You may encounter various job run errors. Resolving these errors can be quite challenging. However, using diagnostic logs can expedite the troubleshooting process. This article shows you how to leverage logs in the Azure environment to diagnose and resolve job run errors.
Understanding Azure Logs
Azure provides two types of logs: activity logs and diagnostic logs.
- Activity Logs: These logs provide you with insight into the operations that were performed on Azure resources in your subscription. They offer information about when the operation occurred, who initiated it, and the status.
- Diagnostic Logs: These logs provide rich, resource-specific diagnostic and performance data.
Operational twists might occur during the deployment or execution of a data science model in Azure, and these logs come in handy in such cases.
Accessing Azure Logs
Azure logs can be accessed via the Azure portal, the Azure Storage, Event Hubs, or Log Analytics, depending on your requirements.
- Azure Portal: The Azure portal provides access to all Azure log data within the logs blade.
- Azure Storage: If you require durability and low cost, then Azure storage is the ideal service.
- Event Hubs: If you wish to stream the Azure logs to a custom endpoint or a third-party logging and telemetry service, you can use Event Hubs.
- Log Analytics:This service is ideal for deep analysis, alerting, and automated response.
To view the logs on the Azure portal, navigate to Monitor -> Logs. Here, you can run queries to analyze your logged data.
Using Logs to Troubleshoot Job Run Errors
Once you have accessed the logs, you can begin diagnosing the job run errors. Azure logs return error information and stack traces that explain why your job is failing. The logs also return metadata and other data science-specific statuses, such as the status of model training.
For example, if a job failed during the model training phase, the logs might return an error such as ‘Model training timeout’. By investigating and debugging the logs, you can identify the root cause of why the model training timed out, and consequently devise a solution.
Reading Logs
Leaning how to read and analyse these logs is vital. The Azure logs return JSON strings that contain details about the operation that was logged. The JSON strings are formatted to be both human-readable and machine-interpretable. Here is an example of log data:
{
“time”: “2020-06-01T02:15:15.0000000Z”,
“resourceId”:”/subscriptions/…”,
“operationName”:”Run Model”,
“category”:”Run Model”,
“resultType”:”Success”,
“callerIpAddress”:”…”,
“correlationId”:”…”,
“identity”:{…},
“level”:”Information”,
“location”:”…”,
“properties”:{…}
}
In conclusion, knowing how to use Azure logs to troubleshoot job run errors can save you a lot of time and resources during the implementation of a data science solution on Azure. It allows you to identify, analyse and resolve errors in your Azure environment. Plus, with Azure’s advanced log processing capabilities, you can not only handle current issues but anticipate and stay ahead of future ones.
Practice Test
True or False: Azure provides logs which can help in debugging and troubleshooting job run errors.
- True
- False
Answer: True
Explanation: Azure does maintain logs for various tasks which can be used for troubleshooting purposes including job run errors.
Multiple choice: Where can you find run history and logs for troubleshooting in Azure Machine Learning Studio?
- a) Experiment Pane
- b) Compute tab
- c) Models
- d) Deploy Pane
Answer: a) Experiment Pane
Explanation: The Experiment Pane in Azure Machine Learning Studio shows the run history and logs for every experiment run.
True or False: Logs for Azure Databricks jobs are found under the “Jobs” tab on the Databricks workspace.
- True
- False
Answer: True
Explanation: You can access logs for each job under the Jobs tab in Azure Databricks workspace. They provide insightful information when you’re diagnosing failures.
Multiple choice: What does the Log Analytics workspace in Azure provide?
- a) Real-time analytics
- b) Search across all logs
- c) Visualizations
- d) All of the above
Answer: d) All of the above
Explanation: Azure Log Analytics workspace provides real-time analytics, search across all logs, and useful visualizations for in-depth analysis.
True or False: Azure Databricks streaming logs can be accessed for real-time troubleshooting.
- True
- False
Answer: True
Explanation: Azure Databricks provides streaming logs which can be accessed for real-time troubleshooting of job run errors.
Multiple choice: Which of the following errors may be resolved by using logs in Azure ML pipelines?
- a) Compute target errors
- b) Data access errors
- c) Execution errors
- d) All of the above
Answer: d) All of the above
Explanation: Using logs, you can troubleshoot a variety of errors in Azure ML pipelines including compute target errors, data access errors, and execution errors.
True or False: Azure Log Analytics can be integrated with Azure Monitor for comprehensive troubleshooting.
- True
- False
Answer: True
Explanation: Azure Monitor and Log Analytics can be integrated to provide comprehensive monitoring and dig deeper into logs for troubleshooting job run errors.
Multiple choice: What types of logs are collected by Azure Monitor?
- a) Activity Logs
- b) Diagnostic Logs
- c) Metrics
- d) All of the above
Answer: d) All of the above
Explanation: Azure Monitor collects Activity Logs, Diagnostic Logs, and Metrics from Azure resources that provide information about their operation and performance.
