmented Experimentation
+
+Feature flags can also be used to run experiments on a segment of users based on modulo range conditions on context variables.
+This allows you to have features that are only enabled for a certain segment of users, comparing across multiple variants
+of the same experiment.
+
+Use cases:
+
+* Enable an experiment for a percentage of users
+* Scale up an experiment incrementally in production - canary release
+* Run multiple experiments or variants simultaneously by assigning a spectrum segment to each experiment variant.
+
+The modulo range condition takes three values - `BASE`, `START` and `END`.
+
+The condition evaluates `START <= CONTEXT_VALUE % BASE <= END`.
+
+=== "modulo_range_feature.py"
+
+    ```python hl_lines="1 6 38"
+    --8<-- "examples/feature_flags/src/modulo_range_feature.py"
+    ```
+
+=== "modulo_range_feature_event.json"
+
+    ```json hl_lines="2"
+    --8<-- "examples/feature_flags/src/modulo_range_feature_event.json"
+    ```
+
+=== "modulo_range_features.json"
+
+    ```json hl_lines="13-21"
+    --8<-- "examples/feature_flags/src/modulo_range_features.json"
+    ```
+
+You can run multiple experiments on your users with the spectrum of your choice.
+
+=== "modulo_range_multiple_feature.py"
+
+    ```python hl_lines="1 6 67"
+    --8<-- "examples/feature_flags/src/modulo_range_multiple_feature.py"
+    ```
+
+=== "modulo_range_multiple_features.json"
+
+    ```json hl_lines="9-16 23-31 37-45"
+    --8<-- "examples/feature_flags/src/modulo_range_multiple_features.json"
+    ```
+
 ### Beyond boolean feature flags
 
 ???+ info "When is this useful?"
@@ -385,9 +433,10 @@ The `action` configuration can have the following values, where the expressions
 | **KEY_NOT_IN_VALUE**                | `lambda a, b: a not in b`                                |
 | **VALUE_IN_KEY**                    | `lambda a, b: b in a`                                    |
 | **VALUE_NOT_IN_KEY**                | `lambda a, b: b not in a`                                |
-| **SCHEDULE_BETWEEN_TIME_RANGE**     | `lambda a, b: time(a).start <= b <= time(a).end`         |
-| **SCHEDULE_BETWEEN_DATETIME_RANGE** | `lambda a, b: datetime(a).start <= b <= datetime(b).end` |
+| **SCHEDULE_BETWEEN_TIME_RANGE**     | `lambda a, b: b.start <= time(a) <= b.end`        |
+| **SCHEDULE_BETWEEN_DATETIME_RANGE** | `lambda a, b: b.start <= datetime(a) <= b.end` |
 | **SCHEDULE_BETWEEN_DAYS_OF_WEEK**   | `lambda a, b: day_of_week(a) in b`                       |
+| **MODULO_RANGE**                    | `lambda a, b: b.start <= a % b.base <= b.end`            |
 
 ???+ info
     The `key` and `value` will be compared to the input from the `context` parameter.

examples/feature_flags/src/modulo_range_feature.py

@@ -0,0 +1,44 @@
+from aws_lambda_powertools.utilities.feature_flags import AppConfigStore, FeatureFlags
+from aws_lambda_powertools.utilities.typing import LambdaContext
+
+app_config = AppConfigStore(environment="dev", application="product-catalogue", name="features")
+
+feature_flags = FeatureFlags(store=app_config)
+
+
+def lambda_handler(event: dict, context: LambdaContext):
+    """
+    This feature flag is enabled under the following conditions:
+    - The request payload contains a field 'tier' with the value 'standard'.
+    - If the user_id belongs to the spectrum 0-19 modulo 100, (20% users) on whom we want to run the sale experiment.
+
+    Rule condition to be evaluated:
+        "conditions": [
+          {
+            "action": "EQUALS",
+            "key": "tier",
+            "value": "standard"
+          },
+          {
+            "action": "MODULO_RANGE",
+            "key": "user_id",
+            "value": {
+              "BASE": 100,
+              "START": 0,
+              "END": 19
+            }
+          }
+        ]
+    """
+
+    # Get customer's tier and identifier from incoming request
+    ctx = {"tier": event.get("tier", "standard"), "user_id": event.get("user_id", 0)}
+
+    # Checking if the sale_experiment is enable
+    sale_experiment = feature_flags.evaluate(name="sale_experiment", default=False, context=ctx)
+
+    if sale_experiment:
+        # Enable special discount for sale experiment segment users:
+        return {"message": "The sale experiment is enabled."}
+
+    return {"message": "The sale experiment is not enabled."}

