Scenario A: In the last 1 hour, a 50% increase in the failed request count to a specific API Proxy compared to the previous check will be considered an anomaly for us. Scenario B: In the last 1 hour, a 50% increase in the ratio of all requests to failed requests for a specific API Proxy compared to the previous check will be considered an anomaly for us. Scenario C: Based on the last 10 data points, blocked requests are compared with Bollinger bands, and if they are equal to or higher than the upper Bollinger band, this situation is detected as an anomaly and notified. If we think with example values, each scenario will check the situations specified below (Failed/Total ratio is given in parentheses); Scenario A: If 100 requests came in the first check hour and 10 of them were errors (%10), the situation where 15 or more of the 100 requests coming in the next hour are errors (%15). If 100 requests came in the first check hour and 10 of them were errors (%10), the situation where 15 or more of the 200 requests coming in the next hour are errors (%7.5). The point to note is that in this scenario, the error amount has increased, but the error rate has decreased compared to the total request count. If 100 requests came in the first check hour and 10 of them were errors (%10), the situation where 2 or more of the 10 requests coming in the next hour are errors (%20). This situation is not considered an error in this scenario. If 100 requests came in the first check hour and 10 of them were errors (%10), the situation where all of the 5 requests coming in the next hour are errors (%100). This situation is not considered an error in this scenario. Scenario B: If 100 requests came in the first check hour and 10 of them were errors (%10), the situation where 15 or more of the 100 requests coming in the next hour are errors (%15). If 100 requests came in the first check hour and 10 of them were errors (%10), the situation where 30 or more of the 200 requests coming in the next hour are errors (%15). If 100 requests came in the first check hour and 10 of them were errors (%10), the situation where 2 or more of the 10 requests coming in the next hour are errors (%20). The point to note is that in this scenario, the error amount has decreased, but the error rate has increased compared to the total request count. If 100 requests came in the first check hour and 10 of them were errors (%10), the situation where all of the 5 requests coming in the next hour are errors (%100). The point to note is that in this scenario, the error amount has decreased, but the error rate has increased compared to the total request count. If 100 requests came in the first check hour and 10 of them were errors (%10), the situation where 20 or more of the 200 requests coming in the next hour are errors (%10). This situation is not considered an error in this scenario because the error amount has increased, but the error rate has not changed compared to the total request count. Scenario C: The upper band of EMA can be used as a threshold that indicates a certain error rate is outside the normal range. In this case, if the error rate exceeds the upper band of EMA, it is marked as an anomaly.Documentation Index
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Scenario A
In this scenario, only failed requests are filtered and failed requests within the last 1 hour are examined. If the ratio of total failed requests coming in the previous hour is 50% or more, this situation is considered an anomaly.
Scenario B
Purpose: To measure the increase in error rate compared to the previous result as a percentage.
Scenario C
- Green line (Metric Value): Shows the count of blocked requests.
- Red line (EMA Lower Value): Represents the calculated lower Exponential Moving Average (EMA) value.
- Black line (EMA Value): Shows the current value of EMA.
- Blue line (EMA Upper Value): Represents the calculated upper EMA value.