Implementing an Azure Data Solution: Monitoring Metrics for Azure Stream Analytics Jobs

Which Metrics to Monitor for Azure Stream Analytics Jobs

Question

You have an Azure Stream Analytics job.

You need to ensure that the job has enough streaming units provisioned.

You configure monitoring of the SU% Utilization metric.

Which two additional metrics should you monitor? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

Answers

Explanations

Click on the arrows to vote for the correct answer

A. B. C. D. E.

BD

B: Late Input Events: events that arrived later than the configured late arrival tolerance window.

Note: While comparing utilization over a period of time, use event rate metrics. InputEvents and OutputEvents metrics show how many events were read and processed.

D: In job diagram, there is a per partition backlog event metric for each input. If the backlog event metric keeps increasing, it's also an indicator that the system resource is constrained (either because of output sink throttling, or high CPU).

https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-scale-jobs

When using Azure Stream Analytics, it is important to ensure that enough streaming units are provisioned to handle the data volume and velocity. Monitoring certain metrics can help determine if additional streaming units are needed. In addition to monitoring the SU% Utilization metric, two additional metrics that should be monitored are Watermark Delay and Backlogged Input Events.

Watermark Delay measures the amount of time it takes for data to move through the stream processing pipeline. If Watermark Delay is consistently high, it may indicate that the processing pipeline is becoming overloaded and additional streaming units are needed.

Backlogged Input Events measures the number of input events waiting to be processed. If Backlogged Input Events is consistently high, it may indicate that the processing pipeline is becoming overloaded and additional streaming units are needed.

Late Input Events, Out of Order Events, and Function Events are also important metrics to monitor for troubleshooting and optimization purposes, but they do not provide information on whether additional streaming units are needed. Late Input Events measures the number of input events that arrive too late to be processed. Out of Order Events measures the number of input events that arrive out of order. Function Events measures the number of events processed by each function.

Therefore, the correct answers for the exam question are A. Watermark Delay and D. Backlogged Input Events.