This requirement is true when using the OpenTelemetry Metric API to send metrics and when using an instrumentation library. The OpenTelemetry API offers six metric 'instruments' to cover various metric scenarios and you need to pick the correct 'Aggregation Type' when visualizing metrics in Metrics Explorer. In this context, custom metrics refers to manually instrumenting your code to collect additional metrics beyond what the OpenTelemetry Instrumentation Libraries automatically collect. This makes upgrading easier and fills a gap in our custom telemetry support until all custom telemetry types are supported via the OpenTelemetry API. Similarly, Application Insights Node.js 3.x collects events created with the Application Insights Classic API. The following table represents the currently supported custom telemetry types: LanguageĪpplication Insights Java 3.x listens for telemetry that's sent to the Application Insights Classic API.

Language-specific logging/metrics libraries.This section explains how to collect custom telemetry from your application.ĭepending on your language and signal type, there are different ways to collect custom telemetry, including: Result = conn.execute(text('select 'hello world'')) # Database calls using the SQLAlchemy library will be automatically captured. # SQLAlchemy instrumentation is not officially supported by this package, however, you can use the OpenTelemetry `instrument()` method manually in conjunction with `configure_azure_monitor()`. # Configure OpenTelemetry to use Azure Monitor.Įngine = create_engine('sqlite:///:memory:')

# Import the `configure_azure_monitor()`, `SQLAlchemyInstrumentor`, `create_engine`, and `text` functions from the appropriate packages.įrom import configure_azure_monitorįrom import SQLAlchemyInstrumentorįrom sqlalchemy import create_engine, text This is not a supported scenario and you may get undesired behavior for your telemetry. Instrumenting a supported instrumentation library manually with instrument() in conjunction with the distro configure_azure_monitor() is not recommended. The list of community instrumentation libraries can be found here. To add a community instrumentation library (not officially supported/included in Azure Monitor distro), you can instrument directly with the instrumentations. New ExpressInstrumentation(), // Express instrumentation Instrumentations can be configured using AzureMonitorOpenTelemetryOptions // Import Azure Monitor OpenTelemetryĬonst = Get the OpenTelemetry tracer provider and meter providerĬonst tracerProvider = (trace.getTracerProvider() as ProxyTracerProvider).getDelegate() Ĭonst meterProvider = metrics.getMeterProvider()

For more information, see OpenTelemetry officially supported instrumentations. The following OpenTelemetry Instrumentation libraries are included as part of the Azure Monitor Application Insights Distro.

Azure Storage - Files Data Lake 12.5.0+.

Azure Storage - Blobs Cryptography 12.11.0+.

Azure Event Hubs - Azure Blob Storage Checkpoint Store 1.5.1+.

Azure Communication Phone Numbers 1.0.0+.

Telemetry emitted by these Azure SDKs is automatically collected by default:

JBoss Logging (including MDC properties) ¹ ³.

Log4j (including MDC/Thread Context properties) ¹ ³.

Micrometer Metrics, including Spring Boot Actuator metrics.

Servlet and Netty autoinstrumentation covers the majority of Java HTTP services, including Java EE, Jakarta EE, Spring Boot, Quarkus, and Micronaut.ĭependencies (plus downstream distributed trace propagation):ĭependencies (without downstream distributed trace propagation):

Signal vault competitior