> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fiddler.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# AlertRecord

> Alert record representing a triggered alert instance.

Alert record representing a triggered alert instance.

An AlertRecord captures the details of a specific alert trigger event, including
the metric values, thresholds, and context that caused an AlertRule to fire.
Alert records provide essential data for monitoring analysis and troubleshooting.

## Example

```python theme={null}
# List recent critical alerts
critical_alerts = [
    record for record in AlertRecord.list(
        alert_rule_id=drift_alert.id,
        start_time=datetime.now() - timedelta(days=3)
    )
    if record.severity == "CRITICAL"
]

# Analyze alert details
for alert in critical_alerts:

    print(f"Alert triggered at {alert.created_at}")
    print(f"Metric value: {alert.alert_value:.3f}")
    print(f"Critical threshold: {alert.critical_threshold:.3f}")
    if alert.feature_name:

        print(f"Feature: {alert.feature_name}")

        print(f"Message: {alert.message}")
        print("—")

        # Check for alert patterns
        hourly_alerts = {}
        for alert in AlertRecord.list(alert_rule_id=perf_alert.id):

            hour = alert.created_at.hour
            hourly_alerts[hour] = hourly_alerts.get(hour, 0) + 1

            print("Alerts by hour:", hourly_alerts)
```

<Info>
  Alert records are read-only entities created automatically by the Fiddler
  platform when AlertRules trigger. They cannot be created or modified directly
  but provide valuable historical data for analysis and debugging.
</Info>

Initialize an AlertRecord instance.

Creates an alert record object for representing triggered alert instances.
Alert records are typically created automatically by the Fiddler platform
when AlertRules trigger, rather than being instantiated directly by users.

<Info>
  Alert records are read-only entities that capture historical alert
  trigger events. They are created automatically by the system and
  cannot be modified after creation.
</Info>

## *classmethod* list()

List alert records triggered by a specific alert rule.

Retrieves historical alert records for analysis and troubleshooting. This method
provides access to all alert trigger events within a specified time range,
enabling pattern analysis and threshold tuning.

### Parameters

<ParamField path="alert_rule_id" type="UUID | str" required={true}>
  The unique identifier of the AlertRule to retrieve records for.
  Must be a valid alert rule UUID.
</ParamField>

<ParamField path="start_time" type="datetime | None" required={false} default="7 days ago">
  Start time for filtering alert records. If None, defaults to
  7 days ago. Used to define the beginning of the query window.
</ParamField>

<ParamField path="end_time" type="datetime | None" required={false} default="current time">
  End time for filtering alert records. If None, defaults to
  current time. Used to define the end of the query window.
</ParamField>

<ParamField path="ordering" type="list[str] | None" required={false} default="None">
  List of field names for result ordering. Prefix with "-" for
  descending order (e.g., \["-created\_at"] for newest first).
</ParamField>

### Yields

`AlertRecord` – Alert record instances with complete
trigger details and context information.

### Returns

`Iterator[AlertRecord]`

### Example

```python theme={null}
# Get recent alerts for analysis
recent_alerts = list(AlertRecord.list(
    alert_rule_id=drift_alert.id,
    start_time=datetime.now() - timedelta(days=3),
    ordering=["-created_at"]  # Newest first
))

# Analyze alert frequency
print(f"Total alerts in last 3 days: {len(recent_alerts)}")
critical_count = sum(1 for a in recent_alerts if a.severity == "CRITICAL")
print(f"Critical alerts: {critical_count}")

# Check alert patterns by feature
feature_alerts = {}
for alert in recent_alerts:

    if alert.feature_name:
        feature_alerts[alert.feature_name] = feature_alerts.get(alert.feature_name, 0) + 1

        print("Alerts by feature:", feature_alerts)

        # Analyze threshold violations
        for alert in recent_alerts[:5]:  # Latest 5 alerts

        violation_ratio = alert.alert_value / alert.critical_threshold
        print(f"Alert value: {alert.alert_value:.3f} "

        f"({violation_ratio:.1%} of threshold)")
```

<Info>
  Results are paginated automatically. The default time range is 7 days
  to balance performance with useful historical context. Use ordering
  parameters to get the most relevant results first.
</Info>