True or False: Logs in Azure pipelines are only available for failed runs.
- True
- False
Answer: False
Explanation: Logs in Azure pipelines are available for all runs, not just the failed ones.
Multiple choice: Which one of the following can be used to create custom views and queries over data in Azure logs?
- a) Diagnostic Settings
- b) Log Analytics workspaces
- c) Activity log
- d) Alerts
Answer: b) Log Analytics workspaces
Explanation: Azure Log Analytics workspaces allow you to create custom views and queries over your collected data.
True or False: You cannot track the progress of an ongoing run with Azure Machine Learning logs.
- True
- False
Answer: False
Explanation: You can use Azure Machine Learning logs to actively track the progress of your ongoing runs.
Multiple choice: The “Metrics” pane in Azure Machine Learning Studio is used for:
- a) Visualizing metrics
- b) Log generation
- c) Error reporting
- d) All the above
Answer: a) Visualizing metrics
Explanation: The “Metrics” pane in Azure Machine Learning Studio provides visualizations of various metrics corresponding to your run. Logs are typically found in the “Experiments” pane.
True or False: The “Application Insights” feature in Azure Machine Learning Service offers real-time telemetry.
- True
- False
Answer: True
Explanation: The “Application Insights” feature in Azure Machine Learning Service provides real-time telemetry and logging capabilities to help diagnose and troubleshoot issues.
Multiple choice: In reference to Azure DataBricks, what is DBU?
- a) DataBricks Unit
- b) DataBricks User
- c) DataBricks Utility
- d) None of the above
Answer: a) DataBricks Unit
Explanation: DBU stands for Databricks Unit, a unit of processing capability per hour used by Azure Databricks.
True or False: The Azure portal does not allow exporting of logs for independent analysis.
- True
- False
Answer: False
Explanation: The Azure portal allows exporting of logs for independent analysis outside of the Azure ecosystem.
Interview Questions
What does logging enable you to do in Azure?
Logging provides visibility into the operational health, availability, and performance of an application, enabling you to rapidly identify, triage, and address issues.
How do you configure logging in Azure SDK?
Logging in Azure SDK can be configured by adding an app settings.json file to your project and configuring the LogLevel for your application.
What is the relationship between Log Analytics and troubleshooting job run errors in Azure?
Log Analytics is a service in Azure that collects and analyzes data generated by resources in your cloud and on-premises environments. It helps you understand how your applications are running and proactively identifies issues affecting them and the resources they depend on.
How can you use Application Insights to troubleshoot job run errors?
Application Insights can be used to monitor your live application, detect performance anomalies, and diagnose issues with distributed tracing and analytics.
How do you use Azure Monitor Logs?
Azure Monitor Logs can be used to perform complex analysis across all your logs from multiple resources and subscriptions. You can create sophisticated queries and set up alerts.
How can you filter log data in Log Analytics?
You can filter log data in Log Analytics by using the filter and group by commands. The filter command reduces the dataset to only include records that match the specified condition, while the group command aggregates records based on specified fields.
What is Kusto Query Language (KQL) in the context of Azure?
The Kusto Query Language (KQL) is the read-only request language used by Azure Data Explorer and Azure Monitor Logs to retrieve, filter, and sort data.
What are some common log-based metrics that could be useful in troubleshooting job run errors?
These could include error rate, number of exceptions, duration of processes, request count, request rate, and response time metrics.
How do you set up alerts based on log data in Azure Monitor Logs?
Alerts can be set up based on log data in Azure Monitor Logs by creating an alert rule which includes the details of the log search query, the alert condition, and the actions to take when the condition is met.
How can Azure Log Analytics help in investigating a specific job error?
Azure Log Analytics can analyze extensive amounts of operational and environmental data. Through creating custom KQL queries, you can interactively analyze your operational data to troubleshoot the specific job error.
What type of data does Azure Monitor collect to give visibility into your application’s behavior?
Azure Monitor collects two types of data: metrics (numerical values that describe some aspect of a system at a particular time) and logs (events and other complex data structures).
How can you archive logs in Azure?
You can archive logs in Azure by enabling diagnostic settings to send platform logs and metrics to different destinations such as a storage account for auditing or manual inspection, Event Hubs for streaming to an external system, or Log Analytics workspace for analysis.
How can you use Azure Alerts to troubleshoot job run errors?
Azure Alerts can monitor your resources and notify you when a condition you specified is met. This can help react quicker to job run errors.
What kind of information can logs in Azure provide regarding job run errors?
Logs in Azure can provide detailed information, such as error messages, stack traces, and other diagnostic data that can help to identify the root cause of the job run error.
Why is log aggregation important in troubleshooting job run errors in Azure?
Log aggregation helps to centrally collect and store logs from different components or services of your application in Azure. It makes it easier to search and analyze logs, providing a holistic view of the system health, which aids in troubleshooting job run errors.
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