Complete Guide to Azure Function Logging and Monitoring

Azure Function logging and monitoring are crucial for ensuring your serverless applications run smoothly. You can configure logging for Azure Functions through the Azure portal or using Azure CLI.

To enable logging, you'll need to create an Application Insights resource, which can be done in a few clicks through the Azure portal. This will give you detailed insights into your function's performance and behavior.

Azure Functions also support logging to various destinations, including Azure Blob Storage, Azure Event Hubs, and Azure Files.

Log levels are a crucial part of logging in Azure Functions, and there are six levels to choose from: Trace, Debug, Information, Warning, Error, and Critical.

The chosen log level affects system performance and diagnostics, so it's essential to balance logging enough data for diagnostics with not overloading the system or filling the log with irrelevant information.

Debug and Trace logs are often only enabled during development or troubleshooting sessions due to their verbosity. Information, Warning, and Error logs are most common in a healthy production system.

To filter through logs and find necessary information, use descriptive tags and messages instead of relying solely on log levels.

Here are the six log levels supported by the ILogger interface, along with a brief description of each:

In a production system, it's common to see Information, Warning, and Error logs, while Debug and Trace logs are typically only enabled during development or troubleshooting.

Structured logging is a powerful way to enhance logging in Azure Functions, allowing you to format and store log events in an organized manner that can be easily searched and analyzed.

To implement structured logging, you can use the ILogger interface, which lets developers add structured data to log messages. This makes it easier to correlate events, identify patterns, and detect anomalies.

You can also use the AzureFunctionsRequestTrackingMiddleware class to log incoming HTTP requests, which will show up as requests in Application Insights when using Arcus Application Insights Serilog sink.

The middleware component can be configured to influence the behavior of the HTTP request tracking, such as including HTTP request/response bodies or filtering HTTP request headers.

Setting Minimum Logging Levels can help suppress unnecessary logs from services like Azure Storage, allowing you to see only the important information like errors.

Logging unhandled exceptions is a crucial aspect of any application, and Azure Functions provides a straightforward way to do so. The AzureFunctionsExceptionHandlingMiddleware class can be added to the worker pipeline to catch and log unhandled exceptions.

This middleware component is designed to catch exceptions that occur during request processing and log them through ILogger implementations. The HTTP status code 500 is used by default as the response code when an unhandled exception is caught.

By implementing this middleware, developers can ensure that any unexpected errors are properly logged and tracked, making it easier to diagnose and resolve issues. This can save a significant amount of time and effort in the long run.

Begin Scope is a powerful feature in logging mechanisms that allows you to group related log entries together. This is done using the BeginScope method, which automatically adds a scoped named property to all log entries within that scope.

With BeginScope, you can nest scopes, which is useful for organizing complex logging scenarios. This is especially helpful when you need to track events across multiple functions or services.

Here are some key benefits of using BeginScope:

By using BeginScope, you can make your log data more organized and easier to analyze, which is a huge time-saver in the long run.

Log Levels are a crucial aspect of logging mechanisms. They help categorize logs based on their importance or severity, making it easier to analyze specific data and determine how much to log and retain.

The ILogger interface supports six log levels: Trace, Debug, Information, Warning, Error, and Critical. Each level serves a specific purpose, with Trace being the most detailed level for debugging purposes and Critical being for severe errors that might prevent the process from continuing to execute.

Information logs are most common in a healthy production system, while Debug and Trace logs are often only enabled during development or troubleshooting sessions due to their verbosity.

Here are the recommended log levels to use in different scenarios:

Using the right log level can significantly impact system performance and diagnostics. Logging at a high volume, especially at the Trace or Debug level, can generate meaningful data, but it can also affect system performance and storage.

Structured logging is a powerful tool in Azure Functions that helps you make sense of your log data. It involves formatting and storing log events in an organized manner that can be easily searched and analyzed.

With structured logging, each log message is a structured data object, usually in JSON format. This makes it more convenient to query and analyze log data.

Structured logging can transform log data into valuable insights, guiding application development and optimization efforts. You can record events in Azure Functions and create a valuable source of information.

To implement structured logging in Azure Functions, you can use the ILogger interface. This interface lets developers use semantic logging to add structured data to log messages.

By using structured logging, you can quickly search vast amounts of log data, filter logs by specific properties, and analyze trends over time. This makes it easier to correlate events, identify patterns, and detect anomalies.