Kotlin Application Monitoring: Overview of Tools and Libraries

Kotlin Application Monitoring: From Metrics to Observability

Modern Kotlin applications — from server-side microservices (Ktor, Spring Boot) to mobile Android solutions — require constant state observation. Without quality monitoring, it is impossible to detect memory leaks, performance drops, or business logic errors in time. In this article, we will review key libraries and tools for monitoring Kotlin applications, show code examples, and provide implementation recommendations.

Why is Monitoring Needed in Kotlin?

Kotlin is actively used in backend (Spring Boot, Ktor, http4k) and mobile development. The main goals of monitoring are:

• Performance — response time, CPU load, memory;
• Reliability — number of errors, failure rate;
• Business metrics — request count, active users;
• Diagnostics — request tracing, context logging.

Main Tools for Kotlin Monitoring

1. Micrometer — Metrics Factory for JVM

Micrometer is the de facto standard for collecting metrics in the JVM ecosystem. It supports export to Prometheus, Graphite, Datadog, New Relic, and other systems. For Kotlin applications on Spring Boot, Micrometer is built into Actuator, but it can also be used with Ktor.

Example of integration with Prometheus in Ktor:

// build.gradle.ktsimplementation("io.micrometer:micrometer-registry-prometheus:1.12.0")implementation("io.ktor:ktor-server-micrometer-metrics")

// Application.ktfun Application.module() {    install(MicrometerMetrics) {        registry = PrometheusMeterRegistry(PrometheusConfig.DEFAULT)        // Adding custom metrics        counter("custom_requests_total") { "endpoint" to "/api" }    }    routing {        get("/metrics") {            call.respond(registry.scrape())        }    }}

After startup, metrics will be available at /metrics in Prometheus format.

2. OpenTelemetry — Full Observability

OpenTelemetry (OTel) is a set of tools for collecting traces, metrics, and logs. An SDK with support for automatic instrumentation of popular frameworks is available for Kotlin. It allows tracking call chains between microservices.

Example of manual tracing:

import io.opentelemetry.api.GlobalOpenTelemetryimport io.opentelemetry.api.trace.Span

fun processOrder(orderId: String) {    val tracer = GlobalOpenTelemetry.getTracer("order-service")    val span = tracer.spanBuilder("processOrder")        .setAttribute("order.id", orderId)        .startSpan()    try {        // business logic        validateOrder(orderId)        chargePayment(orderId)    } catch (e: Exception) {        span.recordException(e)        span.setStatus(StatusCode.ERROR)    } finally {        span.end()    }}

OpenTelemetry integrates well with Jaeger, Zipkin, Grafana Tempo.

3. Logback + Kotlin Logging — Structured Logs

For logging in Kotlin, Logback is often used together with the Kotlin Logging wrapper. This provides convenient syntax and support for MDC (Mapped Diagnostic Context).

import mu.KotlinLoggingimport org.slf4j.MDC

private val logger = KotlinLogging.logger {}

fun handleRequest(userId: String) {    MDC.put("user", userId)    logger.info { "Processing request" }    try {        // code    } catch (e: Exception) {        logger.error(e) { "Error during processing" }    } finally {        MDC.clear()    }}

Structured logs (JSON) can be sent to Elasticsearch or Loki.

4. Ktor Metrics Plugin — For Native Ktor Metrics

For Ktor servers, there is a built-in plugin ktor-server-metrics

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