10 Azure Function Logging Best Practices

Azure Functions are a great way to quickly and easily deploy code to the cloud. However, logging is an important part of any application, and Azure Functions are no exception. Logging can help you debug issues, monitor performance, and track usage.

In this article, we’ll discuss 10 best practices for logging in Azure Functions. We’ll cover topics such as logging levels, log retention, and log aggregation. By following these best practices, you can ensure that your Azure Functions are properly logging and that you have the data you need to troubleshoot and monitor your applications.

1. Use Application Insights

Application Insights is a powerful tool that allows you to monitor and analyze your application’s performance in real-time. It also provides detailed insights into the usage of your functions, including errors, latency, and other metrics.

Using Application Insights for Azure function logging will help you quickly identify any issues with your code or environment, as well as provide valuable data about how users are interacting with your functions. This can be especially useful when debugging complex problems or optimizing your functions for better performance.

2. Log to Azure Storage

Logging to Azure Storage allows you to store your logs in a centralized location, making them easier to access and analyze. It also makes it possible to use tools like Log Analytics or Power BI to visualize the data and gain insights into how your functions are performing.

Azure Functions provides built-in support for logging to Azure Storage, so all you need to do is configure the connection string in your function app settings. Once that’s done, you can start logging messages from within your code using the TraceWriter class. This will allow you to log information about errors, performance metrics, and other useful data points.

3. Write your own logger

When you write your own logger, you can customize it to fit the specific needs of your application. You can also add additional features such as logging levels and custom log formats. This allows you to have more control over what is logged and how it is presented in the logs. Additionally, writing your own logger gives you the ability to easily integrate with other services such as Slack or Splunk for further analysis.

4. Use the built-in TraceWriter

The TraceWriter is a logging object that’s available to all Azure Functions. It allows you to log messages, errors, and other information about your function execution.

The TraceWriter also has the ability to write logs to different destinations such as Application Insights or an external logging service. This makes it easy to track and analyze your functions’ performance over time. Additionally, the TraceWriter can be used to debug issues with your functions in real-time.

Using the TraceWriter for logging will help ensure that your Azure Function runs smoothly and efficiently. It’s important to remember that logging should always be done in a consistent manner so that you can easily identify any potential problems.

5. Use a logging framework

Logging frameworks provide a consistent way to log messages, which makes it easier for developers to understand and debug their code.

Logging frameworks also allow you to configure the logging level (e.g., verbose, info, warning, error) so that only important messages are logged. This helps reduce clutter in your logs and makes it easier to find the information you need.

Finally, logging frameworks can be configured to send log messages to different destinations such as files, databases, or cloud services like Azure Application Insights. This allows you to store and analyze your logs in one place, making it easier to identify trends and issues.

6. Don’t use Console.WriteLine()

Console.WriteLine() is a synchronous operation, which means that it will block the execution of your code until the message has been written to the console. This can cause performance issues and slow down your application.

Instead, use an asynchronous logging library such as Serilog or NLog. These libraries are designed for high-performance applications and allow you to log messages without blocking the execution of your code. They also provide additional features such as structured logging, filtering, and custom formatting.

7. Avoid using log levels

Log levels are often used to indicate the severity of a log message, but they can be misleading. For example, an error log level doesn’t necessarily mean that there is an actual error in your code; it could just be a warning or informational message.

Instead of relying on log levels, use descriptive messages and tags to help you quickly identify what type of log message it is. This will make it easier for you to filter through logs and find the information you need. Additionally, using descriptive messages and tags makes it easier to search for specific types of log messages.

8. Consider structured logging

Structured logging allows you to store log data in a structured format, such as JSON. This makes it easier to query and analyze the logs for insights into your application’s performance.

Structured logging also helps with troubleshooting because it provides more detailed information about errors and exceptions. You can use this information to quickly identify root causes of issues and take corrective action.

Finally, structured logging is more efficient than traditional logging methods because it requires less storage space. This means that you can keep more logs without having to worry about running out of disk space.

9. Use correlation IDs

Correlation IDs are unique identifiers that can be used to track a single request as it passes through multiple services. This makes it easier to trace the source of an issue and identify which components were involved in processing the request.

Using correlation IDs also helps you group related log entries together, making it easier to analyze logs for debugging purposes. Additionally, correlation IDs can help you quickly identify requests from specific users or customers.

To implement correlation IDs in your Azure functions, use the LogCorrelationId() method provided by the Microsoft.Extensions.Logging library. This will add a unique identifier to each log entry generated by your function.

10. Prefer async methods for logging

When logging in an Azure function, you want to make sure that the log messages are written as quickly and efficiently as possible. This is especially important when dealing with high-volume workloads. Async methods allow for faster logging because they don’t block the main thread while waiting for the log message to be written.

Additionally, async methods can help reduce latency by allowing other tasks to run while the log message is being written. This helps ensure that your functions remain responsive even under heavy load.