In today’s digital age, logging data is a crucial part of application development and maintenance. Proper logging practices help developers gain visibility into the application’s behavior and monitor its performance, enabling them to identify and resolve issues more efficiently.
Unfortunately, not all developers and organizations are taking advantage of the best logging practices available, leading to a subpar experience for both developers and end-users. In this article, we will explore the top application logging practices that developers should adopt to ensure their applications are running smoothly.
Establishing Clear Logging Goals
The first step in establishing an effective logging strategy is to define the logging goals. Logging goals are a set of objectives that provide direction and purpose for the logs being generated. Logging goals typically include monitoring application performance, identifying and resolving issues, and complying with regulatory requirements.
To establish clear logging goals, developers should start by analyzing the business requirements and use cases of the application. By doing so, developers can gain a better understanding of the types of issues that may arise and what logs are needed to identify and resolve them.
Leveraging Proper Log Formatting
The format of logs is just as important as the content they contain. Proper log formatting provides a consistent structure that allows developers to read, interpret, and analyze logs quickly.
In general, logs should follow a standard format that includes a timestamp, log level, message, and context. By adhering to this format, developers can quickly identify the severity of issues, filter and search through logs efficiently, and troubleshoot issues more effectively.
Utilizing Log Levels
Log levels are critical to establishing the severity of issues within an application. By using log levels, developers can prioritize issues and take appropriate actions.
The most commonly used log levels include:
- DEBUG: These logs provide detailed information and are typically used for development purposes.
- INFO: These logs provide general information about the application’s behavior and are often used for monitoring and performance analysis.
- WARN: These logs highlight potential issues that may cause problems in the future.
- ERROR: These logs indicate that an error has occurred, and the application cannot continue running.
- FATAL: These logs represent critical errors that could result in system failure.
By using log levels appropriately, developers can identify and resolve issues efficiently, ultimately leading to a better end-user experience.
Incorporating Log Rotation
Log rotation is a practice used to manage log file sizes and improve application performance. Over time, logs can accumulate and take up significant disk space, causing performance issues. Log rotation involves automatically archiving or deleting old log files, making room for new logs.
Developers should establish a log rotation policy that aligns with their application requirements. The policy should consider the frequency of log generation, the maximum size of the log file, and the number of archived log files to keep. By doing so, developers can manage log file sizes efficiently and maintain application performance.
Implementing Log Analysis
Log analysis involves reviewing and analyzing logs to identify trends and patterns that can help improve application performance. By leveraging log analysis tools, developers can identify issues quickly and resolve them efficiently.
There are various log analysis tools available, such as ELK Stack, Graylog, and Splunk. These tools provide a centralized platform for analyzing logs, generating reports, and monitoring application performance.
Ensuring Log Security
Log files often contain sensitive information such as user credentials, API keys, and other data that can compromise the security of the application. Therefore, it is essential to ensure log security by restricting access to log files.
Developers should establish access controls and implement encryption measures to protect log files. They should also ensure that logs are stored in secure locations and that access to logs is only granted to authorized personnel. By doing so, developers can minimize the risk of data breaches and ensure the security of their applications.
Incorporating Automated Logging
Automated logging involves integrating logging practices into the application’s code, enabling logs to be generated automatically without developer intervention. Automated logging reduces the risk of human error and ensures that logs are generated consistently.
Developers can leverage various logging frameworks such as Log4j, NLog, and Serilog to incorporate automated logging into their applications. These frameworks provide a flexible and efficient way of generating logs and can be customized to meet specific application requirements.
Using Cloud-Based Logging Services
Cloud-based logging services provide a scalable and cost-effective way of managing logs. These services allow developers to store logs in the cloud and access them from anywhere, providing flexibility and ease of access.
There are various cloud-based logging services available, such as AWS CloudWatch Logs, Google Cloud Logging, and Azure Log Analytics. These services provide a centralized platform for managing logs, generating reports, and monitoring application performance.
What Do You Mean by Application Logging Practices?
Before diving into the practices themselves, let’s briefly clarify what application logging practices are. In the context of this post, I’m talking specifically about things that you do in your application’s source code.
So this means how you go about using the API. How do you fit the logging API into your source code? How do you use that API? And what kinds of messages do you log?
