本指南提供关于集成和自定义 OpenTelemetry(OTel)工具至 Azure Monitor Application Insights 的说明。
若要了解有关 OpenTelemetry 概念的详细信息,请参阅 OpenTelemetry 概述。
有关 Azure Monitor OpenTelemetry 发行版中包含的所有检测库的概述,请参阅 Azure Monitor OpenTelemetry 的自动数据收集和资源检测器。
当您使用 OpenTelemetry 社区的工具库时,可以自动收集更多数据。
注意
我们不支持也不保证社区检测库的质量。 要为我们的发行版推荐一个,请在反馈社区中发帖或投票。 请注意,某些检测基于实验性的 OpenTelemetry 规范,未来可能会引入中断性变更。
若要添加社区库,请在为库添加 NuGet 包后使用 ConfigureOpenTelemetryMeterProvider 或 ConfigureOpenTelemetryTracerProvider 方法。
以下示例演示如何添加 运行时检测 以收集额外的指标:
dotnet add package OpenTelemetry.Instrumentation.Runtime
// Create a new ASP.NET Core web application builder.
var builder = WebApplication.CreateBuilder(args);
// Configure the OpenTelemetry meter provider to add runtime instrumentation.
builder.Services.ConfigureOpenTelemetryMeterProvider((sp, builder) => builder.AddRuntimeInstrumentation());
// 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 web application.
var app = builder.Build();
// Start the ASP.NET Core web application.
app.Run();
以下示例演示如何添加 运行时检测 以收集额外的指标:
// Create a new OpenTelemetry meter provider and add runtime instrumentation and the Azure Monitor metric exporter.
// It is important to keep the MetricsProvider instance active throughout the process lifetime.
var metricsProvider = Sdk.CreateMeterProviderBuilder()
.AddRuntimeInstrumentation()
.AddAzureMonitorMetricExporter();
不能将社区检测库与 GraalVM Java 原生应用程序一起使用。
export class RegisterExpressInstrumentationSample {
static async run() {
// Dynamically import Azure Monitor and Express instrumentation
const { useAzureMonitor } = await import("@azure/monitor-opentelemetry");
const { registerInstrumentations } = await import("@opentelemetry/instrumentation");
const { ExpressInstrumentation } = await import("@opentelemetry/instrumentation-express");
// Initialize Azure Monitor (uses env var if set)
const monitor = useAzureMonitor();
// Register the Express instrumentation
registerInstrumentations({
instrumentations: [new ExpressInstrumentation()],
});
console.log("Express instrumentation registered");
}
}
要添加社区仪器库,请直接使用仪器进行操作。 可以在 GitHub 上找到社区检测库的列表。
注意
请勿使用和发行版instrument()手动检测configure_azure_monitor()。 此方法不受支持,可能会导致遥测数据出现意外行为。
# Import the `configure_azure_monitor()`, `SQLAlchemyInstrumentor`, `create_engine`, and `text` functions from the appropriate packages.
from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor
from sqlalchemy import create_engine, text
# Configure OpenTelemetry to use Azure Monitor.
configure_azure_monitor()
# Create a SQLAlchemy engine.
engine = create_engine("sqlite:///:memory:")
# SQLAlchemy instrumentation is not officially supported by this package, however, you can use the OpenTelemetry `instrument()` method manually in conjunction with `configure_azure_monitor()`.
SQLAlchemyInstrumentor().instrument(
engine=engine,
)
# Database calls using the SQLAlchemy library will be automatically captured.
with engine.connect() as conn:
result = conn.execute(text("select 'hello world'"))
print(result.all())
收集自定义遥测数据
本部分介绍如何从应用程序收集自定义遥测数据。
根据语言和信号类型,可以通过不同的方式收集自定义遥测数据,包括:
- 开放遥测 API
- 特定于语言的日志记录/指标库
- Application Insights 经典 API
下表显示了目前支持的自定义遥测类型:
| 语言 |
自定义事件 |
自定义指标 |
依赖项 |
异常 |
页面视图 |
请求 |
痕迹 |
|
ASP.NET Core |
|
|
|
|
|
|
|
| 开放遥测 API |
|
是 |
是 |
是 |
|
是 |
|
ILogger 应用程序接口 |
是 |
|
|
|
|
|
是 |
| AI 经典 API |
是 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Java |
|
|
|
|
|
|
|
| 开放遥测 API |
是 |
是 |
是 |
是 |
|
是 |
|
Logback,Log4j,JUL |
|
|
|
是 |
|
|
是 |
| Micrometer 指标 |
|
是 |
|
|
|
|
|
| AI 经典 API |
是 |
是 |
是 |
是 |
是 |
是 |
是 |
|
|
|
|
|
|
|
|
|
Node.js |
|
|
|
|
|
|
|
| 开放遥测 API |
是 |
是 |
是 |
是 |
|
是 |
|
|
|
|
|
|
|
|
|
|
Python |
|
|
|
|
|
|
|
| 开放遥测 API |
|
是 |
是 |
是 |
|
是 |
|
| Python日志记录模块 |
|
|
|
|
|
|
是 |
| 事件扩展模块 |
是 |
|
|
|
|
|
是 |
注意
Application Insights Java 3.x 和 Application Insights Node.js 3.x 从 Application Insights Classic API 收集遥测数据。 此行为简化了升级,并暂时支持自定义遥测,直到 OpenTelemetry API 包含所有自定义遥测类型。
添加自定义指标
在此上下文中,自定义指标术语是指手动检测代码以收集超出 OpenTelemetry Instrumentation 库自动收集的额外指标。 要详细了解如何使用指标,请参阅 Application Insights 中的指标。
OpenTelemetry API 提供六种度量工具,以涵盖各种度量方案。当在指标资源管理器中可视化指标时,需要选取正确的聚合类型。 使用 OpenTelemetry 指标 API 发送指标和使用检测库时,此要求是正确的。
下表显示了每个 OpenTelemetry 度量工具的建议聚合类型。
| OpenTelemetry 工具 |
Azure Monitor聚合类型 |
| 计数器 |
总和 |
| 异步计数器 |
总和 |
| 直方图 |
最小值、最大值、平均值、总和和计数 |
| 异步仪表 |
平均值 |
| UpDownCounter |
总和 |
| 异步增减计数器 |
总和 |
OpenTelemetry 规范介绍了这些工具,并提供了每种工具使用的示例。
提示
直方图是最通用的,也与 Application Insights GetMetric Classic API 最接近。 Azure Monitor目前将直方图工具转换为我们支持的五种聚合类型,并且对百分位数的支持也在开发中。 虽然通用性较低,但其他 OpenTelemetry 仪器对应用程序性能的影响较小。
直方图示例
应用程序启动时必须按名称订阅仪表:
// Create a new ASP.NET Core web application builder.
var builder = WebApplication.CreateBuilder(args);
// Configure the OpenTelemetry meter provider to add a meter named "OTel.AzureMonitor.Demo".
builder.Services.ConfigureOpenTelemetryMeterProvider((sp, builder) => builder.AddMeter("OTel.AzureMonitor.Demo"));
// 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 web application.
var app = builder.Build();
// Start the ASP.NET Core web application.
app.Run();
Meter 必须使用同一名称初始化:
// Create a new meter named "OTel.AzureMonitor.Demo".
var meter = new Meter("OTel.AzureMonitor.Demo");
// Create a new histogram metric named "FruitSalePrice".
Histogram<long> myFruitSalePrice = meter.CreateHistogram<long>("FruitSalePrice");
// Create a new Random object.
var rand = new Random();
// Record a few random sale prices for apples and lemons, with different colors.
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "apple"), new("color", "red"));
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "lemon"), new("color", "yellow"));
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "lemon"), new("color", "yellow"));
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "apple"), new("color", "green"));
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "apple"), new("color", "red"));
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "lemon"), new("color", "yellow"));
public class Program
{
// Create a static readonly Meter object named "OTel.AzureMonitor.Demo".
// This meter will be used to track metrics about the application.
private static readonly Meter meter = new("OTel.AzureMonitor.Demo");
public static void Main()
{
// Create a new MeterProvider object using the OpenTelemetry SDK.
// The MeterProvider object is responsible for managing meters and sending
// metric data to exporters.
// It is important to keep the MetricsProvider instance active
// throughout the process lifetime.
//
// The MeterProviderBuilder is configured to add a meter named
// "OTel.AzureMonitor.Demo" and an Azure Monitor metric exporter.
using var meterProvider = Sdk.CreateMeterProviderBuilder()
.AddMeter("OTel.AzureMonitor.Demo")
.AddAzureMonitorMetricExporter()
.Build();
// Create a new Histogram metric named "FruitSalePrice".
// This metric will track the distribution of fruit sale prices.
