Troubleshoot Azure Stream Analytics by using diagnostics logs

Occasionally, an Azure Stream Analytics job unexpectedly stops processing. It's important to be able to troubleshoot this kind of event. Failures can be caused by an unexpected query result, by connectivity to devices, or by an unexpected service outage. The diagnostics logs in Stream Analytics can help you identify the cause of issues when they occur and reduce recovery time.

It is highly recommended to enable diagnostic logs for all jobs as this will greatly help with debugging and monitoring.

Log types

Stream Analytics offers two types of logs:

  • Activity logs (always on), which give insights into operations performed on jobs.

  • Diagnostics logs (configurable), which provide richer insights into everything that happens with a job. Diagnostics logs start when the job is created and end when the job is deleted. They cover events when the job is updated and while it’s running.


You can use services like Azure Storage, Azure Event Hubs, and Azure Monitor logs to analyze nonconforming data. You are charged based on the pricing model for those services.


This article was recently updated to use the term Azure Monitor logs instead of Log Analytics. Log data is still stored in a Log Analytics workspace and is still collected and analyzed by the same Log Analytics service. We are updating the terminology to better reflect the role of logs in Azure Monitor. See Azure Monitor terminology changes for details.

Debugging using activity logs

Activity logs are on by default and give high-level insights into operations performed by your Stream Analytics job. Information present in activity logs may help find the root cause of the issues impacting your job. Do the following steps to use activity logs in Stream Analytics:

  1. Sign in to the Azure portal and select Activity log under Overview.

    Stream Analytics activity log

  2. You can see a list of operations that have been performed. Any operation that caused your job to fail has a red info bubble.

  3. Click an operation to see its summary view. Information here is often limited. To learn more details about the operation, click JSON.

    Stream Analytics activity log operation summary

  4. Scroll down to the Properties section of the JSON, which provides details of the error that caused the failed operation. In this example, the failure was due to a runtime error from out of bound latitude values. Discrepancy in the data that is processed by a Stream Analytics job causes a data error. You can learn about different input and output data errors and why they occur.

    JSON error details

  5. You can take corrective actions based on the error message in JSON. In this example, checks to ensure latitude value is between -90 degrees and 90 degrees need to be added to the query.

  6. If the error message in the Activity logs isn’t helpful in identifying root cause, enable diagnostic logs and use Azure Monitor logs.

Send diagnostics to Azure Monitor logs

Turning on diagnostic logs and sending them to Azure Monitor logs is highly recommended. Diagnostics logs are off by default. To turn on diagnostics logs, complete these steps:

  1. Sign in to the Azure portal, and navigate to your Stream Analytics job. Under Monitoring, select Diagnostics logs. Then select Turn on diagnostics.

    Blade navigation to diagnostics logs

  2. Create a Name in Diagnostic settings and check the box next to Send to Log Analytics. Then add an existing or create a new Log analytics workspace. Check the boxes for Execution and Authoring under LOG, and AllMetrics under METRIC. Click Save. It is recommended to use a Log Analytics workspace in the same Azure region as your Stream Analytics job to prevent additional costs.

    Settings for diagnostics logs

  3. When your Stream Analytics job starts, diagnostic logs are routed to your Log Analytics workspace. To view diagnostic logs for your job, select Logs under the Monitoring section.

    Diagnostic Logs under Monitoring

  4. Stream Analytics provides pre-defined queries that allows you to easily search for the logs that you are interested in. The 3 categories are General, Input data errors and Output data errors. For example, to see a summary of all the errors of your job in the last 7 days, you can select Run of the appropriate pre-defined query.

    Diagnostic Logs under Monitoring

    Results of logs

Diagnostics log categories

Azure Stream Analytics captures two categories of diagnostics logs:

  • Authoring: Captures log events that are related to job authoring operations, such as job creation, adding and deleting inputs and outputs, adding and updating the query, and starting or stopping the job.

  • Execution: Captures events that occur during job execution.

    • Connectivity errors
    • Data processing errors, including:
      • Events that don’t conform to the query definition (mismatched field types and values, missing fields, and so on)
      • Expression evaluation errors
    • Other events and errors

Diagnostics logs schema

All logs are stored in JSON format. Each entry has the following common string fields:

Name Description
time Timestamp (in UTC) of the log.
resourceId ID of the resource that the operation took place on, in upper case. It includes the subscription ID, the resource group, and the job name. For example, /SUBSCRIPTIONS/6503D296-DAC1-4449-9B03-609A1F4A1C87/RESOURCEGROUPS/MY-RESOURCE-GROUP/PROVIDERS/MICROSOFT.STREAMANALYTICS/STREAMINGJOBS/MYSTREAMINGJOB.
category Log category, either Execution or Authoring.
operationName Name of the operation that is logged. For example, Send Events: SQL Output write failure to mysqloutput.
status Status of the operation. For example, Failed or Succeeded.
level Log level. For example, Error, Warning, or Informational.
properties Log entry-specific detail, serialized as a JSON string. For more information, see the following sections in this article.

Execution log properties schema

Execution logs have information about events that happened during Stream Analytics job execution. The schema of properties varies depending on whether the event is a data error or a generic event.

Data errors

Any error that occurs while the job is processing data is in this category of logs. These logs most often are created during data read, serialization, and write operations. These logs do not include connectivity errors. Connectivity errors are treated as generic events. You can learn more about the cause of various different input and output data errors.

Name Description
Source Name of the job input or output where the error occurred.
Message Message associated with the error.
Type Type of error. For example, DataConversionError, CsvParserError, or ServiceBusPropertyColumnMissingError.
Data Contains data that is useful to accurately locate the source of the error. Subject to truncation, depending on size.

Depending on the operationName value, data errors have the following schema:

  • Serialize events occur during event read operations. They occur when the data at the input does not satisfy the query schema for one of these reasons:

    • Type mismatch during event (de)serialize: Identifies the field that's causing the error.

    • Cannot read an event, invalid serialization: Lists information about the location in the input data where the error occurred. Includes blob name for blob input, offset, and a sample of the data.

  • Send events occur during write operations. They identify the streaming event that caused the error.

Generic events

Generic events cover everything else.

Name Description
Error (optional) Error information. Usually, this is exception information if it's available.
Message Log message.
Type Type of message. Maps to internal categorization of errors. For example, JobValidationError or BlobOutputAdapterInitializationFailure.
Correlation ID GUID that uniquely identifies the job execution. All execution log entries from the time the job starts until the job stops have the same Correlation ID value.

Next steps