为 .NET、Java、Node.js 和 Python 应用程序筛选 Azure Monitor OpenTelemetry

项目
01/10/2025

本文提供有关如何使用 Azure Monitor Application Insights 为应用程序筛选 OpenTelemetry 的指导。

你可能想要筛出遥测的原因包括：

筛出运行状况检查遥测，以减少噪音。
确保不会收集到 PII 和凭据。
筛出低价值的遥测以优化性能。

若要详细了解 OpenTelemetry 概念，请参阅 OpenTelemetry 概述或 OpenTelemetry 常见问题解答。

使用检测库筛选遥测

如需 Azure Monitor OpenTelemetry 发行版中包含的所有检测库的列表，请参阅添加和修改适用于 .NET、Java、Node.js 和 Python 应用程序的 Azure Monitor OpenTelemetry。

许多检测库都提供筛选选项。有关指南，请参阅相应的自述文件：

¹ 我们在包中提供 SqlClient 检测，它仍处于 beta 版。当它成为稳定版时，我们会将其作为标准包引用而添加。在此之前，若要自定义 SQLClient 检测，请将 OpenTelemetry.Instrumentation.SqlClient 包引用添加到项目并使用其公共 API。

dotnet add package --prerelease OpenTelemetry.Instrumentation.SqlClient

builder.Services.AddOpenTelemetry().UseAzureMonitor().WithTracing(builder =>
{
    builder.AddSqlClientInstrumentation(options =>
    {
        options.SetDbStatementForStoredProcedure = false;
    });
});

注意

此示例特定于 HTTP 检测。对于其他信号类型，目前没有可用于筛出遥测的特定机制。相反，需要自定义范围处理器。

以下示例演示如何排除使用 HTTP/HTTPS 检测库跟踪的某个 URL：

// Import the useAzureMonitor function and the ApplicationInsightsOptions class from the @azure/monitor-opentelemetry package.
const { useAzureMonitor, ApplicationInsightsOptions } = require("@azure/monitor-opentelemetry");

// Import the HttpInstrumentationConfig class from the @opentelemetry/instrumentation-http package.
const { HttpInstrumentationConfig }= require("@opentelemetry/instrumentation-http");

// Import the IncomingMessage and RequestOptions classes from the http and https packages, respectively.
const { IncomingMessage } = require("http");
const { RequestOptions } = require("https");

// Create a new HttpInstrumentationConfig object.
const httpInstrumentationConfig: HttpInstrumentationConfig = {
    enabled: true,
    ignoreIncomingRequestHook: (request: IncomingMessage) => {
    // Ignore OPTIONS incoming requests.
    if (request.method === 'OPTIONS') {
        return true;
    }
    return false;
    },
    ignoreOutgoingRequestHook: (options: RequestOptions) => {
    // Ignore outgoing requests with the /test path.
    if (options.path === '/test') {
        return true;
    }
    return false;
    }
};

// Create a new ApplicationInsightsOptions object.
const config: ApplicationInsightsOptions = {
    instrumentationOptions: {
    http: {
        httpInstrumentationConfig
    }
    }
};

// Enable Azure Monitor integration using the useAzureMonitor function and the ApplicationInsightsOptions object.
useAzureMonitor(config);

注意

此示例特定于 HTTP 检测。对于其他信号类型，目前没有可用于筛出遥测的特定机制。相反，需要自定义跨度处理器。

以下示例演示如何使用 OTEL_PYTHON_EXCLUDED_URLS 环境变量排除跟踪某个 URL：

export OTEL_PYTHON_EXCLUDED_URLS="http://localhost:8080/ignore"

这样做将排除以下 Flask 示例中所示的终结点：

...
# Import the Flask and Azure Monitor OpenTelemetry SDK libraries.
import flask
from azure.monitor.opentelemetry import configure_azure_monitor

# Configure OpenTelemetry to use Azure Monitor with the specified connection string.
# Replace `<your-connection-string>` with the connection string to your Azure Monitor Application Insights resource.
configure_azure_monitor(
    connection_string="<your-connection-string>",
)

# Create a Flask application.
app = flask.Flask(__name__)

# Define a route. Requests sent to this endpoint will not be tracked due to
# flask_config configuration.
@app.route("/ignore")
def ignore():
    return "Request received but not tracked."
...

使用跨度处理器筛选遥测

使用自定义处理器：

提示

添加 Azure Monitor 之前，请添加此处显示的处理器。

// Create an ASP.NET Core application builder.
var builder = WebApplication.CreateBuilder(args);

// Configure the OpenTelemetry tracer provider to add a new processor named ActivityFilteringProcessor.
builder.Services.ConfigureOpenTelemetryTracerProvider((sp, builder) => builder.AddProcessor(new ActivityFilteringProcessor()));
// Configure the OpenTelemetry tracer provider to add a new source named "ActivitySourceName".
builder.Services.ConfigureOpenTelemetryTracerProvider((sp, builder) => builder.AddSource("ActivitySourceName"));
// Add the Azure Monitor telemetry service to the application. This service will collect and send telemetry data to Azure Monitor.
builder.Services.AddOpenTelemetry().UseAzureMonitor();

// Build the ASP.NET Core application.
var app = builder.Build();

// Start the ASP.NET Core application.
app.Run();

使用以下代码将 ActivityFilteringProcessor.cs 添加到你的项目：

public class ActivityFilteringProcessor : BaseProcessor<Activity>
{
    // The OnStart method is called when an activity is started. This is the ideal place to filter activities.
    public override void OnStart(Activity activity)
    {
        // prevents all exporters from exporting internal activities
        if (activity.Kind == ActivityKind.Internal)
        {
            activity.IsAllDataRequested = false;
        }
    }
}

