{"id":3660,"date":"2025-12-24T10:01:13","date_gmt":"2025-12-24T15:01:13","guid":{"rendered":"https:\/\/www.mymiller.name\/wordpress\/?p=3660"},"modified":"2025-12-24T10:01:13","modified_gmt":"2025-12-24T15:01:13","slug":"crafting-custom-metrics-for-micrometer-tracing-a-comprehensive-guide","status":"publish","type":"post","link":"https:\/\/www.mymiller.name\/wordpress\/spring_tracing\/crafting-custom-metrics-for-micrometer-tracing-a-comprehensive-guide\/","title":{"rendered":"Crafting Custom Metrics for Micrometer Tracing: A Comprehensive Guide"},"content":{"rendered":"\n

Micrometer, a versatile application metrics facade, seamlessly integrates with Spring Boot Actuator to capture and expose vital insights into your application\u2019s performance. While Micrometer offers a plethora of pre-defined metrics, you often need to track application-specific metrics that aren\u2019t covered out of the box. This blog post will explore creating custom metrics for Micrometer tracing, along with the necessary Gradle\/Maven setup and examples of various custom metric types.<\/p>\n

1. Gradle\/Maven Setup<\/strong><\/p>\n

First, ensure your Spring Boot project includes the required Micrometer dependencies.<\/p>\n

Gradle<\/strong><\/p>\n

dependencies {\n    implementation 'io.micrometer:micrometer-core'\n    implementation 'io.micrometer:micrometer-registry-prometheus' \/\/ Or your preferred registry\n}\n<\/code><\/pre>\n
Maven<\/strong><\/p>\n
<dependencies>\n    <dependency>\n        <groupId>io.micrometer<\/groupId>\n        <artifactId>micrometer-core<\/artifactId>\n    <\/dependency>\n    <dependency>\n        <groupId>io.micrometer<\/groupId>\n        <artifactId>micrometer-registry-prometheus<\/artifactId> \n    <\/dependency>\n<\/dependencies>\n<\/code><\/pre>\n
2. Application.yml Configuration<\/strong><\/p>\n
While Micrometer typically auto-configures itself, you may want to fine-tune its behavior using application.yml<\/code>:<\/p>\n
management:\n  metrics:\n    export:\n      prometheus:\n        enabled: true  # Enable Prometheus endpoint (if using Prometheus)\n<\/code><\/pre>\n
3. The MeterRegistry<\/code><\/strong><\/p>\n
The MeterRegistry<\/code> is the core component in Micrometer responsible for managing and collecting metrics. In Spring Boot applications, Micrometer automatically creates and configures a MeterRegistry<\/code> bean for you. You can obtain a reference to this bean using dependency injection:<\/p>\n
import io.micrometer.core.instrument.MeterRegistry;\n\n@Service\npublic class MyService {\n\n    private final MeterRegistry meterRegistry;\n\n    public MyService(MeterRegistry meterRegistry) {\n        this.meterRegistry = meterRegistry;\n    }\n\n    \/\/ ... your service methods ...\n}\n<\/code><\/pre>\n
Once you have a reference to the MeterRegistry<\/code>, you can use it to create and register your custom metrics, as shown in the examples below.<\/p>\n
4. Creating Custom Metrics<\/strong><\/p>\n
Micrometer provides a rich set of metric types to instrument various aspects of your application.<\/p>\n
4.1. Counter<\/strong><\/p>\n
Counters are ideal for tracking the number of occurrences of an event.<\/p>\n
import io.micrometer.core.instrument.Counter;\n\n\/\/ ... (inside your service class)\n\npublic void performOperation() {\n    \/\/ ... your operation logic ...\n    Counter myCounter = Counter.builder("my.custom.counter")\n            .description("Counts the number of times a specific operation is performed")\n            .register(meterRegistry); \n    myCounter.increment(); \n}\n<\/code><\/pre>\n
4.2. Gauge<\/strong><\/p>\n
Gauges represent a single numerical value that can go up or down.<\/p>\n
import io.micrometer.core.instrument.Gauge;\n\n\/\/ ... (inside your service class)\n\nprivate int currentQueueSize = 0;\n\npublic MyService(MeterRegistry registry) {\n    Gauge.builder("my.queue.size", this, MyService::getCurrentQueueSize)\n            .description("Tracks the current size of a queue")\n            .register(registry);\n}\n\npublic int getCurrentQueueSize() {\n    return currentQueueSize;\n}\n\n\/\/ ... methods to add\/remove items from the queue, updating currentQueueSize\n<\/code><\/pre>\n
4.3. Timer<\/strong><\/p>\n
Timers measure the duration (and distribution) of an operation.<\/p>\n
import io.micrometer.core.instrument.Timer;\n\n\/\/ ... (inside your service class)\n\npublic void performOperation() {\n    Timer myTimer = Timer.builder("my.operation.timer")\n            .description("Measures the duration of a specific operation")\n            .register(meterRegistry);\n\n    myTimer.record(() -> {\n        \/\/ ... your operation logic ...\n    });\n}\n<\/code><\/pre>\n
4.4. Distribution Summary<\/strong><\/p>\n
Distribution summaries track the distribution of events (e.g., request sizes).<\/p>\n
import io.micrometer.core.instrument.DistributionSummary;\n\n\/\/ ... (inside your service class)\n\npublic void processRequest(int requestSize) {\n    \/\/ ... your request processing logic ...\n    DistributionSummary requestSizeSummary = DistributionSummary.builder("my.request.size")\n            .description("Tracks the distribution of request sizes")\n            .register(meterRegistry);\n    requestSizeSummary.record(requestSize); \n}\n<\/code><\/pre>\n
4.5. Long Task Timer<\/strong><\/p>\n
Long task timers track the duration of long-running operations.<\/p>\n
import io.micrometer.core.instrument.LongTaskTimer;\n\n\/\/ ... (inside your service class)\n\npublic void performLongTask() {\n    LongTaskTimer longTaskTimer = LongTaskTimer.builder("my.long.task")\n            .description("Tracks the duration of a long-running task")\n            .register(meterRegistry);\n\n    longTaskTimer.record(() -> {\n        \/\/ ... your long-running task logic ...\n    });\n}\n<\/code><\/pre>\n
By creating custom metrics with Micrometer and leveraging the MeterRegistry<\/code>, you gain granular visibility into your Spring Boot application\u2019s behavior. Utilize these metrics to identify bottlenecks, optimize performance, and make informed decisions about your application\u2019s architecture and resource allocation. Remember, effective monitoring is key to building resilient and high-performing applications.<\/p>\n<\/div>\n\n\n\n
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