As a developer, stack traces are one of the most common error types you’ll run into. Every developer makes mistakes, including you. When you make a mistake, your code will likely exit and print a weird-looking message called a stack trace. But actually, a stack trace represents a path to a treasure, like a pirate map. It shows you the exact route your code traversed leading up to the point where your program printed an exception.
But, how do you read a stack trace? How does a stack trace help with troubleshooting your code? Let’s start with a comprehensive definition of a stack trace.
What Is a Stack Trace?
To put it simply, a stack trace represents a call stack at a certain point in time. To better understand what a call stack is, let’s dive a bit deeper into how programming languages work.
A stack is actually a data type that contains a collection of elements. The collection works as a last-in, first-out (LIFO) collection. Each element in this collection represents a function call in your code that contains logic.
Whenever a certain function call throws an error, you’ll have a collection of function calls that lead up to the call that caused the particular problem. This is due to the LIFO behavior of the collection that keeps track of underlying, previous function calls.
This also implies that a stack trace is printed top-down. The stack trace first prints the function call that caused the error and then prints the previous underlying calls that led up to the faulty call. Therefore, reading the first line of the stack trace shows you the exact function call that threw an error.
Now that you have a deeper understanding of how a stack trace works, let’s learn to read one.
How to Read a Stack Trace
Stack traces are constructed in a very similar way in most languages: they follow the LIFO stack approach. Let’s take a look at the Java stack trace below.
Exception in thread "main" java.lang.NullPointerException at com.example.myproject.Book.getTitle(Book.java:16) at com.example.myproject.Author.getBookTitles(Author.java:25) at com.example.myproject.Bootstrap.main(Bootstrap.java:14)
The first line tells us the exact error that caused the program to print a stack trace. We see a NullPointerException, which is a common mistake for Java programmers. From the Java documentation, we note that a NullPointerException is thrown when an application attempts to use “null” in a case where an object is required.
Now we know the exact error that caused the program to exit. Next, let’s read the previous call stack. You see three lines that start with the word “at”. All three of those lines are part of the call stack. The first line represents the function call where the error occurred. As we can see, the getTitle function of the Book class is the last executed call. Furthermore, the error occurred at line 16 in the Book class file.
The other calls represent previous calls that lead up to the getTitle function call. In other words, the code produced the following execution path:
- Start in main() function.
- Call getBookTitles() function in Author class at line 25.
- Call getTitle() function in Book class at line 16.
Hopefully, this information gives you a better understanding of how to approach a call stack. So, what’s a stack trace used for?
How to Use a Stack Trace
A stack trace is a valuable piece of information that can be used for debugging purposes. Whenever you encounter an error in your application, you want to be able to quickly debug the problem. Initially, developers should look in the application logs for the stack trace, because often, the stack trace tells you the exact line or function call that caused a problem. It’s a very valuable piece of information for quickly resolving bugs because it points to the exact location where things went wrong.
In addition to telling you the exact line or function that caused a problem, a stack trace also tracks important metrics that monitor the health of your application. For example, if the average number of stack traces found in your logs increases, you know a bug has likely occurred. Furthermore, a low level of stack trace exceptions indicates that your application is in good health. In short, stack traces and the type of errors they log can reveal various metrics related to your application as explained in the example.
Common Problems With Stack Traces and Third-Party Packages
In some cases, an error occurs when you send incorrect input to one of the third-party libraries you use. As you might expect, your program will print a stack trace of the function calls leading up to the problem. However, now that you’re dealing with a third-party library, you’ll have to read many function calls before you recognize one associated with your code. In some cases, like when too many function calls happen within a third-party package, you may not even see any references to your code.
You can still try to debug your code by looking at where a particular package was used. However, if you use this particular package frequently throughout your code, debugging your application won’t be easy.
But there’s a solution to the third-party stack problem. Let’s check it out!
Solving the Third-Party Stack Trace Problem
Luckily, you can solve third-party stack trace problems by catching exceptions. A call to a third-party library may cause an exception, which will cause your program to print a stack trace containing function calls coming from within the third-party library. However, you can catch the exception in your code with the use of a try-catch statement, which is native to many programming languages such as Java or Node.js.
If you use a try-catch statement, the stack trace will start from the point where you included the try-catch logic. This presents you with a more actionable and readable stack trace that doesn’t traverse into third-party library’s function calls. It’s an effective and simple solution to make your stack traces easy to understand.
Next, let’s learn how log management and stack traces work together.
Log Management and Stack Traces
You might wonder what log management and stack traces have to do with each other, and actually, they’re very compatible. It’s best practice for your DevOps team to implement a logging solution. Without an active logging solution, it’s much harder to read and search for stack traces. A logging solution provides you with an easy-to-use interface and better filtering capabilities.
A log management solution helps aggregate logs, index them, and make them searchable, all from a single interface. You can run advanced queries to find specific logs or stack trace information. This approach is much faster than using the CTRL+F key combination to look through your logs.
To summarize, we focused on the need for a logging solution to access stack traces and any other relevant information your application outputs. A stack trace is one of the most valuable pieces of information to help developers identify problems quickly.
Furthermore, a stack trace shows an exact execution path, providing context to developers trying to solve bugs. The first line in the call stack represents the last executed function call, so remember to always read a stack trace top-down. That first function call is responsible for throwing an exception.
Want to decrease your bug resolution time? Check out Scalyr’s log management solution. If you want to learn more about log formatting and best practices for logging, check out this log formatting article.
This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. Besides that, he loves learning about marketing, UX psychology, and entrepreneurship. When he’s not writing, he’s probably enjoying a Belgian beer!