Update monitor
The access token received from the authorization server in the OAuth 2.0 flow.
Configuration for a monitoring rule. Monitors can be configured to:
APL (Axiom Processing Language) query string used for monitoring. This query defines what data to analyze and how to process it.
"| where severity = 'error' | count() > 100"
Name of the monitor
"Production CPU Monitor"
Type of monitoring check to perform:
Threshold, MatchEvent, AnomalyDetection "Threshold"
Whether to alert when no data is received
true
Name of the column to monitor
"cpu_usage"
Number of days to compare for anomaly detection
x <= 77
Timestamp when the monitor was created
"2024-03-20T10:00:00Z"
ID of the user who created the monitor
"usr_789xyz"
Detailed description of the monitor's purpose
"Monitors CPU usage and alerts when it exceeds 90%"
Whether the monitor is currently disabled
false
Timestamp until when the monitor should remain disabled
"2024-04-01T00:00:00Z"
How frequently the monitor should run, in minutes. Minimum value is 1 minute.
x >= 15
List of notifier IDs that will receive alerts. Notifiers can be email, Slack, webhook endpoints, etc.
["notify_slack_prod", "notify_email_oncall"]Whether to group notifications
false
Whether to send notifications on every check
false
Comparison operator for threshold checks:
Below, BelowOrEqual, Above, AboveOrEqual, AboveOrBelow "Above"
Time window to evaluate in each check, in minutes. For example, "last 5 minutes of data"
x >= 15
Whether the alert can be manually resolved
true
Delay in seconds before triggering the alert
x <= 86400300
Whether to skip resolved alerts
false
Threshold value for triggering the alert
90
Tolerance percentage for anomaly detection
x <= 10010
Number of positive results needed before triggering
2
Number of consecutive check runs that must fail before triggering an alert. Use this to avoid alerting on temporary spikes.
3
Monitor
Monitor configuration with its unique identifier
APL (Axiom Processing Language) query string used for monitoring. This query defines what data to analyze and how to process it.
"| where severity = 'error' | count() > 100"
Name of the monitor
"Production CPU Monitor"
Type of monitoring check to perform:
Threshold, MatchEvent, AnomalyDetection "Threshold"
Unique identifier for the monitor
"mon_xyz789"
Whether to alert when no data is received
true
Name of the column to monitor
"cpu_usage"
Number of days to compare for anomaly detection
x <= 77
Timestamp when the monitor was created
"2024-03-20T10:00:00Z"
ID of the user who created the monitor
"usr_789xyz"
Detailed description of the monitor's purpose
"Monitors CPU usage and alerts when it exceeds 90%"
Whether the monitor is currently disabled
false
Timestamp until when the monitor should remain disabled
"2024-04-01T00:00:00Z"
How frequently the monitor should run, in minutes. Minimum value is 1 minute.
x >= 15
List of notifier IDs that will receive alerts. Notifiers can be email, Slack, webhook endpoints, etc.
["notify_slack_prod", "notify_email_oncall"]Whether to group notifications
false
Whether to send notifications on every check
false
Comparison operator for threshold checks:
Below, BelowOrEqual, Above, AboveOrEqual, AboveOrBelow "Above"
Time window to evaluate in each check, in minutes. For example, "last 5 minutes of data"
x >= 15
Whether the alert can be manually resolved
true
Delay in seconds before triggering the alert
x <= 86400300
Whether to skip resolved alerts
false
Threshold value for triggering the alert
90
Tolerance percentage for anomaly detection
x <= 10010
Number of positive results needed before triggering
2
Number of consecutive check runs that must fail before triggering an alert. Use this to avoid alerting on temporary spikes.
3