Histogram<long> myFruitSalePrice = meter.CreateHistogram<long>("FruitSalePrice");
// Create a new Random object. This object will be used to generate random sale prices.
var rand = new Random();
// Record a few random sale prices for apples and lemons, with different colors.
// Each record includes a timestamp, a value, and a set of attributes.
// The attributes can be used to filter and analyze the metric data.
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "apple"), new("color", "red"));
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "lemon"), new("color", "yellow"));
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "lemon"), new("color", "yellow"));
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "apple"), new("color", "green"));
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "apple"), new("color", "red"));
myFruitSalePrice.Record(rand.Next(1, 1000), new("name", "lemon"), new("color", "yellow"));
// Display a message to the user and wait for them to press Enter.
// This allows the user to see the message and the console before the
// application exits.
System.Console.WriteLine("Press Enter key to exit.");
System.Console.ReadLine();
}
}
import io.opentelemetry.api.GlobalOpenTelemetry;
import io.opentelemetry.api.metrics.DoubleHistogram;
import io.opentelemetry.api.metrics.Meter;
public class Program {
public static void main(String[] args) {
Meter meter = GlobalOpenTelemetry.getMeter("OTEL.AzureMonitor.Demo");
DoubleHistogram histogram = meter.histogramBuilder("histogram").build();
histogram.record(1.0);
histogram.record(100.0);
histogram.record(30.0);
}
}
注入 OpenTelemetry:
创建直方图:
import io.opentelemetry.api.metrics.DoubleHistogram;
import io.opentelemetry.api.metrics.Meter;
Meter meter = openTelemetry.getMeter("OTEL.AzureMonitor.Demo");
DoubleHistogram histogram = meter.histogramBuilder("histogram").build();
histogram.record(1.0);
histogram.record(100.0);
histogram.record(30.0);
注意
Quarkus 社区支持和维护 Quarkus 扩展。 寻求帮助,请使用 Quarkus 社区支持渠道。 Azure不提供对此集成的技术支持。
export class HistogramSample {
static async run() {
// Dynamically import Azure Monitor and metrics API
const { useAzureMonitor } = await import("@azure/monitor-opentelemetry");
const { metrics } = await import("@opentelemetry/api");
// Initialize Azure Monitor
const monitor = useAzureMonitor({
azureMonitorExporterOptions: {
connectionString:
process.env.APPLICATIONINSIGHTS_CONNECTION_STRING || "<YOUR-CONNECTION-STRING>",
},
});
// Create a histogram and record values
const meter = metrics.getMeter("testMeter");
const histogram = meter.createHistogram("histogram");
histogram.record(1, { testKey: "testValue" });
histogram.record(30, { testKey: "testValue2" });
histogram.record(100, { testKey2: "testValue" });
console.log("Histogram metrics recorded");
}
}
# Import the `configure_azure_monitor()` and `metrics` functions from the appropriate packages.
from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry import metrics
import os
# 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>",
)
# Opt in to allow grouping of your metrics via a custom metrics namespace in app insights metrics explorer.
# Specify the namespace name using get_meter("namespace-name")
os.environ["APPLICATIONINSIGHTS_METRIC_NAMESPACE_OPT_IN"] = "true"
# Get a meter provider and a meter with the name "otel_azure_monitor_histogram_demo".
meter = metrics.get_meter_provider().get_meter("otel_azure_monitor_histogram_demo")
# Record three values to the histogram.
histogram = meter.create_histogram("histogram")
histogram.record(1.0, {"test_key": "test_value"})
histogram.record(100.0, {"test_key2": "test_value"})
histogram.record(30.0, {"test_key": "test_value2"})
# Wait for background execution.
input()
反例
应用程序启动时必须按名称订阅仪表:
// Create a new ASP.NET Core web application builder.
var builder = WebApplication.CreateBuilder(args);
// Configure the OpenTelemetry meter provider to add a meter named "OTel.AzureMonitor.Demo".
builder.Services.ConfigureOpenTelemetryMeterProvider((sp, builder) => builder.AddMeter("OTel.AzureMonitor.Demo"));
// 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 web application.
var app = builder.Build();
// Start the ASP.NET Core web application.
app.Run();
Meter 必须使用同一名称初始化:
// Create a new meter named "OTel.AzureMonitor.Demo".
var meter = new Meter("OTel.AzureMonitor.Demo");
// Create a new counter metric named "MyFruitCounter".
Counter<long> myFruitCounter = meter.CreateCounter<long>("MyFruitCounter");
// Record the number of fruits sold, grouped by name and color.
myFruitCounter.Add(1, new("name", "apple"), new("color", "red"));
myFruitCounter.Add(2, new("name", "lemon"), new("color", "yellow"));
myFruitCounter.Add(1, new("name", "lemon"), new("color", "yellow"));
myFruitCounter.Add(2, new("name", "apple"), new("color", "green"));
myFruitCounter.Add(5, new("name", "apple"), new("color", "red"));
myFruitCounter.Add(4, new("name", "lemon"), new("color", "yellow"));
public class Program
{
// Create a static readonly Meter object named "OTel.AzureMonitor.Demo".
// This meter will be used to track metrics about the application.
private static readonly Meter meter = new("OTel.AzureMonitor.Demo");
public static void Main()
{
// Create a new MeterProvider object using the OpenTelemetry SDK.
// The MeterProvider object is responsible for managing meters and sending
// metric data to exporters.
// It is important to keep the MetricsProvider instance active
// throughout the process lifetime.
//
// The MeterProviderBuilder is configured to add a meter named
// "OTel.AzureMonitor.Demo" and an Azure Monitor metric exporter.
using var meterProvider = Sdk.CreateMeterProviderBuilder()
.AddMeter("OTel.AzureMonitor.Demo")
.AddAzureMonitorMetricExporter()
.Build();
// Create a new counter metric named "MyFruitCounter".
// This metric will track the number of fruits sold.
Counter<long> myFruitCounter = meter.CreateCounter<long>("MyFruitCounter");
// Record the number of fruits sold, grouped by name and color.
myFruitCounter.Add(1, new("name", "apple"), new("color", "red"));
myFruitCounter.Add(2, new("name", "lemon"), new("color", "yellow"));
myFruitCounter.Add(1, new("name", "lemon"), new("color", "yellow"));
myFruitCounter.Add(2, new("name", "apple"), new("color", "green"));
myFruitCounter.Add(5, new("name", "apple"), new("color", "red"));
myFruitCounter.Add(4, new("name", "lemon"), new("color", "yellow"));
// Display a message to the user and wait for them to press Enter.
// This allows the user to see the message and the console before the
// application exits.
System.Console.WriteLine("Press Enter key to exit.");
System.Console.ReadLine();
}
}
import io.opentelemetry.api.GlobalOpenTelemetry;
import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.common.Attributes;
import io.opentelemetry.api.metrics.LongCounter;
import io.opentelemetry.api.metrics.Meter;
public class Program {
public static void main(String[] args) {
Meter meter = GlobalOpenTelemetry.getMeter("OTEL.AzureMonitor.Demo");
LongCounter myFruitCounter = meter
.counterBuilder("MyFruitCounter")
.build();
myFruitCounter.add(1, Attributes.of(AttributeKey.stringKey("name"), "apple", AttributeKey.stringKey("color"), "red"));
myFruitCounter.add(2, Attributes.of(AttributeKey.stringKey("name"), "lemon", AttributeKey.stringKey("color"), "yellow"));
myFruitCounter.add(1, Attributes.of(AttributeKey.stringKey("name"), "lemon", AttributeKey.stringKey("color"), "yellow"));
myFruitCounter.add(2, Attributes.of(AttributeKey.stringKey("name"), "apple", AttributeKey.stringKey("color"), "green"));
myFruitCounter.add(5, Attributes.of(AttributeKey.stringKey("name"), "apple", AttributeKey.stringKey("color"), "red"));
myFruitCounter.add(4, Attributes.of(AttributeKey.stringKey("name"), "lemon", AttributeKey.stringKey("color"), "yellow"));
}
}
注入 OpenTelemetry:
创建计数器:
import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.common.Attributes;
import io.opentelemetry.api.metrics.LongCounter;
import io.opentelemetry.api.metrics.Meter;
Meter meter = openTelemetry.getMeter("OTEL.AzureMonitor.Demo");
LongCounter myFruitCounter = meter.counterBuilder("MyFruitCounter")
.build();
myFruitCounter.add(1, Attributes.of(AttributeKey.stringKey("name"), "apple", AttributeKey.stringKey("color"), "red"));
myFruitCounter.add(2, Attributes.of(AttributeKey.stringKey("name"), "lemon", AttributeKey.stringKey("color"), "yellow"));
myFruitCounter.add(1, Attributes.of(AttributeKey.stringKey("name"), "lemon", AttributeKey.stringKey("color"), "yellow"));
myFruitCounter.add(2, Attributes.of(AttributeKey.stringKey("name"), "apple", AttributeKey.stringKey("color"), "green"));
myFruitCounter.add(5, Attributes.of(AttributeKey.stringKey("name"), "apple", AttributeKey.stringKey("color"), "red"));
myFruitCounter.add(4, Attributes.of(AttributeKey.stringKey("name"), "lemon", AttributeKey.stringKey("color"), "yellow"));
注意
Quarkus 社区支持和维护 Quarkus 扩展。 寻求帮助,请使用 Quarkus 社区支持渠道。 Azure不提供对此集成的技术支持。
export class CounterSample {
static async run() {
// Dynamically import Azure Monitor and metrics API
const { useAzureMonitor } = await import("@azure/monitor-opentelemetry");
const { metrics } = await import("@opentelemetry/api");
// Initialize Azure Monitor
const monitor = useAzureMonitor({
azureMonitorExporterOptions: {
connectionString:
process.env.APPLICATIONINSIGHTS_CONNECTION_STRING || "<YOUR-CONNECTION-STRING>",
},
});
// Create a counter and add some sample values
const meter = metrics.getMeter("otel_azure_monitor_counter_demo");
const counter = meter.createCounter("MyFruitCounter");
counter.add(1, { name: "apple", color: "red" });
counter.add(2, { name: "lemon", color: "yellow" });
counter.add(1, { name: "lemon", color: "yellow" });
counter.add(2, { name: "apple", color: "green" });
counter.add(5, { name: "apple", color: "red" });
counter.add(4, { name: "lemon", color: "yellow" });
console.log("Counter metrics recorded");