Use a Framework for Application Logging
Logging frameworks handle the standard parts of application logging for you. They also generally offer you ready-made options for the logging medium. So basically, you add a logging package to your application’s source code, and then you just start making calls to an API. All of the details of file I/O or database drivers or whatever become the framework’s problem.
Why do I recommend this? Well, first of all, it helps a lot with standardization. And secondly, this is a well-solved problem. You’ll find your life much easier if you rely on the solutions of those who have put a lot of time and thought into it, rather than reinventing that wheel. You specialize in writing software for your problem domain — not in writing loggers.
Avoid Side Effects and Logging Code in the Critical Path
Next up, let’s consider the effect that your logging code has on the rest of your application. The relationship here is rather interesting.
Logging, as a practice, is critically important. But the execution of any given logging logic generally takes a backseat to your code fulfilling its primary purpose. In other words, when your e-commerce platform (for instance) is logging a sale, the financial transaction itself is a whole lot more important than writing information about the transaction to a log file. You can live with a missing entry in your log file. You’ll lose a customer if you bill them without shipping them a product.
So make sure your logging logic doesn’t interfere with critical application activities. Don’t let some kind of exception generated in a logging statement, for instance, throw you out of your application’s normal execution flow. And definitely don’t involve the logger in the logic flow of your application. Logging should have almost no impact on your application. Logging frameworks can help a lot with this, but they won’t entirely prevent you from shooting yourself in the foot.
Know And Consider Your Audience
When you’re creating writes to your log, think through who (or what) will consume it. In the broadest terms, there are two types of consumers: humans and machines. If humans will consume the log, then it’s important to create human-readable log messages through the API. On the other hand, if machines will consume it, then you want to emphasize writing entries that those machines can parse (e.g. with key value pairs or JSON).
But you can take this even a bit further. Will operations people read it? Or, if you’re writing code for a small startup, perhaps non-technical people will pitch in for troubleshooting. I can’t enumerate all possible cases, but you should have a good idea for your codebase. Generally speaking, bear your audience in mind, the same way you might when writing documentation or code comments.
This is a related point to keeping your audience in mind, particularly when your audience consists of humans. Make sure you supply context to the entries you write to the log file.
In other words, give those reading enough relevant information to make sense of the entry. As an extreme example, imagine an entry in a log file that just said, “error” and listed a timestamp. Would that help you? Would it help anyone at all, ever? Probably not.
What sort of error was it? An exception? A validation error? And where did it happen? What inputs and outputs triggered it? What are the possible implications?
Again, I can’t take you through everything someone might find relevant in your situation. But if you give it some thought, you should be able to help those reading the log.
Think of Your Log as Data
The point about context slants toward humans consuming the log file, whereas this point focuses more on machine parsing. Still, the best scenario involves creating log files that either can consume. So think of your logs as data no matter who might consume them.
What do I mean by this? Quite simply, don’t think of your logs as free-form text. You’re not writing a short story through this medium. Instead, at a fundamental level, your logs capture data about your application’s runtime behavior. Give your entries relevant data such as time stamps, call stacks, and values of variables in your application.
Think of your logs, in general terms, as key-value stores that others want to use for data mining purposes. Get the best of all worlds by thinking of your log entries this way and then endeavoring to make them human-readable as well.
Strive for Consistency
This is another area where your logging framework can help a lot, but you still have some work to do. Go for consistency in how you structure entries to the log file.
Don’t add time stamps for some entries but not others. Don’t have an assortment of formats that people might use, depending on mood.
Instead, have a standard for log messages in your application, like you have a coding standard. You don’t need to go overboard, but it should establish a basic template for log messages and some guidance on what sorts of information to capture and when.
This consistency adds up to a log file that’s easy to consume and that you can rely on to provide information to you in a predictable way.
Err on the Side of Too Much
I’ll close with a philosophical recommendation. When in doubt, log it. If you’re unsure, err on the side of logging too much.
I get it. Logging historically has a pretty low signal to noise ratio. Most techies have, at some time or another, found themselves staring at gigantic log files, looking for needles in haystacks. It’s headache-inducing and frustrating, but you shouldn’t let it stop you from logging as much as possible.
The world of application development has come a long way and solved many problems. When it comes to noise in logs, efficient aggregation and search capabilities let you sift through all of that information without headaches. So capture as much of it as possible during the application logging phase of development. Aggregation and search have your back.