[codex] refine alert cooldown scope

README.md

@@ -47,9 +47,9 @@ With the bundled sample data, the default run currently produces:
 
 - `41` normalized events
 - `24` windows
-- `12` alerts after applying a `60` second per-rule cooldown
+- `12` alerts after applying a `60` second cooldown
 
-The default config suppresses repeated alerts with the same `rule_name` until `60` seconds have elapsed since that rule's last emitted alert. Different rules can still alert on the same window.
+The default config suppresses repeated alerts by cooldown key. The key is `rule_name` plus an entity scope when the rule input includes `entity`, `source`, `target`, or `host`; otherwise it falls back to `rule_name` alone. Different cooldown keys can still alert on the same window.
 
 ## Outputs
 

src/telemetry_window_demo/rules.py

@@ -14,6 +14,7 @@
     "window_end",
     "message",
 )
+COOLDOWN_SCOPE_COLUMNS = ("entity", "source", "target", "host")
 
 
 def apply_rules(
@@ -46,7 +47,7 @@ def apply_rules(
     if not alerts:
         return pd.DataFrame(columns=ALERT_COLUMNS)
 
-    alerts_frame = pd.DataFrame(alerts, columns=ALERT_COLUMNS)
+    alerts_frame = pd.DataFrame(alerts)
     alerts_frame = alerts_frame.sort_values(["alert_time", "rule_name"]).reset_index(drop=True)
     return _apply_alert_cooldown(alerts_frame, cooldown_seconds)
 
@@ -56,6 +57,7 @@ def _row_alert(
     rule_name: str,
     severity: str,
     message: str,
+    cooldown_scope: str | None = None,
 ) -> dict[str, object]:
     return {
         "alert_time": row["window_end"],
@@ -64,33 +66,66 @@ def _row_alert(
         "window_start": row["window_start"],
         "window_end": row["window_end"],
         "message": message,
+        "cooldown_scope": _resolve_cooldown_scope(row, cooldown_scope),
     }
 
 
+def _resolve_cooldown_scope(
+    row: pd.Series,
+    explicit_scope: str | None = None,
+) -> str | None:
+    if explicit_scope is not None:
+        value = explicit_scope.strip()
+        if value:
+            return value
+
+    for column in COOLDOWN_SCOPE_COLUMNS:
+        if column not in row.index:
+            continue
+
+        value = row[column]
+        if pd.isna(value):
+            continue
+
+        value_text = str(value).strip()
+        if value_text:
+            return f"{column}={value_text}"
+
+    return None
+
+
 def _apply_alert_cooldown(
     alerts: pd.DataFrame,
     cooldown_seconds: int,
 ) -> pd.DataFrame:
     if alerts.empty or cooldown_seconds <= 0:
-        return alerts.reset_index(drop=True)
+        return alerts.loc[:, ALERT_COLUMNS].reset_index(drop=True)
 
-    last_kept_at: dict[str, pd.Timestamp] = {}
+    last_kept_at: dict[tuple[str, str | None], pd.Timestamp] = {}
     kept_rows: list[int] = []
 
     for index, row in alerts.iterrows():
         rule_name = str(row["rule_name"])
         alert_time = pd.Timestamp(row["alert_time"])
-        last_alert_time = last_kept_at.get(rule_name)
+        scope_value = row.get("cooldown_scope")
+        if pd.isna(scope_value):
+            scope = None
+        else:
+            scope_text = str(scope_value).strip()
+            scope = scope_text or None
+
+        cooldown_key = (rule_name, scope)
+        last_alert_time = last_kept_at.get(cooldown_key)
 
         if last_alert_time is None:
             kept_rows.append(index)
-            last_kept_at[rule_name] = alert_time
+            last_kept_at[cooldown_key] = alert_time
             continue
 
         elapsed = (alert_time - last_alert_time).total_seconds()
         if elapsed >= cooldown_seconds:
             kept_rows.append(index)
-            last_kept_at[rule_name] = alert_time
+            last_kept_at[cooldown_key] = alert_time
 
     return alerts.loc[kept_rows, ALERT_COLUMNS].reset_index(drop=True)
 

tests/test_rules.py

@@ -139,6 +139,71 @@ def test_apply_rules_suppresses_repeated_same_rule_within_cooldown() -> None:
     ]
 
 
+def test_apply_rules_scopes_same_rule_cooldown_by_source_when_present() -> None:
+    features = pd.DataFrame(
+        [
+            {
+                "window_start": pd.Timestamp("2026-03-10T10:00:00Z"),
+                "window_end": pd.Timestamp("2026-03-10T10:01:00Z"),
+                "source": "host_a",
+                "event_count": 10,
+                "error_count": 4,
+                "error_rate": 0.40,
+                "unique_sources": 4,
+                "unique_targets": 2,
+                "high_severity_count": 0,
+                "login_fail_count": 0,
+                "malware_alert_count": 0,
+            },
+            {
+                "window_start": pd.Timestamp("2026-03-10T10:00:10Z"),
+                "window_end": pd.Timestamp("2026-03-10T10:01:10Z"),
+                "source": "host_b",
+                "event_count": 11,
+                "error_count": 5,
+                "error_rate": 0.45,
+                "unique_sources": 5,
+                "unique_targets": 2,
+                "high_severity_count": 0,
+                "login_fail_count": 0,
+                "malware_alert_count": 0,
+            },
+            {
+                "window_start": pd.Timestamp("2026-03-10T10:00:20Z"),
+                "window_end": pd.Timestamp("2026-03-10T10:01:20Z"),
+                "source": "host_a",
+                "event_count": 12,
+                "error_count": 6,
+                "error_rate": 0.50,
+                "unique_sources": 6,
+                "unique_targets": 2,
+                "high_severity_count": 0,
+                "login_fail_count": 0,
+                "malware_alert_count": 0,
+            },
+        ]
+    )
+
+    alerts = apply_rules(
+        features,
+        {
+            "cooldown_seconds": 60,
+            "high_error_rate": {"threshold": 0.30, "severity": "medium"},
+            "persistent_high_error": {
+                "threshold": 1.0,
+                "consecutive_windows": 10,
+                "severity": "medium",
+            },
+        },
+    )
+
+    assert list(alerts["rule_name"]) == ["high_error_rate", "high_error_rate"]
+    assert list(alerts["alert_time"]) == [
+        pd.Timestamp("2026-03-10T10:01:00Z"),
+        pd.Timestamp("2026-03-10T10:01:10Z"),
+    ]
+
+
 def test_apply_rules_keeps_different_rules_during_same_cooldown_window() -> None:
     features = pd.DataFrame(
         [