What is code profiling?

Code profiling is an IT technology that helps identify the root causes of potential system issues by analyzing a desktop or mobile application's performance while it's running. Profiling tools can track app behavior from both high-level and granular perspectives.

For example, code profilers could identify which processes use the largest proportion of CPU resources. They can also examine individual lines of code to discover the source of memory leaks or find the root cause of resource conflicts. Equipped with this information, developers can make targeted code changes to improve application performance.

Applications don't always work as intended. Accordingly, profiling identifies and explains errors by analyzing application performance. By observing applications in action, profiling tools can track source code operations, memory allocation processes, and function call operations.

This is the primary advantage of code profiling: Instead of taking apps offline and examining operations in isolation, profiling tools explore app behavior in live environments.

While profiling can identify virtually any code issue, it's primarily used for high-level tasks, such as identifying CPU bottlenecks or memory leaks. This is because profiling tools can impact performance during code analysis. The scope and scale of this impact depend on the profiler type used and the complexity of the application analyzed.

The two primary types of code profilers are instrumentation and sampling, which both help improve observability.

Instrumentation

Instrumentation profilers add lines of code to track and monitor specific performance metrics, such as how long processes take to execute, which functions are called the most, or how CPU resources are used. Because instrumentation profiling adds lines of code, it can increase application overhead and reduce performance.

Sampling

Sampling profilers take snapshots of application code. These snapshots provide a high-level overview of which processes are running, what resources they're using, and how this impacts performance. Because sampling profilers view rather than alter application code, they create minimal performance issues. The trade-off? They may provide less detailed information about program processes than their instrumented counterparts.

Choosing the right type

Both types of code profiling offer benefits — which type is most effective depends on the specific situation and IT's objectives. For example, if teams suspect specific lines of code are causing performance bottlenecks, an instrumentation profiler makes sense to pinpoint the issue. However, if the goal is to understand application performance trends over time, a sampling profiler may be the best choice.

Code profiling offers organizations several benefits.

Identifying resource bottlenecks. Profilers can pinpoint the location of resource bottlenecks and suggest ways to remedy these issues.

Increasing application performance. Once issues are identified, the code can be modified to increase application performance.

Improving code quality. Resolving issues leads to cleaner code, both in the existing application and for applications in development, as teams can apply knowledge gained to their next project.

Enhancing the end-user experience. Applications are only as good as the user experience. Code profilers can help locate the source of lag or latency in user-facing apps to help resolve the issue.

Reducing development time and costs. Profilers can reduce the cost of post-deployment maintenance by quickly identifying issues. They can also streamline the development process when applied at the beginning of the development cycle, saving companies time and money.

While the specifics differ by organization and industry, code profilers have several common use cases.

Tracking memory usage

Excess memory usage or leaks can cause significant drops in application performance. In addition, memory issues are cumulative — the longer the application runs, the worse the problem gets. As a result, teams need to pinpoint when memory leaks start, where they're occurring, and how these leaks impact applications over time.

Code profilers make it possible to track memory issues from app startup to termination, allowing teams to track the memory leaks' progression over time.

Measuring execution time

Execution time is also a key concern for applications. If core processes take too long to execute, the user experience suffers. In the best-case scenario, users close the app and try again later. In the worst case, they stop using the app altogether.

Code profilers can measure the execution time of internal and user-facing processes, such as time to initial display (TTID) and time to fully drawn (TTFD). Execution times that are higher than expected or increasing over time may indicate a code issue.

Finding hot paths

Hot paths are the code paths an application most often uses. While every app has a hot path (or paths), code profilers can help ensure the paths used most frequently are efficiently designed and effectively integrated.

Observability solutions are critical in pinpointing potential problems. But organizations use an average of 10 monitoring tools to help track and manage apps. This produces a potential paradox — the solutions organizations use to improve visibility can create observability issues.

If teams constantly switch between apps and services that lack interoperability, they may spend more time getting back up to speed rather than solving code concerns. As a result, it's not enough to simply introduce a code profiling tool. Instead, organizations need to consider where the solution fits in their current stack and how it will interact with other processes.

In practice, four considerations are critical.

1. Impact

Profiler operations can impact application performance. For example, some tools may require source code modifications to effectively operate, while others may introduce large amounts of extra overhead. In the first case, these modifications may introduce additional code issues or errors that can be hard to identify and remediate. In the second, this extra overhead may cause significant application slowdowns.

Ideally, tools should be minimally invasive and easy to use.

2. Integration

It's also worth looking for solutions that integrate with existing environments or are provided as part of a larger observability platform. By unifying observability functions, IT teams can reduce the risk of blind spots and minimize tool complexity.

3. In-depth reporting

Tools should provide accurate and detailed reports that are easy to read. Consider a profiler that accurately captures CPU bottlenecks and operational challenges but produces confusing and complicated reports. While teams can put in the work and learn to parse these outputs, that time could be better spent addressing code issues.

4. Interoperability

Finally, profiling tools should play well with other applications and services within the network. Given the interconnected nature of applications, tools that operate in isolation offer limited value for teams looking to improve app performance.

As part of the larger Dynatrace observability platform, code profiling helps companies visualize and understand the interconnected impacts of their network environment.

With Dynatrace code-level profiling and optimization, teams are better equipped to pinpoint code issues, find root causes, and improve application performance. For example, thread analysis allows for continuous production profiling, while I/O bottlenecks can be traced down to the method name. Memory and allocation analysis, meanwhile, can eliminate memory leaks and improve code speed.