}
}
# Import the `configure_azure_monitor()` and `metrics` functions from the appropriate packages.
from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry import metrics
import os
# 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>",
)
# Opt in to allow grouping of your metrics via a custom metrics namespace in app insights metrics explorer.
# Specify the namespace name using get_meter("namespace-name")
os.environ["APPLICATIONINSIGHTS_METRIC_NAMESPACE_OPT_IN"] = "true"
# Get a meter provider and a meter with the name "otel_azure_monitor_counter_demo".
meter = metrics.get_meter_provider().get_meter("otel_azure_monitor_counter_demo")
# Create a counter metric with the name "counter".
counter = meter.create_counter("counter")
# Add three values to the counter.
# The first argument to the `add()` method is the value to add.
# The second argument is a dictionary of dimensions.
# Dimensions are used to group related metrics together.
counter.add(1.0, {"test_key": "test_value"})
counter.add(5.0, {"test_key2": "test_value"})
counter.add(3.0, {"test_key": "test_value2"})
# Wait for background execution.
input()
量表示例
应用程序启动时必须按名称订阅仪表:
// Create a new ASP.NET Core web application builder.
var builder = WebApplication.CreateBuilder(args);
// Configure the OpenTelemetry meter provider to add a meter named "OTel.AzureMonitor.Demo".
builder.Services.ConfigureOpenTelemetryMeterProvider((sp, builder) => builder.AddMeter("OTel.AzureMonitor.Demo"));
// 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 web application.
var app = builder.Build();
// Start the ASP.NET Core web application.
app.Run();
Meter 必须使用同一名称初始化:
// Get the current process.
var process = Process.GetCurrentProcess();
// Create a new meter named "OTel.AzureMonitor.Demo".
var meter = new Meter("OTel.AzureMonitor.Demo");
// Create a new observable gauge metric named "Thread.State".
// This metric will track the state of each thread in the current process.
ObservableGauge<int> myObservableGauge = meter.CreateObservableGauge("Thread.State", () => GetThreadState(process));
private static IEnumerable<Measurement<int>> GetThreadState(Process process)
{
// Iterate over all threads in the current process.
foreach (ProcessThread thread in process.Threads)
{
// Create a measurement for each thread, including the thread state, process ID, and thread ID.
yield return new((int)thread.ThreadState, new("ProcessId", process.Id), new("ThreadId", thread.Id));
}
}
public class Program
{
// Create a static readonly Meter object named "OTel.AzureMonitor.Demo".
// This meter will be used to track metrics about the application.
private static readonly Meter meter = new("OTel.AzureMonitor.Demo");
public static void Main()
{
// Create a new MeterProvider object using the OpenTelemetry SDK.
// The MeterProvider object is responsible for managing meters and sending
// metric data to exporters.
// It is important to keep the MetricsProvider instance active
// throughout the process lifetime.
//
// The MeterProviderBuilder is configured to add a meter named
// "OTel.AzureMonitor.Demo" and an Azure Monitor metric exporter.
using var meterProvider = Sdk.CreateMeterProviderBuilder()
.AddMeter("OTel.AzureMonitor.Demo")
.AddAzureMonitorMetricExporter()
.Build();
// Get the current process.
var process = Process.GetCurrentProcess();
// Create a new observable gauge metric named "Thread.State".
// This metric will track the state of each thread in the current process.
ObservableGauge<int> myObservableGauge = meter.CreateObservableGauge("Thread.State", () => GetThreadState(process));
// Display a message to the user and wait for them to press Enter.
// This allows the user to see the message and the console before the
// application exits.
System.Console.WriteLine("Press Enter key to exit.");
System.Console.ReadLine();
}
private static IEnumerable<Measurement<int>> GetThreadState(Process process)
{
// Iterate over all threads in the current process.
foreach (ProcessThread thread in process.Threads)
{
// Create a measurement for each thread, including the thread state, process ID, and thread ID.
yield return new((int)thread.ThreadState, new("ProcessId", process.Id), new("ThreadId", thread.Id));
}
}
}
import io.opentelemetry.api.GlobalOpenTelemetry;
import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.common.Attributes;
import io.opentelemetry.api.metrics.Meter;
public class Program {
public static void main(String[] args) {
Meter meter = GlobalOpenTelemetry.getMeter("OTEL.AzureMonitor.Demo");
meter.gaugeBuilder("gauge")
.buildWithCallback(
observableMeasurement -> {
double randomNumber = Math.floor(Math.random() * 100);
observableMeasurement.record(randomNumber, Attributes.of(AttributeKey.stringKey("testKey"), "testValue"));
});
}
}
注入 OpenTelemetry:
创建仪表:
import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.common.Attributes;
import io.opentelemetry.api.metrics.Meter;
Meter meter = openTelemetry.getMeter("OTEL.AzureMonitor.Demo");
meter.gaugeBuilder("gauge")
.buildWithCallback(
observableMeasurement -> {
double randomNumber = Math.floor(Math.random() * 100);
observableMeasurement.record(randomNumber, Attributes.of(AttributeKey.stringKey("testKey"), "testValue"));
});
注意
Quarkus 社区支持和维护 Quarkus 扩展。 寻求帮助,请使用 Quarkus 社区支持渠道。 Azure不提供对此集成的技术支持。
export class GaugeSample {
static async run() {
// Dynamically import Azure Monitor and metrics API
const { useAzureMonitor } = await import("@azure/monitor-opentelemetry");
const { metrics } = await import("@opentelemetry/api");
// Initialize Azure Monitor
const monitor = useAzureMonitor({
azureMonitorExporterOptions: {
connectionString:
process.env.APPLICATIONINSIGHTS_CONNECTION_STRING || "<YOUR-CONNECTION-STRING>",
},
});
// Create an observable gauge and register a callback
const meter = metrics.getMeter("testMeter");
const gauge = meter.createObservableGauge("gauge");
gauge.addCallback((observableResult) => {
const randomNumber = Math.floor(Math.random() * 100);
observableResult.observe(randomNumber, { testKey: "testValue" });
});
console.log("Observable gauge registered");
}
}
# Import the necessary packages.
from typing import Iterable
import os
from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry import metrics
from opentelemetry.metrics import CallbackOptions, Observation
# 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>",
)
# Opt in to allow grouping of your metrics via a custom metrics namespace in app insights metrics explorer.
# Specify the namespace name using get_meter("namespace-name")
os.environ["APPLICATIONINSIGHTS_METRIC_NAMESPACE_OPT_IN"] = "true"
# Get a meter provider and a meter with the name "otel_azure_monitor_gauge_demo".
meter = metrics.get_meter_provider().get_meter("otel_azure_monitor_gauge_demo")
# Define two observable gauge generators.
# The first generator yields a single observation with the value 9.
# The second generator yields a sequence of 10 observations with the value 9 and a different dimension value for each observation.
