Manually instrument Node.js applications for Splunk Observability Cloud

Manually instrument your Node.js application when you need to add custom attributes to spans or want to manually generate spans and metrics.

Instrumenting applications automatically using the agent of the Splunk Distribution of OpenTelemetry Node.js covers most needs. Manually instrumenting your application is only necessary when, for example, you need to add custom attributes to spans or need to manually generate spans.

Note: Manual OTel instrumentation is fully compatible with Splunk automatic Node.js instrumentation and is fully supported by Splunk.

Custom traces

To send custom traces to Splunk Observability Cloud, add the required dependencies and configure tracing:

const { start } = require('@splunk/otel');
const opentelemetry = require('@opentelemetry/api');

start({
  serviceName: 'my-service',
});

const tracer = opentelemetry.trace.getTracer('example-app', '0.1.0');

function randomNumber() {
  return tracer.startActiveSpan('make-random', (span) => {
    const result = Math.random() * 42;
    span.setAttribute('random-result', result);
    span.end();
    return result;
  });
}

setInterval(() => {
  console.log(randomNumber());
}, 1000);
Note: For more examples of manual instrumentation, see Manual instrumentation in the OpenTelemetry official documentation.

Custom metrics

To send custom application metrics to Splunk Observability Cloud, add @opentelemetry/api to your dependencies:

const { start } = require('@splunk/otel');
const { Resource } = require('@opentelemetry/resources');
const { metrics } = require('@opentelemetry/api');

// All fields are optional.
start({
  // Takes preference over OTEL_SERVICE_NAME environment variable
  serviceName: 'my-service',
  metrics: {
    // The suggested resource is filled in using OTEL_RESOURCE_ATTRIBUTES
    resourceFactory: (suggestedResource: Resource) => {
      return suggestedResource.merge(new Resource({
        'my.property': 'xyz',
        'build': 42,
      }));
    },
    exportIntervalMillis: 1000, // default: 5000
    // The default exporter used is OTLP over gRPC
    endpoint: 'http://collector:4317',
  },
});

const meter = metrics.getMeter('my-meter');
const counter = meter.createCounter('clicks');
counter.add(3);

Set up custom metric readers and exporters

You can provide custom exporters and readers using the metricReaderFactory setting.

CAUTION: Usage of metricReaderFactory invalidates the exportInterval and endpoint settings.

The following example shows how to provide a custom exporter:

const { start } = require('@splunk/otel');
const { PrometheusExporter } = require('@opentelemetry/exporter-prometheus');
const { OTLPMetricExporter } = require('@opentelemetry/exporter-metrics-otlp-http');
const { PeriodicExportingMetricReader } = require('@opentelemetry/sdk-metrics-base');

start({
  serviceName: 'my-service',
  metrics: {
    metricReaderFactory: () => {
      return [
        new PrometheusExporter(),
        new PeriodicExportingMetricReader({
          exportIntervalMillis: 1000,
          exporter: new OTLPMetricExporter({ url: 'http://localhost:4318' })
        })
      ]
    },
  },
});

Select the type of aggregation temporality

Aggregation temporality describes how data is reported over time.

You can define two different aggregation temporalities:

  • AggregationTemporality.CUMULATIVE: Cumulative metrics, such as counters and histograms, are continuously summed together from a given starting point, which in this case is set with the call to start. This is the default temporality.

  • AggregationTemporality.DELTA: Metrics are summed together relative to the last metric collection step, which is set by the export interval.

To configure aggregation temporality in your custom metrics, use AggregationTemporality as in the example:

const { start } = require('@splunk/otel');
const { OTLPMetricExporter } = require('@opentelemetry/exporter-metrics-otlp-grpc');
const { AggregationTemporality, PeriodicExportingMetricReader } = require('@opentelemetry/sdk-metrics-base');

start({
  serviceName: 'my-service',
  metrics: {
    metricReaderFactory: () => {
      return [
        new PeriodicExportingMetricReader({
          exporter: new OTLPMetricExporter({
            temporalityPreference: AggregationTemporality.DELTA
          })
        })
      ]
    },
  },
});

For more information on aggregation temporality, see https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/data-model.md#sums on GitHub.