Application Real-Time Monitoring Service:Customize metrics using the OpenTelemetry Java SDK

Step 1: Add a custom metric

The following code provides a simple example of a flash sale application. It defines two metrics:

• product_seckill_count: The number of flash sale attempts.

• product_current_stock: The current product inventory.

When you obtain the meter factory class to define metrics, a product_seckill parameter is passed. This parameter acts as a group identifier. All metrics that are subsequently defined by using this meter belong to this group, which is used in later configurations.

import io.opentelemetry.api.GlobalOpenTelemetry;
import io.opentelemetry.api.OpenTelemetry;
import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.common.Attributes;
import io.opentelemetry.api.metrics.LongCounter;
import io.opentelemetry.api.metrics.Meter;
import io.opentelemetry.api.metrics.ObservableLongGauge;

import javax.annotation.PreDestroy;
import java.util.concurrent.atomic.AtomicInteger;

class ProductService {

    // Static inventory counter.
    public final AtomicInteger stock = new AtomicInteger(0);

    private LongCounter seckillCounter;
    private ObservableLongGauge observableLongGauge;
    private final AttributeKey<String> seckillResult = AttributeKey.stringKey("seckill_result");

    public ProductService() {
        OpenTelemetry agentOpenTelemetry = GlobalOpenTelemetry.get();

        // Define the metric factory class. The 'product_seckill' name is important.
        Meter meter = agentOpenTelemetry.getMeter("product_seckill");
        // Define a counter to record the number of flash sale attempts.
        seckillCounter = meter.counterBuilder("product_seckill_count")
                .setUnit("1")
                .setDescription("seckill product count")
                .build();

        // Define a gauge to represent the current product inventory.
        observableLongGauge = meter.gaugeBuilder("product_current_stock").ofLongs().buildWithCallback((measurement -> {
            // Record the current product quantity.
            measurement.record(stock.get());
        }));

    }
    
    @PreDestroy
    public void clear() {
        observableLongGauge.close(); 
    }
    
    public void setKillProductCount(int count) {
        stock.set(count);
    }

    public String seckillProduct() {
        int currentStock = stock.get();
        if (currentStock <= 0) {
            seckillCounter.add(1, Attributes.of(seckillResult, "failed"));
            return "Flash sale failed. The product is sold out.";
        }
        // Try to decrement the stock.
        if (stock.decrementAndGet() >= 0) {
            seckillCounter.add(1, Attributes.of(seckillResult, "success"));
            return "Flash sale successful. Remaining stock: " + stock.get();
        } else {
            stock.incrementAndGet(); // Rollback.
            seckillCounter.add(1, Attributes.of(seckillResult, "failed"));
            return "Flash sale failed. The product is sold out.";
        }
    }
}

Step 2: Configure metric collection

In the console, modify the Custom Metric Collection Configuration by adding the group name you specified when creating the Meter in the previous step.

This configuration takes effect only for agent versions 4.5.0 or later and does not require an application restart. When you finish, click Save.

Step 3: View metrics and configure alerts

1. In the ARMS console, navigate to the Managed Service for Prometheus > Instances page. From the top menu bar, select the region where your application is deployed. Search for the Prometheus storage instance whose name starts with metricstore-apm-metrics-custom, and then click Shared Edition to open Grafana.

2. On the Grafana page, click Explore. From the data source drop-down list, select the Prometheus storage instance from the previous step.

   On the Grafana folder page, if the folder is empty, you will see the message This folder doesn't have any dashboards yet. Click + Create Dashboard to add a new one, or click Manage dashboards to move an existing dashboard into the folder.

3. Use PromQL to query the custom metrics defined in your code, as shown in the following figure. You can also create a custom observability dashboard in Grafana.

   

ARMS reports and stores the custom metrics defined with the OpenTelemetry SDK in your Prometheus storage instance. You can then create alert rules for these metrics.

Usage notes

• ARMS reports metrics at a 15-second interval.

• For counter metrics, ARMS reports the incremental value within each reporting interval, not the cumulative total.