Logging Best Practices In Software Development

From an application point of view, logs are designed to enhance communication between users and the application software. Having appropriate logs with exceptional search capabilities is similar to having an artificial lighting appliance in a dark room.

Most software developers neglect the significance of logs in resolving software issues. However, the fact is, logs are indispensable when it comes to addressing software issues and helping the developers to know the health status of the application. Also, logs are pivotal to the performance of an application. If you’re a developer and have no insight into logs as a programming tool, you’re missing an invaluable tool to better your development career.

With that said, here are the logging best practices in software development

Write Logs Cautiously (don’t hinder performance)

If not done carefully, the addition of logs can add a handful of milliseconds to your application response time. Developers are typically encouraged to write logs in an asynchronous format. It’s possible to write logs in an external log file and export it to your preferred service. Programmers can use the library based on the language/framework they are working with. Using queues is perhaps an option to export logs; however, note the workable lag when viewing logs.

Use Appropriate Tools for the Job

As a developer, it’s up to you to pick the tools and services to ship, view, sort logs, and search. Based on the budget, the desire for the use of external services can be fully self-managed. The most important thing is to use tools that match your needs efficiently. For instance, it would be imprudent to use a queue for real-time logs. If you want real-time logs, come with a logging infrastructure and stack that’s suited for your needs.

Structure Your Logs

Structure your log lines to include a message and a content array to type extra information pertaining to the message. Adhere to the recommended JSON format for logging. This eases the act of searching and parsing a string of texts. You can set protocol of things that must be complied with, such as date, a description cannot surpass 255 characters, additional information goes to the content section, and so forth. With these simple protocols, it is effortless to find a log suited for your needs. It is commendable to deliver content with log lines like the points of the order when logging that the order might perhaps be exported.

Log Information Optimally

Excessive information is a nuisance, and too little of it is insignificant. It’s challenging to determine how much log is optimal and inadequate. This is something that developers struggle to resolve. For microservices, developers should contemplate traceability amid the services like using a distinct request identifier. It’s important to note that logs are impermanent. They are only alive for days to weeks since they’re not stored in the database.

Know Your Audience

As a developer, you must be insightful that your application logs target two types of audiences, namely humans and machines.

Machines are perfect at processing vast chunks of structured data rapidly and automatically.

On the other hand, humans are only good at processing small chunks of data, and it takes time to read through the logs. Alternatively, humans are more comfortable in handling unstructured data.

To make the best use of your logs, you need to design logs that are both human-readable and structured for robots.

Understand Metrics

Metrics are a fundamental concept in logging.

A metric is an explicit value of a property in a specified time, typically measured periodically.

Common types of metrics include:

  • Gauge – measure a random value such as CPU
  • Timer – measures the time taken by a procedure to be effected
  • Meter – measures the time interval between events
  • Counter- determines the increment and decrement of an integer value.

Every single metric defines the status of some components of the system.

The awesome things pertaining to metrics are having them in large numbers and tie up different metrics together. For instance, if we realize that whenever users’ login to our application is using a particular method over the other, we can tell the favorite method amongst the users.

Developers are encouraged to keep track of log metrics. In other words, separate metrics from your logs.

Reporting Alerts and Exception Handling

If your code develops some problems, logs help you understand the source of the problem and perhaps the steps to take to correct the code. Errors are part of software development and thus the need to find mechanisms to address them. Therefore, it’s essential to learn more about logs as they aid in unearthing the code issues within a system. This eases work for the programmers.

Moreover, when logging an exception, though the stack trace is focal, it’s arduous to read. However, you can use a handful of tools to make the stack trace easy to understand.

Pick a Proper Logging Framework and Leverage Its Advanced Features

Don’t attempt to create your logging framework. There are a handful of exceptional logging libraries for different programming languages.

Logging frameworks help you develop varying appenders, each having its output format and custom log pattern.

Other typical features comprise the logger name’s automatic addition, support for several severity levels, a timestamp, and filtering by these stages.

Logging framework also comprises of other advanced features such as lossy appender and logs-summarizing appender.

Log a Lot and then Log Some More.

We often disregard to write core logs or fail to log intentionally to keep your logs intact. You will usually spend more time because of lacking the answer in your logs, and that’s why it is essential to spend your time writing logs. The quality of your log goes hand in hand with the quality of your code.

Use Log Severity Levels

Every event has a distinct severe implication. This is critical since it helps you distinguish extreme and essential events from regular and irregular events.

Don’t neglect petty severity concerns; they can be applied as data points when trying to develop a baseline for the behavior of an application.

Your log files should comprise of few Error and Warn Messages and more Debug and Info messages. If you find your logs taking up significant disk space consider not logging debug messages on prod hosts and come up with efficient rotating strategies. Also ensure that you aren’t logging any important information in debug that would be necessary to root cause an issue if you do suppress debug levels.


From the above, it is evident that logs are critical in matters pertaining to software development. As a developer, there is a need to use logs to ensure your application is free from errors. By following the discussed logging best practices above for software developers, you will simplify your software development job and its users by being more aware of what your applications are doing.

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