如果没有使用 AddSource("ActivitySourceName") 显式添加特定源，则不会导出使用该源创建的任何活动。

使用自定义处理器：

// Create an OpenTelemetry tracer provider builder.
// It is important to keep the TracerProvider instance active throughout the process lifetime.
using var tracerProvider = Sdk.CreateTracerProviderBuilder()
        .AddSource("OTel.AzureMonitor.Demo") // Add a source named "OTel.AzureMonitor.Demo".
        .AddProcessor(new ActivityFilteringProcessor()) // Add a new processor named ActivityFilteringProcessor.
        .AddAzureMonitorTraceExporter() // Add the Azure Monitor trace exporter.
        .Build();

使用以下代码将 ActivityFilteringProcessor.cs 添加到你的项目：

public class ActivityFilteringProcessor : BaseProcessor<Activity>
{
    // The OnStart method is called when an activity is started. This is the ideal place to filter activities.
    public override void OnStart(Activity activity)
    {
        // prevents all exporters from exporting internal activities
        if (activity.Kind == ActivityKind.Internal)
        {
            activity.IsAllDataRequested = false;
        }
    }
}

如果没有使用 AddSource("ActivitySourceName") 显式添加特定源，则不会导出使用该源创建的任何活动。

你可以使用自定义范围处理器来排除导出某些范围。若要将范围标记为不导出，请将 TraceFlag 设置为 DEFAULT。

使用自定义属性示例，但替换以下代码行：

// Import the necessary packages.
const { SpanKind, TraceFlags } = require("@opentelemetry/api");
const { ReadableSpan, Span, SpanProcessor } = require("@opentelemetry/sdk-trace-base");

// Create a new SpanEnrichingProcessor class.
class SpanEnrichingProcessor implements SpanProcessor {
    forceFlush(): Promise<void> {
        return Promise.resolve();
    }

    shutdown(): Promise<void> {
        return Promise.resolve();
    }

    onStart(_span: Span): void {}

    onEnd(span) {
        // If the span is an internal span, set the trace flags to NONE.
        if(span.kind == SpanKind.INTERNAL){
        span.spanContext().traceFlags = TraceFlags.NONE;
        }
    }
}

你可以使用自定义范围处理器来排除导出某些范围。若要将范围标记为不导出，请将 TraceFlag 设置为 DEFAULT：

...
# Import the necessary libraries.
from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry import trace

# Configure OpenTelemetry to use Azure Monitor with the specified connection string.
# Replace `<your-connection-string>` with the connection string to your Azure Monitor Application Insights resource.
configure_azure_monitor(
    connection_string="<your-connection-string>",
    # Configure the custom span processors to include span filter processor.
    span_processors=[span_filter_processor],
)

...

使用以下代码将 SpanFilteringProcessor 添加到你的项目：

# Import the necessary libraries.
from opentelemetry.trace import SpanContext, SpanKind, TraceFlags
from opentelemetry.sdk.trace import SpanProcessor

# Define a custom span processor called `SpanFilteringProcessor`.
class SpanFilteringProcessor(SpanProcessor):

    # Prevents exporting spans from internal activities.
    def on_start(self, span, parent_context):
        # Check if the span is an internal activity.
        if span._kind is SpanKind.INTERNAL:
            # Create a new span context with the following properties:
            #   * The trace ID is the same as the trace ID of the original span.
            #   * The span ID is the same as the span ID of the original span.
            #   * The is_remote property is set to `False`.
            #   * The trace flags are set to `DEFAULT`.
            #   * The trace state is the same as the trace state of the original span.
            span._context = SpanContext(
                span.context.trace_id,
                span.context.span_id,
                span.context.is_remote,
                TraceFlags(TraceFlags.DEFAULT),
                span.context.trace_state,
            )

后续步骤

若要进一步配置 OpenTelemetry 发行版，请参阅 Azure Monitor OpenTelemetry 配置。
若要查看源代码，请参阅 Azure Monitor AspNetCore GitHub 存储库。
若要安装 NuGet 包、检查更新或查看发行说明，请参阅 Azure Monitor AspNetCore NuGet 包页面。
若要进一步了解 Azure Monitor 和 OpenTelemetry，请参阅 Azure Monitor 示例应用程序。
若要详细了解 OpenTelemetry 及其社区，请参阅 OpenTelemetry .NET GitHub 存储库。
若要开启使用体验，请参阅启用 Web 或浏览器用户监视。
若要查看常见问题解答、故障排除步骤、支持选项或提供 OpenTelemetry 反馈，请参阅适用于 Azure Monitor Application Insights 的 OpenTelemetry 帮助、支持和反馈。

要查看源代码，请参阅 Azure Monitor OpenTelemetry GitHub 存储库。
若要安装 npm 包并检查更新，请参阅 @azure/monitor-opentelemetry npm 包页面。
若要进一步了解 Azure Monitor Application Insights 和 OpenTelemetry，请参阅 Azure Monitor 示例应用程序。
若要详细了解 OpenTelemetry 及其社区，请参阅 OpenTelemetry JavaScript GitHub 存储库。
若要开启使用体验，请参阅启用 Web 或浏览器用户监视。
若要查看常见问题解答、故障排除步骤、支持选项或提供 OpenTelemetry 反馈，请参阅适用于 Azure Monitor Application Insights 的 OpenTelemetry 帮助、支持和反馈。

通过

为 .NET、Java、Node.js 和 Python 应用程序筛选 Azure Monitor OpenTelemetry

使用检测库筛选遥测

使用跨度处理器筛选遥测

后续步骤

其他资源