def observable_gauge_generator(options: CallbackOptions) -> Iterable[Observation]:
yield Observation(9, {"test_key": "test_value"})
def observable_gauge_sequence(options: CallbackOptions) -> Iterable[Observation]:
observations = []
for i in range(10):
observations.append(
Observation(9, {"test_key": i})
)
return observations
# Create two observable gauges using the defined generators.
gauge = meter.create_observable_gauge("gauge", [observable_gauge_generator])
gauge2 = meter.create_observable_gauge("gauge2", [observable_gauge_sequence])
# Wait for background execution.
input()
添加自定义异常
选择的检测库会自动将异常情况报告给 Application Insights。
不过,你可能希望手动报告一些仪器库未报告的例外情况。
例如,通常不会报告代码捕获的异常。 你可能希望报告这些问题,以便在相关体验中引起注意,尤其是在故障部分和端到端事务视图中。
要使用活动记录异常事件:
// Start a new activity named "ExceptionExample".
using (var activity = activitySource.StartActivity("ExceptionExample"))
{
// Try to execute some code.
try
{
throw new Exception("Test exception");
}
// If an exception is thrown, catch it and set the activity status to "Error".
catch (Exception ex)
{
activity?.SetStatus(ActivityStatusCode.Error);
activity?.RecordException(ex);
}
}
要使用 ILogger 记录异常:
// Create a logger using the logger factory. The logger category name is used to filter and route log messages.
var logger = loggerFactory.CreateLogger(logCategoryName);
// Try to execute some code.
try
{
throw new Exception("Test Exception");
}
catch (Exception ex)
{
// Log an error message with the exception. The log level is set to "Error" and the event ID is set to 0.
// The log message includes a template and a parameter. The template will be replaced with the value of the parameter when the log message is written.
logger.Log(
logLevel: LogLevel.Error,
eventId: 0,
exception: ex,
message: "Hello {name}.",
args: new object[] { "World" });
}
要使用活动记录异常事件:
// Start a new activity named "ExceptionExample".
using (var activity = activitySource.StartActivity("ExceptionExample"))
{
// Try to execute some code.
try
{
throw new Exception("Test exception");
}
// If an exception is thrown, catch it and set the activity status to "Error".
catch (Exception ex)
{
activity?.SetStatus(ActivityStatusCode.Error);
activity?.RecordException(ex);
}
}
要使用 ILogger 记录异常:
// Create a logger using the logger factory. The logger category name is used to filter and route log messages.
var logger = loggerFactory.CreateLogger("ExceptionExample");
try
{
// Try to execute some code.
throw new Exception("Test Exception");
}
catch (Exception ex)
{
// Log an error message with the exception. The log level is set to "Error" and the event ID is set to 0.
// The log message includes a template and a parameter. The template will be replaced with the value of the parameter when the log message is written.
logger.Log(
logLevel: LogLevel.Error,
eventId: 0,
exception: ex,
message: "Hello {name}.",
args: new object[] { "World" });
}
可以使用 opentelemetry-api 来更新跨度的状态并记录异常。
将 opentelemetry-api-1.0.0.jar(或更高版本)添加到应用程序:
<dependency>
<groupId>io.opentelemetry</groupId>
<artifactId>opentelemetry-api</artifactId>
<version>1.0.0</version>
</dependency>
将状态设置为 error,并记录代码中的异常:
import io.opentelemetry.api.trace.Span;
import io.opentelemetry.api.trace.StatusCode;
Span span = Span.current();
span.setStatus(StatusCode.ERROR, "errorMessage");
span.recordException(e);
将状态设置为 error,并记录代码中的异常:
import io.opentelemetry.api.trace.Span;
import io.opentelemetry.api.trace.StatusCode;
Span span = Span.current();
span.setStatus(StatusCode.ERROR, "errorMessage");
span.recordException(e);
仅当记录在顶级跨度或远程或内部跨度的子级时,Node.js SDK 才会将基于跨度的手动记录异常导出到 Application Insights。
export class CustomExceptionSample {
static async run() {
// Dynamically import Azure Monitor and tracing API
const { useAzureMonitor } = await import("@azure/monitor-opentelemetry");
const { trace } = await import("@opentelemetry/api");
// Initialize Azure Monitor
const monitor = useAzureMonitor({
azureMonitorExporterOptions: {
connectionString:
process.env.APPLICATIONINSIGHTS_CONNECTION_STRING || "<YOUR-CONNECTION-STRING>",
},
});
// Create a span and record an exception
const tracer = trace.getTracer("testTracer");
const span = tracer.startSpan("hello");
try {
throw new Error("Test Error");
} catch (error) {
span.recordException(error as Error);
} finally {
span.end();
}
console.log("Exception recorded on span");
}
}
OpenTelemetry Python SDK 以自动捕获和记录引发的异常的方式实现。 有关此行为的示例,请参阅以下代码示例:
# Import the necessary packages.
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>",
)
# Get a tracer for the current module.
tracer = trace.get_tracer("otel_azure_monitor_exception_demo")
# Exception events
try:
# Start a new span with the name "hello".
with tracer.start_as_current_span("hello") as span:
# This exception will be automatically recorded
raise Exception("Custom exception message.")
except Exception:
print("Exception raised")
如果要手动记录异常,可以在上下文管理器中禁用该选项,并直接使用 record_exception(),如下例所示:
...
# Start a new span with the name "hello" and disable exception recording.
with tracer.start_as_current_span("hello", record_exception=False) as span:
try:
# Raise an exception.
raise Exception("Custom exception message.")
except Exception as ex:
# Manually record exception
span.record_exception(ex)
...
添加自定义范围
你可能想要在两种情况下添加自定义跨度。 首先,当存在仪器库尚未收集的依赖请求时。 其次,当你希望在端到端事务视图上将应用程序进程建模为跨度时。
注意
Activity 命名空间中的 ActivitySource 和 System.Diagnostics 类分别代表 Span 和 Tracer 的 OpenTelemetry 概念。 可以通过使用其构造函数而不是使用 ActivitySource 来直接创建 TracerProvider。 每个 ActivitySource 类都必须显式地通过 TracerProvider 来连接到 AddSource()。 这是因为 OpenTelemetry 跟踪 API 的某些部分直接合并到.NET运行时中。 若要了解详细信息,请参阅 OpenTelemetry .NET 跟踪 API 的简介。
// Define an activity source named "ActivitySourceName". This activity source will be used to create activities for all requests to the application.
internal static readonly ActivitySource activitySource = new("ActivitySourceName");
// Create an ASP.NET Core application builder.
var builder = WebApplication.CreateBuilder(args);
// Configure the OpenTelemetry tracer provider to add a source named "ActivitySourceName". This will ensure that all activities created by the activity source are traced.
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();
// Map a GET request to the root path ("/") to the specified action.
app.MapGet("/", () =>
{
// Start a new activity named "CustomActivity". This activity will be traced and the trace data will be sent to Azure Monitor.
using (var activity = activitySource.StartActivity("CustomActivity"))
{
// your code here
}
// Return a response message.
return $"Hello World!";
});
// Start the ASP.NET Core application.
app.Run();
StartActivity 默认为 ActivityKind.Internal,但你可以提供任何其他 ActivityKind。
ActivityKind.Client、ActivityKind.Producer、ActivityKind.Internal 映射到 Application Insights dependencies。
ActivityKind.Server、ActivityKind.Consumer 映射到 Application Insights requests。
注意
Activity 命名空间中的 ActivitySource 和 System.Diagnostics 类分别代表 Span 和 Tracer 的 OpenTelemetry 概念。 可以通过使用其构造函数而不是使用 ActivitySource 来直接创建 TracerProvider。 每个 ActivitySource 类都必须显式地通过 TracerProvider 来连接到 AddSource()。 这是因为 OpenTelemetry 跟踪 API 的某些部分直接合并到.NET运行时中。 若要了解详细信息,请参阅 OpenTelemetry .NET 跟踪 API 的简介。
// 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("ActivitySourceName")
.AddAzureMonitorTraceExporter()
.Build();
// Create an activity source named "ActivitySourceName".
var activitySource = new ActivitySource("ActivitySourceName");
// Start a new activity named "CustomActivity". This activity will be traced and the trace data will be sent to Azure Monitor.
