Profiling Kotlin Code: Finding Bottlenecks

Introduction: Why Profiling Kotlin Code Is Critically Important

When developing in Kotlin — a modern, concise, and safe language for JVM, Android, and multiplatform projects — performance often remains in the shadows. We write clean code, use coroutines, delegates, and extensions, but one day the application starts to lag: animations freeze, API requests take longer than expected, and memory grows like yeast. In such cases, intuition and code review won't help — a scientific approach is needed.

Profiling is the process of measuring execution time, memory usage, CPU load, and other metrics to identify bottlenecks. Without it, you risk optimizing the wrong sections, spending hours on useless micro-optimizations. In this article, we'll break down how to profile Kotlin code, which tools to use, and how to interpret the results. The material will be useful for Android developers, backend engineers working with Ktor, and anyone writing Kotlin for JVM.

Key Metrics and Profiling Tools

Before diving into the code, it's important to understand what exactly we are measuring. The main profiling metrics are:

• CPU Time — how much processor time a method or block of code consumed.
• Wall Clock Time — the real time from start to finish of execution, including I/O wait time.
• Heap Memory Usage — the number of objects created in the heap and garbage collection (GC) time.
• Allocation Count — how many times a new object was created. This is often a hidden performance killer.

For Kotlin on JVM, the following tools are available:

• IntelliJ Profiler (Async Profiler) — a CPU and memory profiler built into IntelliJ IDEA Ultimate. Supports sampling and tracing.
• JProfiler — a powerful commercial tool with visualization of threads, heap, and GC.
• VisualVM — a free profiler included with the JDK. Allows monitoring a running application.
• YourKit — another commercial profiler with excellent support for Kotlin and coroutines.
• Android Studio Profiler — for Android applications: CPU, Memory, Network, and Energy.

For microbenchmarking (measuring the performance of individual functions), use kotlinx-benchmark — a library based on JMH (Java Microbenchmark Harness).

Practical Example: Profiling CPU and Memory

Step 1: Preparing Code for Profiling

Suppose we have a function that processes a list of users and generates a report. We suspect it is running slowly. Here is the source code:

data class User(val id: Int, val name: String, val email: String)

fun generateReport(users: List<User>): String {    val sb = StringBuilder()    for (user in users) {        sb.append("ID: ${user.id}, Name: ${user.name}, Email: ${user.email}")    }    // Additional processing: filtering and sorting    val filtered = users.filter { it.email.contains("@") }        .sortedBy { it.name }    for (u in filtered) {        sb.append("Filtered: ${u.name}")    }    return sb.toString()}

It seems harmless, but with a list of 100,000 elements, this could become a problem.

Step 2: Running CPU Profiling in IntelliJ IDEA

Open IntelliJ IDEA Ultimate, run the application, and select Run → Profile 'main' (or use the profiling icon next to the Run button). IntelliJ will launch Async Profiler and display a Flame Graph.

After profiling completes, you will see something like:

CPU profiling result:- generateReport: 85% time  - String conc