Learn about the stateless nature of Log Search Alerts in Azure

In Azure Monitor, Log Search Alerts are designed to be stateless, meaning that they do not retain any historical context or state between alert evaluations. Each time the query runs, it operates independently, evaluating the current log data without knowledge of previous executions. This stateless nature impacts how the alerts behave and how you configure them.

Key Characteristics of Stateless Log Search Alerts

No Historical Context

Every time a log search query runs as part of the alert evaluation, it starts fresh, considering only the log data within the specified time window (e.g., the last 5 minutes, 1 hour).

The query does not remember previous results or conditions. It only evaluates the current state of the logs for the given time period. For example, if you're monitoring failed login attempts, the alert will only look at failed logins during the time frame it is evaluating, without considering past failures.

This means if an event occurred and no longer falls within the query's window when the alert runs, it will not be detected or trigger an alert.

Independent Query Evaluation

Log search alerts work by evaluating the log query at a set frequency (e.g., every 5 minutes). Each evaluation is independent of the last, with no memory of whether an alert was triggered previously or whether any logs were returned before.

The alerting condition is only evaluated on the query results from that specific time window, and not based on cumulative results from past evaluations.

Triggering Conditions Are Self-contained

The conditions set for an alert (e.g., number of occurrences of a particular event) are defined within the query itself and for the specified time window. These conditions do not take into account previous states. For example, if you want to be alerted when there are more than 10 failed logins within 10 minutes, the system checks if that specific condition is true for the current 10-minute window only.

If the condition was met in a previous evaluation but no longer meets the criteria in the current window, the alert will not trigger, regardless of the prior state.

Impact on State-Dependent Scenarios

Statelessness makes log search alerts simple and efficient in most cases, as they don’t have to track long-term history or complex states. However, it can be a limitation in certain scenarios where tracking events over time is important.

For example, if you want to track an issue that is based on the accumulation of events over time (such as a series of failed logins across multiple evaluation periods), a stateless approach will not help. In such cases, you would need to either adjust the query to aggregate results over a longer time window or use a different solution, like metric alerts or external tracking tools that can persist state.

No Alert Dedupe or Tracking:

Statelessness also means that if an alert is triggered multiple times for the same issue (e.g., repeated failed login attempts over several evaluation windows), there is no deduplication mechanism built into the log search alert itself. If the same alert condition is met multiple times across several query evaluations, Azure Monitor will trigger the alert each time the condition is met, even if the issue has already been acknowledged or resolved in previous evaluations.

Example of Statelessness

Imagine you have a query that searches for failed login attempts in the past 10 minutes:

xxxxxxxxxx
5
1
SecurityEvent
2
| where EventID == 4625
3
| where TimeGenerated > ago(10m)
4
| summarize count() by bin(TimeGenerated, 10m)
5
| where count_ > 5

1. Scenario 1: If, in the past 10 minutes, there were 6 failed login attempts, the alert will trigger.

2. Scenario 2: If, in the next 10-minute evaluation window, the failed login attempts drop to 4, the alert will not trigger.

3. Statelessness: Even though the system evaluated the query for failed logins previously, it will not "remember" that there were 6 failed logins earlier. The alert will only trigger based on the results from the most recent 10-minute window, not taking into account what happened earlier.

Why Does Statelessness Matter?

1. Simplicity: Stateless alerts are generally easier to configure and manage because they do not require complex logic to track and store historical states.

2. Efficiency: Stateless alerts can be more efficient in terms of computation and resources since each evaluation is self-contained.

3. Limitations in Long-Term Monitoring: While stateless alerts work well for conditions that are based on a fixed window of time (e.g., "more than 5 failed logins in 10 minutes"), they are not suitable for use cases where you need to track cumulative events over longer periods (e.g., "trigger an alert if there are 100 failed logins in the past 24 hours").

How to Overcome Statelessness in Certain Scenarios

1. Use Aggregation in Queries: You can aggregate the results within a query to account for conditions that span longer periods, such as using summarize or bin to group events over time.

2. External Systems: For more advanced tracking, you can use external systems to store and track states (e.g., using Azure Logic Apps or external monitoring tools that can maintain a stateful perspective over time).

3. Metric Alerts: For certain use cases (e.g., continuous monitoring of performance metrics like CPU utilization or memory), metric alerts might be a better fit since they are designed to handle stateful conditions (like checking for sustained thresholds over time).

Summary

The stateless nature of log search alerts in Azure Monitor makes them straightforward and efficient for monitoring specific conditions based on the latest log data. However, if your use case requires tracking cumulative events or state over time, you may need to look for alternative solutions or design your log queries and alerting strategies accordingly.