using (var activity = activitySource.StartActivity("CustomActivity"))
{
// your code here
}
StartActivity 默认为 ActivityKind.Internal,但你可以提供任何其他 ActivityKind。
ActivityKind.Client、ActivityKind.Producer、ActivityKind.Internal 映射到 Application Insights dependencies。
ActivityKind.Server、ActivityKind.Consumer 映射到 Application Insights requests。
使用 OpenTelemetry 注解
添加自己的范围的最简单方法是使用 OpenTelemetry 的 @WithSpan 注释。
范围会填充 Application Insights 中的 requests 和 dependencies 表。
将 opentelemetry-instrumentation-annotations-1.32.0.jar(或更高版本)添加到应用程序:
<dependency>
<groupId>io.opentelemetry.instrumentation</groupId>
<artifactId>opentelemetry-instrumentation-annotations</artifactId>
<version>1.32.0</version>
</dependency>
使用 @WithSpan 标记,每次执行你的方法时发出一个范围:
import io.opentelemetry.instrumentation.annotations.WithSpan;
@WithSpan(value = "your span name")
public void yourMethod() {
}
默认情况下,跨度会显示在 dependencies 表中,依赖项类型为 InProc。
对于表示自动仪表化未捕获的后台作业的方法,我们建议将 kind = SpanKind.SERVER 属性应用于 @WithSpan 注释,以确保它们会显示在 Application Insights requests 表中。
使用 OpenTelemetry API
如果上述 OpenTelemetry @WithSpan 注释不能满足你的需求,可使用 OpenTelemetry API 添加范围。
将 opentelemetry-api-1.0.0.jar(或更高版本)添加到应用程序:
<dependency>
<groupId>io.opentelemetry</groupId>
<artifactId>opentelemetry-api</artifactId>
<version>1.0.0</version>
</dependency>
使用 GlobalOpenTelemetry 类创建 Tracer:
import io.opentelemetry.api.GlobalOpenTelemetry;
import io.opentelemetry.api.trace.Tracer;
static final Tracer tracer = GlobalOpenTelemetry.getTracer("com.example");
创建一个范围,使其成为当前范围,然后结束该范围:
Span span = tracer.spanBuilder("my first span").startSpan();
try (Scope ignored = span.makeCurrent()) {
// do stuff within the context of this
} catch (Throwable t) {
span.recordException(t);
} finally {
span.end();
}
注入 OpenTelemetry:
创建 Tracer:
import io.opentelemetry.api.trace.Tracer;
static final Tracer tracer = openTelemetry.getTracer("com.example");
创建一个范围,使其成为当前范围,然后结束该范围:
Span span = tracer.spanBuilder("my first span").startSpan();
try (Scope ignored = span.makeCurrent()) {
// do stuff within the context of this
} catch (Throwable t) {
span.recordException(t);
} finally {
span.end();
}
注意
Quarkus 社区支持和维护 Quarkus 扩展。 寻求帮助,请使用 Quarkus 社区支持渠道。 Azure不提供对此集成的技术支持。
export class CustomTraceSample {
static async run() {
// Dynamically import Azure Monitor and tracing API
const { useAzureMonitor } = await import("@azure/monitor-opentelemetry");
const { trace } = await import("@opentelemetry/api");
// Initialize Azure Monitor
const monitor = useAzureMonitor({
azureMonitorExporterOptions: {
connectionString:
process.env.APPLICATIONINSIGHTS_CONNECTION_STRING || "<YOUR-CONNECTION-STRING>",
},
});
// Create a custom span, add attributes/events, then end
const tracer = trace.getTracer("otel_azure_monitor_custom_trace_demo");
const span = tracer.startSpan("doWork");
try {
span.setAttribute("component", "worker");
span.setAttribute("operation.id", "42");
span.addEvent("invoking doWork");
for (let i = 0; i < 1_000_000; i++) { /* simulate work */ }
} catch (err) {
span.recordException(err as Error);
} finally {
span.end();
}
console.log("Custom span recorded");
}
}
OpenTelemetry API 可用于添加你自己的跨度,它们会显示在 Application Insights 的 requests 和 dependencies 表中。
代码示例演示如何使用 tracer.start_as_current_span() 方法在其上下文中启动跨度、将跨度设为当前,以及结束跨度。
...
# Import the necessary packages.
from opentelemetry import trace
# Get a tracer for the current module.
tracer = trace.get_tracer(__name__)
# Start a new span with the name "my first span" and make it the current span.
# The "with" context manager starts, makes the span current, and ends the span within it's context
with tracer.start_as_current_span("my first span") as span:
try:
# Do stuff within the context of this span.
# All telemetry generated within this scope will be attributed to this span.
except Exception as ex:
# Record the exception on the span.
span.record_exception(ex)
...
默认情况下,跨度位于 dependencies 表中,依赖项类型为 InProc。
如果您的方法代表自动化检测尚未捕获的某个后台作业,我们建议设置特性 kind = SpanKind.SERVER,以确保它出现在 Application Insights requests 表中。
...
# Import the necessary packages.
from opentelemetry import trace
from opentelemetry.trace import SpanKind
# Get a tracer for the current module.
tracer = trace.get_tracer(__name__)
# Start a new span with the name "my request span" and the kind set to SpanKind.SERVER.
with tracer.start_as_current_span("my request span", kind=SpanKind.SERVER) as span:
# Do stuff within the context of this span.
...
发送自定义事件
Application Insights 将自定义事件 customEvents 存储在表中。
分析、筛选和可视化它们的一种方法是使用 Application Insights 使用情况体验。
如果要自动收集客户端交互事件,可以在 JavaScript SDK 中使用插件。
自定义事件使用 Azure.Monitor.OpenTelemetry.AspNetCore。
若要使用 CustomEvent 发送 ILogger,请设置消息模板中的 "microsoft.custom_event.name" 属性。
// Create a logger factory and configure OpenTelemetry with Azure Monitor
var loggerFactory = LoggerFactory.Create(builder =>
{
builder
.AddOpenTelemetry(options =>
{
options.AddAzureMonitorLogExporter();
});
});
// Create a logger for the specified category
var logger = loggerFactory.CreateLogger(logCategoryName);
// Log a custom event with a custom name and additional attribute
// The 'microsoft.custom_event.name' value will be used as the name of the customEvent
logger.LogInformation("{microsoft.custom_event.name} {additional_attrs}", "test-event-name", "val1");
自定义事件使用 Azure.Monitor.OpenTelemetry.Exporter。
若要使用 CustomEvent 发送 ILogger,请设置消息模板中的 "microsoft.custom_event.name" 属性。
// Create a logger factory and configure OpenTelemetry with Azure Monitor
var loggerFactory = LoggerFactory.Create(builder =>
{
builder
.AddOpenTelemetry(options =>
{
options.AddAzureMonitorLogExporter();
});
});
// Create a logger for the specified category
var logger = loggerFactory.CreateLogger(logCategoryName);
// Log a custom event with a custom name and additional attribute
// The 'microsoft.custom_event.name' value will be used as the name of the customEvent
logger.LogInformation("{microsoft.custom_event.name} {additional_attrs}", "test-event-name", "val1");
若要使用 Java 代理发送 customEvent,请在 OpenTelemetry 日志记录上设置 "microsoft.custom_event.name" 属性。
根据是否启用了 Application Insights Java 代理或自动配置 SDK,获取 OpenTelemetry 记录器的方式略有不同。 以下示例进一步介绍了此详细信息。
对于 Application Insights Java 代理:
import io.opentelemetry.api.GlobalOpenTelemetry;
import io.opentelemetry.api.logs.Logger;
import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.logs.Severity;
Logger logger = GlobalOpenTelemetry.get().getLogsBridge().get("opentelemetry-logger");
logger.logRecordBuilder()
.setAttribute(AttributeKey.stringKey("microsoft.custom_event.name"),"test-event-name")
.setSeverity(Severity.INFO)
.emit();
对于自动配置 SDK:
import com.azure.monitor.opentelemetry.autoconfigure.AzureMonitorAutoConfigure;
import com.azure.monitor.opentelemetry.autoconfigure.AzureMonitorAutoConfigureOptions;
import io.opentelemetry.api.OpenTelemetry;
import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.logs.Logger;
import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdk;
import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdkBuilder;
AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorAutoConfigureOptions options = new AzureMonitorAutoConfigureOptions();
options.connectionString("<YOUR-CONNECTION-STRING>");
AzureMonitorAutoConfigure.customize(sdkBuilder, options);
OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();
Logger logger = openTelemetry.getLogsBridge().get("opentelemetry-logger");
logger.logRecordBuilder()
.setAttribute(AttributeKey.stringKey("microsoft.custom_event.name"),"test-event-name")
.setSeverity(Severity.INFO)
.emit();
若要可靠地发出自定义事件,请直接使用 OpenTelemetry API。 某些日志记录框架不支持追加或分析自定义事件属性。
在Java native中无法使用 customEvent 属性发送 "microsoft.custom_event.name"。
若要使用customEvent发送logger.emit,请在日志"microsoft.custom_event.name"对象中设置attributes属性。 还可以根据需要包含其他属性。
export class CustomEventSample {
static async run() {
// Dynamically import Azure Monitor and the OpenTelemetry logs API
const { useAzureMonitor } = await import("@azure/monitor-opentelemetry");
const { logs, SeverityNumber } = await import("@opentelemetry/api-logs");
// Initialize Azure Monitor (enables logs bridge)
const monitor = useAzureMonitor();
// Get a logger and emit a customEvent by setting the microsoft attribute key
const logger = logs.getLogger("my-app-logger");
logger.emit({
body: "Hello World!",
severityNumber: SeverityNumber.INFO,
attributes: {
"microsoft.custom_event.name": "test-event-name",
"additional_attrs": "val1",
},
});
// Example: populate client_IP via attribute 'client.address'
logger.emit({
body: "This entry will have a custom client_IP",
severityNumber: SeverityNumber.INFO,
attributes: {
"microsoft.custom_event.name": "test_event",
"client.address": "192.168.1.1",
},
});
console.log("Custom events emitted");
}
}
若要在 Python 中发送 customEvent,请将日志记录库与 "microsoft.custom_event.name" 参数中的 extra 属性一起使用。
import logging
from azure.monitor.opentelemetry import configure_azure_monitor
# Set up your application logger
logger = logging.getLogger("my-app-logger")
# Configure Azure Monitor to collect logs from the specified logger name
configure_azure_monitor(
logger_name="my-app-logger", # Collect logs from your namespaced logger
)
# Log a custom event with a custom name and additional attribute
# The 'microsoft.custom_event.name' value will be used as the name of the customEvent
logger.warning(
"Hello World!",
extra={
"microsoft.custom_event.name": "test-event-name",
"additional_attrs": "val1"
}
)
# You can also populate fields like client_IP with attribute `client.address`
logger.info(
"This entry will have a custom client_IP",
extra={
"microsoft.custom_event.name": "test_event",
"client.address": "192.168.1.1"
}
)
修改遥测
本部分介绍如何修改遥测。
添加范围属性
这些属性可能包括向遥测添加自定义属性。 还可以使用属性来设置 Application Insights 架构中的可选字段,如客户端 IP。
将自定义属性添加到 Span
你添加到范围的任何属性都将导出为自定义属性。 它们将填充请求、依赖项、跟踪或异常表中的 customDimensions 字段。
若要添加范围属性,请使用以下两种方法之一:
许多检测库都提供扩充选项。 有关指南,请参阅各个检测库的自述文件:
使用自定义处理器:
提示
添加此处显示的处理器之前添加Azure Monitor。
// Create an ASP.NET Core application builder.
