Metric | Description |
---|---|
Backlogged Input Events
|
Number of input events that are backlogged. A non-zero value for this metric implies that your job can’t keep up with the number of incoming events. If this value is slowly increasing or consistently non-zero, you should scale out your job.
|
Data Conversion Errors
|
Number of errors that the Stream Analytics job encountered when attempting to convert data types.
|
Early Input Events
|
Events whose application timestamp is earlier than their arrival time by more than 5 minutes.
|
Failed Function Requests
|
Number of failed Azure Machine Learning function calls (if applicable).
|
Function Events
|
Number of events sent to the Azure Machine Learning function (if applicable).
|
Function Requests
|
Number of calls to the Azure Machine Learning function (if applicable).
|
Input Deserialization Errors
|
Number of input events that could not be deserialized.
|
Input Event Bytes
|
Amount of data received by the Stream Analytics job, in bytes. This can be used to validate that events are being sent to the input source.
|
Input Events
|
Number of records deserialized from the input events. This count does not include incoming events that result in deserialization errors.
|
Input Sources Received
|
Number of messages received by the job. For Event Hub, a message is a single EventData. For Blob, a message is a single blob.
|
Late Input Events
|
Events that arrived later than the configured late arrival tolerance window.
|
Out-of-Order Events
|
Number of events received out of order that were either dropped or given an adjusted timestamp, based on the Event Ordering Policy.
|
Output Events
|
Amount of data sent by the Stream Analytics job to the output target, in number of events.
|
Runtime Errors
|
Total number of errors related to query processing (excluding errors found while ingesting events or outputting results)
|
SU % Utilization
|
The utilization of the Streaming Unit(s) assigned to a job from the Scale tab of the job. Should this indicator reach 80%, or above, there is high probability that event processing may be delayed or stopped making progress.
|
Watermark Delay
|
The maximum watermark delay across all partitions of all outputs in the job.
|
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Tuesday, February 11, 2020
Data Platform Tips 76 - Metrics to monitor on Azure Stream Analytics
Azure Stream Analytics Job need to be monitored for resource consumption and error handling scenarios and it is a good practice to monitor the following metrics that will assist in trouble shooting the jobs.
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