Update monitor
Update monitor
PUT
/v2/monitors/{id}Features
Send the token in the Authorization: Bearer API_TOKEN header. See Tokens for which token types this endpoint accepts.
Input27
Sent from your browser straight to the API — never to Docsbook, never stored.
(path)
Whether to alert when no data is received
APL (Axiom Processing Language) query string used for monitoring. This query defines what data to analyze and how to process it. At least one of aplQuery or mplQuery must be provided.
Name of the column to monitor
Number of days to compare for anomaly detection
Timestamp when the monitor was created
ID of the user who created the monitor
Detailed description of the monitor's purpose
Whether the monitor is currently disabled
Timestamp until when the monitor should remain disabled
How frequently the monitor should run, in minutes. Minimum value is 1 minute.
MPL (Metrics Processing Language) query string for metrics-based monitoring. Use this as an alternative to aplQuery for metrics datasets. At least one of aplQuery or mplQuery must be provided.
Name of the monitor
List of notifier IDs that will receive alerts. Notifiers can be email, Slack, webhook endpoints, etc.
Whether to group notifications
Whether to send notifications on every check
Comparison operator for threshold checks: - Below: Trigger when value < threshold - BelowOrEqual: Trigger when value <= threshold - Above: Trigger when value > threshold - AboveOrEqual: Trigger when value >= threshold - AboveOrBelow: Trigger when value is outside a range One of:
Below, BelowOrEqual, Above, AboveOrEqual, AboveOrBelow.Time window to evaluate in each check, in minutes. For example, "last 5 minutes of data"
Whether the alert can be manually resolved
Delay in seconds before triggering the alert
Whether to skip resolved alerts
Threshold value for triggering the alert
Tolerance percentage for anomaly detection
Number of positive results needed before triggering
Number of consecutive check runs that must fail before triggering an alert. Use this to avoid alerting on temporary spikes.
Type of monitoring check to perform: - Threshold: Compares a numeric value against a threshold - MatchEvent: Looks for specific events or patterns - AnomalyDetection: Identifies unusual patterns based on historical data One of:
Threshold, MatchEvent, AnomalyDetection.Timestamp when the monitor was last updated
Output27
Whether to alert when no data is received
APL (Axiom Processing Language) query string used for monitoring. This query defines what data to analyze and how to process it. At least one of aplQuery or mplQuery must be provided.
Name of the column to monitor
Number of days to compare for anomaly detection
Timestamp when the monitor was created
ID of the user who created the monitor
Detailed description of the monitor's purpose
Whether the monitor is currently disabled
Timestamp until when the monitor should remain disabled
How frequently the monitor should run, in minutes. Minimum value is 1 minute.
MPL (Metrics Processing Language) query string for metrics-based monitoring. Use this as an alternative to aplQuery for metrics datasets. At least one of aplQuery or mplQuery must be provided.
Name of the monitor
List of notifier IDs that will receive alerts. Notifiers can be email, Slack, webhook endpoints, etc.
Whether to group notifications
Whether to send notifications on every check
Comparison operator for threshold checks: - Below: Trigger when value < threshold - BelowOrEqual: Trigger when value <= threshold - Above: Trigger when value > threshold - AboveOrEqual: Trigger when value >= threshold - AboveOrBelow: Trigger when value is outside a range One of:
Below, BelowOrEqual, Above, AboveOrEqual, AboveOrBelow.Time window to evaluate in each check, in minutes. For example, "last 5 minutes of data"
Whether the alert can be manually resolved
Delay in seconds before triggering the alert
Whether to skip resolved alerts
Threshold value for triggering the alert
Tolerance percentage for anomaly detection
Number of positive results needed before triggering
Number of consecutive check runs that must fail before triggering an alert. Use this to avoid alerting on temporary spikes.
Type of monitoring check to perform: - Threshold: Compares a numeric value against a threshold - MatchEvent: Looks for specific events or patterns - AnomalyDetection: Identifies unusual patterns based on historical data One of:
Threshold, MatchEvent, AnomalyDetection.Timestamp when the monitor was last updated
Unique identifier for the monitor
Responses
200Monitor
Example input
curl -X PUT 'https://api.axiom.co/v2/monitors/{id}' \
-H 'Authorization: Bearer API_TOKEN' \
-H 'Content-Type: application/json' \
-d '{"name":"Production CPU Monitor","type":"<type>"}'Example output
{
"alertOnNoData": true,
"aplQuery": "| where severity = 'error' | count() > 100",
"columnName": "cpu_usage",
"compareDays": 7,
"createdAt": "2024-03-20T10:00:00Z",
"createdBy": "usr_789xyz",
"description": "Monitors CPU usage and alerts when it exceeds 90%",
"disabled": false,
"disabledUntil": "2024-04-01T00:00:00Z",
"intervalMinutes": 5,
"mplQuery": "test-metrics:http_request_duration_seconds",
"name": "Production CPU Monitor"
}