var builder = WebApplication.CreateBuilder(args);
// Configure the OpenTelemetry tracer provider to add a new processor named ActivityEnrichingProcessor.
builder.Services.ConfigureOpenTelemetryTracerProvider((sp, builder) => builder.AddProcessor(new ActivityEnrichingProcessor()));
// 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 ActivityEnrichingProcessor.cs 添加到你的项目:
public class ActivityEnrichingProcessor : BaseProcessor<Activity>
{
public override void OnEnd(Activity activity)
{
// The updated activity will be available to all processors which are called after this processor.
activity.DisplayName = "Updated-" + activity.DisplayName;
activity.SetTag("CustomDimension1", "Value1");
activity.SetTag("CustomDimension2", "Value2");
}
}
若要添加范围属性,请使用以下两种方法之一:
提示
使用检测库提供的选项(当它们可用时)的优点是整个上下文都可用。 因此,用户可以选择添加或筛选更多属性。 例如,HttpClient 检测库中的扩展功能让用户可以访问 httpRequestMessage 自身。 他们可以从中选择任何内容并将其存储为属性。
许多检测库都提供扩充选项。 有关指南,请参阅各个检测库的自述文件:
使用自定义处理器:
提示
添加此处显示的处理器之前Azure Monitor导出程序。
// Create an OpenTelemetry tracer provider builder.
// It is important to keep the TracerProvider instance active throughout the process lifetime.
using var tracerProvider = Sdk.CreateTracerProviderBuilder()
// Add a source named "OTel.AzureMonitor.Demo".
.AddSource("OTel.AzureMonitor.Demo") // Add a new processor named ActivityEnrichingProcessor.
.AddProcessor(new ActivityEnrichingProcessor()) // Add the Azure Monitor trace exporter.
.AddAzureMonitorTraceExporter() // Add the Azure Monitor trace exporter.
.Build();
使用以下代码将 ActivityEnrichingProcessor.cs 添加到你的项目:
public class ActivityEnrichingProcessor : BaseProcessor<Activity>
{
// The OnEnd method is called when an activity is finished. This is the ideal place to enrich the activity with additional data.
public override void OnEnd(Activity activity)
{
// Update the activity's display name.
// The updated activity will be available to all processors which are called after this processor.
activity.DisplayName = "Updated-" + activity.DisplayName;
// Set custom tags on the activity.
activity.SetTag("CustomDimension1", "Value1");
activity.SetTag("CustomDimension2", "Value2");
}
}
可以使用 opentelemetry-api 向范围中添加属性。
添加一个或多个范围属性会填充 customDimensions、requests、dependencies 或 traces 表中的 exceptions 字段。
将 opentelemetry-api-1.0.0.jar(或更高版本)添加到应用程序:
<dependency>
<groupId>io.opentelemetry</groupId>
<artifactId>opentelemetry-api</artifactId>
<version>1.0.0</version>
</dependency>
在代码中添加自定义维度:
import io.opentelemetry.api.trace.Span;
import io.opentelemetry.api.common.AttributeKey;
AttributeKey attributeKey = AttributeKey.stringKey("mycustomdimension");
Span.current().setAttribute(attributeKey, "myvalue1");
在代码中添加自定义维度:
import io.opentelemetry.api.trace.Span;
import io.opentelemetry.api.common.AttributeKey;
AttributeKey attributeKey = AttributeKey.stringKey("mycustomdimension");
Span.current().setAttribute(attributeKey, "myvalue1");
export class SpanAttributeEnrichmentSample {
static async run() {
// Dynamically import the Azure Monitor integration
const { useAzureMonitor } = await import("@azure/monitor-opentelemetry");
// Create a SpanEnrichingProcessor to add custom dimensions
class SpanEnrichingProcessor {
forceFlush() { return Promise.resolve(); }
shutdown() { return Promise.resolve(); }
onStart() {}
onEnd(span: any) {
(span as any).attributes = (span as any).attributes || {};
(span as any).attributes["CustomDimension1"] = "value1";
(span as any).attributes["CustomDimension2"] = "value2";
}
}
// Initialize Azure Monitor with the custom processor
const monitor = useAzureMonitor({
spanProcessors: [new SpanEnrichingProcessor()],
});
console.log("Span enrichment processor registered");
}
}
使用自定义处理器:
...
# Import the necessary packages.
from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry import trace
# Create a SpanEnrichingProcessor instance.
span_enrich_processor = SpanEnrichingProcessor()
# 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 enrich processor.
span_processors=[span_enrich_processor],
)
...
使用以下代码将 SpanEnrichingProcessor 添加到你的项目:
# Import the SpanProcessor class from the opentelemetry.sdk.trace module.
from opentelemetry.sdk.trace import SpanProcessor
class SpanEnrichingProcessor(SpanProcessor):
def on_end(self, span):
# Prefix the span name with the string "Updated-".
span._name = "Updated-" + span.name
# Add the custom dimension "CustomDimension1" with the value "Value1".
span._attributes["CustomDimension1"] = "Value1"
# Add the custom dimension "CustomDimension2" with the value "Value2".
span._attributes["CustomDimension2"] = "Value2"
设置用户 IP
可以通过在 span 上设置属性来填充请求的 client_IP 字段。 Application Insights 使用 IP 地址生成用户位置属性,然后默认放弃它。
使用自定义属性示例,但替换 ActivityEnrichingProcessor.cs 中的以下代码行:
// Add the client IP address to the activity as a tag.
// only applicable in case of activity.Kind == Server
activity.SetTag("client.address", "<IP Address>");
使用自定义属性示例,但替换 ActivityEnrichingProcessor.cs 中的以下代码行:
// Add the client IP address to the activity as a tag.