examples/feature_flags/src/modulo_range_feature_event.json

@@ -0,0 +1,5 @@
+{
+  "user_id": 134532511,
+  "tier": "standard",
+  "basked_id": "random_id"
+}

examples/feature_flags/src/modulo_range_features.json

@@ -0,0 +1,26 @@
+{
+  "sale_experiment": {
+    "default": false,
+    "rules": {
+      "experiment 1 segment - 20% users": {
+        "when_match": true,
+        "conditions": [
+          {
+            "action": "EQUALS",
+            "key": "tier",
+            "value": "standard"
+          },
+          {
+            "action": "MODULO_RANGE",
+            "key": "user_id",
+            "value": {
+              "BASE": 100,
+              "START": 0,
+              "END": 19
+            }
+          }
+        ]
+      }
+    }
+  }
+}

examples/feature_flags/src/modulo_range_multiple_feature.py

@@ -0,0 +1,69 @@
+from aws_lambda_powertools.utilities.feature_flags import AppConfigStore, FeatureFlags
+from aws_lambda_powertools.utilities.typing import LambdaContext
+
+app_config = AppConfigStore(environment="dev", application="product-catalogue", name="features")
+
+feature_flags = FeatureFlags(store=app_config)
+
+
+def lambda_handler(event: dict, context: LambdaContext):
+    """
+    This non-boolean feature flag returns the percentage discount depending on the sale experiment segment:
+    - 10% standard discount if the user_id belongs to the spectrum 0-3 modulo 10, (40% users).
+    - 15% experiment discount if the user_id belongs to the spectrum 4-6 modulo 10, (30% users).
+    - 18% experiment discount if the user_id belongs to the spectrum 7-9 modulo 10, (30% users).
+
+    Rule conditions to be evaluated:
+    "rules": {
+      "control group - standard 10% discount segment": {
+        "when_match": 10,
+        "conditions": [
+          {
+            "action": "MODULO_RANGE",
+            "key": "user_id",
+            "value": {
+              "BASE": 10,
+              "START": 0,
+              "END": 3
+            }
+          }
+        ]
+      },
+      "test experiment 1 - 15% discount segment": {
+        "when_match": 15,
+        "conditions": [
+          {
+            "action": "MODULO_RANGE",
+            "key": "user_id",
+            "value": {
+              "BASE": 10,
+              "START": 4,
+              "END": 6
+            }
+          }
+        ]
+      },
+      "test experiment 2 - 18% discount segment": {
+        "when_match": 18,
+        "conditions": [
+          {
+            "action": "MODULO_RANGE",
+            "key": "user_id",
+            "value": {
+              "BASE": 10,
+              "START": 7,
+              "END": 9
+            }
+          }
+        ]
+      }
+    }
+    """
+
+    # Get customer's tier and identifier from incoming request
+    ctx = {"tier": event.get("tier", "standard"), "user_id": event.get("user_id", 0)}
+
+    # Get sale discount percentage from feature flag.
+    sale_experiment_discount = feature_flags.evaluate(name="sale_experiment_discount", default=0, context=ctx)
+
+    return {"message": f" {sale_experiment_discount}% discount applied."}