// only applicable in case of activity.Kind == Server
activity.SetTag("client.address", "<IP Address>");
使用自定义属性示例,但替换以下代码行:
export class SetUserIpSample {
static async run() {
// Dynamically import Azure Monitor and tracing API
const { useAzureMonitor } = await import("@azure/monitor-opentelemetry");
const { trace } = await import("@opentelemetry/api");
// Initialize Azure Monitor
const monitor = useAzureMonitor();
// Framework-agnostic helper to set client IP on the active server span
const setIpForRequest = (clientIp: string) => {
const span = trace.getActiveSpan();
if (span) {
// Preferred attribute for client IP
span.setAttribute("client.address", clientIp);
// Optional: legacy/alternate attribute
span.setAttribute("http.client_ip", clientIp);
}
};
// Call setIpForRequest("<IP Address>") from within your web framework's request pipeline
console.log("Use setIpForRequest('<IP Address>') inside your request handler to stamp the active span.");
}
}
使用自定义属性示例,但替换 SpanEnrichingProcessor.py 中的以下代码行:
# Set the `http.client_ip` attribute of the span to the specified IP address.
span._attributes["http.client_ip"] = "<IP Address>"
设置用户 ID 或经过身份验证的用户 ID
可以使用以下指导填充请求的 user_Id 或 user_AuthenticatedId 字段。 用户 ID 是匿名用户标识符。 经过身份验证的用户 ID 是已知的用户标识符。
重要
在设置经过身份验证的用户 ID 之前,请参考适用的隐私法律。
使用自定义属性示例:
// Add the user ID to the activity as a tag, but only if the activity is not null.
activity?.SetTag("enduser.id", "<User Id>");
使用自定义属性示例:
// Add the user ID to the activity as a tag, but only if the activity is not null.
activity?.SetTag("enduser.id", "<User Id>");
填充 user ID、requests 或 dependencies 表中的 exceptions 字段。
将 opentelemetry-api-1.0.0.jar(或更高版本)添加到应用程序:
<dependency>
<groupId>io.opentelemetry</groupId>
<artifactId>opentelemetry-api</artifactId>
<version>1.0.0</version>
</dependency>
在代码中设置 user_Id:
import io.opentelemetry.api.trace.Span;
Span.current().setAttribute("enduser.id", "myuser"); // (user_AuthenticatedId)
Span.current().setAttribute("enduser.pseudo.id", "myuser"); // (user_Id)
填充 user ID、requests 或 dependencies 表中的 exceptions 字段。
在代码中设置 user_Id:
import io.opentelemetry.api.trace.Span;
Span.current().setAttribute("enduser.id", "myuser"); // (user_AuthenticatedId)
Span.current().setAttribute("enduser.pseudo.id", "myuser"); // (user_Id)
使用自定义属性示例,但替换以下代码行:
export class SetUserIdSample {
static async run() {
// Dynamically import Azure Monitor and tracing API
const { useAzureMonitor } = await import("@azure/monitor-opentelemetry");
const { trace } = await import("@opentelemetry/api");
// Initialize Azure Monitor
const monitor = useAzureMonitor();
// Framework-agnostic helper to set user identifiers on the active server span
const setUserForRequest = (authenticatedId?: string, anonymousId?: string) => {
const span = trace.getActiveSpan();
if (span) {
if (authenticatedId) span.setAttribute("enduser.id", authenticatedId); // user_AuthenticatedId
if (anonymousId) span.setAttribute("enduser.pseudo.id", anonymousId); // user_Id
}
};
// Call setUserForRequest("<authenticated-id>", "<anonymous-id>") inside your request handler
console.log("Use setUserForRequest('<auth-id>', '<anon-id>') inside your request handler to stamp the active span.");
}
}
使用自定义属性示例,但替换以下代码行:
# Set the `enduser.id` attribute of the span to the specified user ID.
span._attributes["enduser.id"] = "<User ID>"
添加日志属性
OpenTelemetry 使用 .NET 的 ILogger。
可以使用消息模板将自定义维度附加到日志。
OpenTelemetry 使用 .NET 的 ILogger。
可以使用消息模板将自定义维度附加到日志。
会自动检测 Logback、Log4j 和 java.util.logging。 可通过以下方式将自定义维度附加到日志:
对于 Spring Boot 原生应用程序,Logback 是开箱即用的。
export class BunyanLogAttributesSample {
static async run() {
// Dynamically import Azure Monitor and Bunyan
const { useAzureMonitor } = await import("@azure/monitor-opentelemetry");
const bunyanMod = await import("bunyan");
const bunyan = (bunyanMod as any).default ?? bunyanMod;
// Enable Azure Monitor integration and bunyan instrumentation
const monitor = useAzureMonitor({
instrumentationOptions: { bunyan: { enabled: true } },
});
// Emit a log with custom attributes
const log = (bunyan as any).createLogger({ name: "testApp" });
log.info({ key1: "value1", feature: "demo" }, "Warning log with properties");
console.log("Bunyan log with attributes emitted");
}
}
Python logging 库autoinstrumented。 通过将一个字典传递到日志的 extra 参数,可以将自定义维度附加到日志中:
...
# Create a warning log message with the properties "key1" and "value1".
logger.warning("WARNING: Warning log with properties", extra={"key1": "value1"})
...
覆盖 HTTP 4xx 响应中的请求错误状态
可以阻止 Application Insights 将 4xx 响应计数为错误。
以下代码显示了将 HTTP 4xx 响应标记为成功的自定义活动处理器。
处理器:
public class Http4xxSuccessProcessor : BaseProcessor<Activity>
{
public override void OnEnd(Activity activity)
{
if (activity.Kind == ActivityKind.Server)
{
var statusCodeTag = activity.GetTagItem("http.response.status_code");
if (statusCodeTag is int statusCode && statusCode >= 400 && statusCode < 500)
{
// Set status to Ok to bypass the Azure Monitor exporter's default logic
// which treats any HTTP 4xx as failure when status is Unset.
// The response code tag (e.g., 400) remains unchanged — only the
// success field in Application Insights is affected.
activity.SetStatus(ActivityStatusCode.Ok);
}
}
base.OnEnd(activity);
}
}
注册:
builder.Services.AddOpenTelemetry()
.UseAzureMonitor(options =>
{
options.ConnectionString = "<your-connection-string>";
})
.WithTracing(tracing =>
{
// Add custom processor to mark 4xx responses as successful
tracing.AddProcessor<Http4xxSuccessProcessor>();
});
以下代码适用于将 HTTP 4xx 响应标记为成功的自定义活动处理器。
处理器:
public class Http4xxSuccessProcessor : BaseProcessor<Activity>
{
public override void OnEnd(Activity activity)
{
if (activity.Kind == ActivityKind.Server)
{
var statusCodeTag = activity.GetTagItem("http.response.status_code");
if (statusCodeTag is int statusCode && statusCode >= 400 && statusCode < 500)
{
// Set status to Ok to bypass the Azure Monitor exporter's default logic
// which treats any HTTP 4xx as failure when status is Unset.
// The response code tag (e.g., 400) remains unchanged — only the
// success field in Application Insights is affected.
activity.SetStatus(ActivityStatusCode.Ok);
}
}
base.OnEnd(activity);
}
}
注册:
using System.Diagnostics;
using OpenTelemetry;
using OpenTelemetry.Trace;
using var tracerProvider = Sdk.CreateTracerProviderBuilder()
// Add the processor before the exporter
.AddProcessor(new Http4xxSuccessProcessor())
.AddAzureMonitorTraceExporter(options =>
{
options.ConnectionString = "<your-connection-string>";
})
.Build();
注意
此功能从 Java 代理版本 3.3.0 开始可用。
默认情况下,代理将捕获导致 4xx 响应代码为错误的 HTTP 服务器请求。 可以更改此行为,使其视为成功:
{
"preview": {
"captureHttpServer4xxAsError": false
}
}
处理器:
import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.trace.SpanKind;
import io.opentelemetry.api.trace.StatusCode;
import io.opentelemetry.context.Context;
import io.opentelemetry.sdk.common.CompletableResultCode;
import io.opentelemetry.sdk.trace.ReadWriteSpan;
import io.opentelemetry.sdk.trace.ReadableSpan;
import io.opentelemetry.sdk.trace.internal.ExtendedSpanProcessor;
import java.util.Set;
public final class Http4xxAsSuccessProcessor implements ExtendedSpanProcessor {
private static final AttributeKey<Long> HTTP_RESPONSE_STATUS_CODE =
AttributeKey.longKey("http.response.status_code");
// Deprecated semconv attribute that some libraries may still emit
private static final AttributeKey<Long> HTTP_STATUS_CODE =
AttributeKey.longKey("http.status_code");
private static final AttributeKey<Boolean> MODIFIED_FLAG =
AttributeKey.booleanKey("ai.modified.http4xx.success");
private final Set<Long> keepAsFailure = Set.of(429L);
@Override
public void onStart(Context parentContext, ReadWriteSpan span) {
// No-op
}
@Override
public boolean isStartRequired() {
return false;
}
@Override
public void onEnding(ReadWriteSpan span) {
if (span.getKind() != SpanKind.SERVER) {
return;
}
Long statusCode = span.getAttribute(HTTP_RESPONSE_STATUS_CODE);
if (statusCode == null) {
statusCode = span.getAttribute(HTTP_STATUS_CODE);
}
if (statusCode != null
&& statusCode >= 400
&& statusCode < 500
&& !keepAsFailure.contains(statusCode)) {
// Keep the HTTP status code attribute unchanged, but mark the span as OK
// so downstream backends such as Application Insights don't count it as a failure.
span.setStatus(StatusCode.OK);
span.setAttribute(MODIFIED_FLAG, true);
}
}
@Override
public boolean isOnEndingRequired() {
return true;
}
@Override
public void onEnd(ReadableSpan span) {
// No-op
}
@Override
public boolean isEndRequired() {
return false;
}
@Override
public CompletableResultCode shutdown() {
return CompletableResultCode.ofSuccess();
}
@Override
public CompletableResultCode forceFlush() {
return CompletableResultCode.ofSuccess();
}
}
注册(Spring Boot):
import com.azure.monitor.opentelemetry.autoconfigure.AzureMonitorAutoConfigure;
import io.opentelemetry.api.OpenTelemetry;
import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdk;
import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdkBuilder;
public class TelemetryConfiguration {
public static OpenTelemetry configureOpenTelemetry() {
AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorAutoConfigure.customize(sdkBuilder, "<your-connection-string>");
sdkBuilder.addTracerProviderCustomizer(
(tracerProviderBuilder, configProperties) ->
tracerProviderBuilder.addSpanProcessor(new Http4xxAsSuccessProcessor())
);
return sdkBuilder.build().getOpenTelemetrySdk();
}
}
注册(夸库斯):
import jakarta.enterprise.context.ApplicationScoped;
@ApplicationScoped
public class Http4xxAsSuccessProcessor extends Http4xxAsSuccessProcessorBase {
}
import { SpanKind, SpanStatusCode } from "@opentelemetry/api";
import { SpanProcessor, ReadableSpan } from "@opentelemetry/sdk-trace-base";
import { ATTR_HTTP_RESPONSE_STATUS_CODE } from "@opentelemetry/semantic-conventions";
// Deprecated semconv attribute; not exported from the stable entry point.
const ATTR_HTTP_STATUS_CODE = "http.status_code";
export class Http4xxAsSuccessProcessor implements SpanProcessor {
private keepAsFailure = new Set<number>(/* [429] */);
onStart(): void {}
onEnd(span: ReadableSpan): void {
if (span.kind !== SpanKind.SERVER) return;
const attrs = span.attributes;
const sc =
(attrs[ATTR_HTTP_RESPONSE_STATUS_CODE] as number | undefined) ??
(attrs[ATTR_HTTP_STATUS_CODE] as number | undefined);
if (
typeof sc === "number" &&
sc >= 400 &&
sc < 500 &&
!this.keepAsFailure.has(sc)
) {
// By onEnd the span is ended, so setStatus()/setAttribute() are no-ops.
// Directly mutate the backing fields so the exporter sees the change.
(span as any).status = { code: SpanStatusCode.OK };
(span as any).attributes = {
...attrs,
"ai.modified.http4xx.success": true,
};
console.log(`[Processor] Rewrote ${sc} span to OK`);
}
}
shutdown(): Promise<void> {
return Promise.resolve();
}
forceFlush(): Promise<void> {
return Promise.resolve();
}
}
处理器:
from __future__ import annotations
from typing import Iterable, Optional
from opentelemetry.trace import SpanKind, Status, StatusCode
from opentelemetry.sdk.trace import ReadableSpan, SpanProcessor
# Current HTTP semconv attribute
ATTR_HTTP_RESPONSE_STATUS_CODE = "http.response.status_code"
# Older / deprecated attribute that some instrumentations may still emit
ATTR_HTTP_STATUS_CODE = "http.status_code"
class Http4xxAsSuccessProcessor(SpanProcessor):
def __init__(self, keep_as_failure: Optional[Iterable[int]] = None) -> None:
# Example: keep 429 as a failure if you want throttling to remain visible
# self._keep_as_failure = set(keep_as_failure or {429})
def on_start(self, span, parent_context=None) -> None:
# No-op
return
def on_end(self, span: ReadableSpan) -> None:
if span.kind is not SpanKind.SERVER:
return
attrs = span.attributes
status_code = attrs.get(
ATTR_HTTP_RESPONSE_STATUS_CODE,
attrs.get(ATTR_HTTP_STATUS_CODE),
)
if (
isinstance(status_code, int)
and 400 <= status_code < 500
and status_code not in self._keep_as_failure
):
# ReadableSpan.status is read-only, so in current released SDKs
# we mutate the private backing field that the exporter reads.
span._status = Status(StatusCode.OK) # pylint: disable=protected-access
# Optional diagnostic marker so you can tell the processor rewrote it.
# ReadableSpan.attributes returns a read-only MappingProxyType, so we
# update the private backing store instead.
if getattr(span, "_attributes", None) is None:
span._attributes = {} # pylint: disable=protected-access
try:
span._attributes["ai.modified.http4xx.success"] = True # pylint: disable=protected-access
except TypeError:
# Fallback if the backing store is not directly writable
span._attributes = dict(span.attributes) # pylint: disable=protected-access
span._attributes["ai.modified.http4xx.success"] = True # pylint: disable=protected-access
def shutdown(self) -> None:
return
def force_flush(self, timeout_millis: int = 30000) -> bool:
return True
注册:
from azure.monitor.opentelemetrgit import configure_azure_monitor
configure_azure_monitor(
connection_string="<your-connection-string>",
span_processors=[Http4xxAsSuccessProcessor()],
)
获取追踪 ID 或跨度 ID
要获取当前处于活动状态的跨度中的Trace ID和Span ID,请使用以下步骤。
注意
Activity 命名空间中的 ActivitySource 和 System.Diagnostics 类分别代表 Span 和 Tracer 的 OpenTelemetry 概念。 这是因为 OpenTelemetry 跟踪 API 的某些部分直接合并到.NET运行时中。 若要了解详细信息,请参阅 OpenTelemetry .NET 跟踪 API 的简介。
// Get the current activity.
Activity activity = Activity.Current;
// Get the trace ID of the activity.
string traceId = activity?.TraceId.ToHexString();
// Get the span ID of the activity.
string spanId = activity?.SpanId.ToHexString();
注意
Activity 命名空间中的 ActivitySource 和 System.Diagnostics 类分别代表 Span 和 Tracer 的 OpenTelemetry 概念。 这是因为 OpenTelemetry 跟踪 API 的某些部分直接合并到.NET运行时中。 若要了解详细信息,请参阅 OpenTelemetry .NET 跟踪 API 的简介。
// Get the current activity.
Activity activity = Activity.Current;
// Get the trace ID of the activity.
string traceId = activity?.TraceId.ToHexString();
// Get the span ID of the activity.
string spanId = activity?.SpanId.ToHexString();
可以使用 opentelemetry-api 获取跟踪 ID 或范围 ID。
将 opentelemetry-api-1.0.0.jar(或更高版本)添加到应用程序:
<dependency>
<groupId>io.opentelemetry</groupId>
<artifactId>opentelemetry-api</artifactId>
<version>1.0.0</version>
</dependency>
在代码中获取请求跟踪 ID 和跨度 ID。
import io.opentelemetry.api.trace.Span;
Span span = Span.current();
String traceId = span.getSpanContext().getTraceId();
String spanId = span.getSpanContext().getSpanId();
在代码中获取请求跟踪 ID 和跨度 ID。
import io.opentelemetry.api.trace.Span;
Span span = Span.current();
String traceId = span.getSpanContext().getTraceId();
String spanId = span.getSpanContext().getSpanId();
在代码中获取请求跟踪 ID 和跨度 ID。
export class GetTraceAndSpanIdSample {
static async run() {
// Dynamically import tracing API
const { trace } = await import("@opentelemetry/api");
// Read the span/trace id from the active span (if any)
const activeSpan = trace.getActiveSpan();
const spanId = activeSpan?.spanContext().spanId;
const traceId = activeSpan?.spanContext().traceId;
console.log("SpanId:", spanId, "TraceId:", traceId);
}
}
在代码中获取请求跟踪 ID 和跨度 ID。
# Import the necessary libraries.
from opentelemetry import trace
# Get the trace ID and span ID of the current span.
trace_id = trace.get_current_span().get_span_context().trace_id
span_id = trace.get_current_span().get_span_context().span_id
故障排除、反馈和支持
提示
以下部分适用于所有 OpenTelemetry 发行版文章。
故障排除
OpenTelemetry 反馈
若要提供反馈,请查看以下内容:
支持
选择所选语言的选项卡,以发现支持选项。
注意
Quarkus 社区支持和维护 Quarkus 扩展。 寻求帮助,请使用 Quarkus 社区支持渠道。 Azure不提供对此集成的技术支持。
后续步骤