System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) · 课时

使用 OTel SDK 为应用添加检测

概览适用于各种编程语言的 OpenTelemetry SDK。了解如何使用它们生成日志、指标和追踪数据。

使用 OTel SDK 为应用添加检测 是 CoddyKit 上的免费 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

What are OTel SDKs?

OpenTelemetry SDKs are language-specific libraries that enable your applications to generate and export observability data. Think of them as the toolkit for making your code observable.

They provide the necessary APIs (Application Programming Interfaces) to capture logs, metrics, and traces directly from your application's runtime.

Role of OpenTelemetry SDKs

SDKs are crucial because they bridge the gap between your application code and the OpenTelemetry standard. They handle the complex work of:

Collecting Data: Gathering raw observability signals like method calls or variable values.
Processing Data: Enriching, filtering, and batching this data.
Exporting Data: Sending the processed data to an observability backend (like Jaeger, Prometheus, or an ELK stack).

Key OTel SDK Components

An OpenTelemetry SDK typically includes several core components that work together:

Providers: Such as TracerProvider, MeterProvider, and LoggerProvider, which manage the creation of Tracers, Meters, and Loggers.
Processors/Readers: These define how collected data (spans, metrics, log records) is processed before being sent to an exporter.
Exporters: Components that send the processed data to an external system, like a console or a remote endpoint.

Tracing with OTel SDKs

When you want to trace requests through your system, OpenTelemetry SDKs provide tools to create and manage spans. A span represents a single operation within a trace.

The SDK gives you a Tracer object, which you use to start new spans, set attributes on them, and link them to parent spans, effectively building a complete trace of a request's journey.

OTel Tracing Code Example

Here's a simple Python example showing how to initialize OpenTelemetry and create a basic trace with nested spans. We use a ConsoleSpanExporter to print spans to the console.

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor

# 1. Configure the TracerProvider
provider = TracerProvider()
processor = SimpleSpanProcessor(ConsoleSpanExporter())
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

# 2. Get a Tracer
tracer = trace.get_tracer(__name__)

# 3. Create a Span
with tracer.start_as_current_span("my-first-span"):
    print("Inside the span!")
    with tracer.start_as_current_span("child-span"):
        print("Inside the child span!")

print("Tracing example finished.")

Metrics with OTel SDKs

OpenTelemetry SDKs allow you to define and record various types of metrics. Metrics are numerical measurements captured over time, useful for aggregated views of system health and performance.

You obtain a Meter object from the SDK, which you then use to create different types of instruments, such as Counters (for incrementing values) or Gauges (for current values).

OTel Metrics Code Example

This Python example demonstrates how to set up OpenTelemetry for metrics and use a Meter to create and update a Counter. The ConsoleMetricExporter prints the collected metrics.

from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import ConsoleMetricExporter, PeriodicExportingMetricReader

# 1. Configure the MeterProvider
reader = PeriodicExportingMetricReader(ConsoleMetricExporter())
provider = MeterProvider(metric_readers=[reader])
metrics.set_meter_provider(provider)

# 2. Get a Meter
meter = metrics.get_meter(__name__)

# 3. Create a Counter instrument
counter = meter.create_counter(
    "my_example_counter",
    description="Counts how many times something happens"
)

# 4. Record a measurement
counter.add(1, {"key": "value"})
counter.add(2, {"another_key": "another_value"})
print("Metrics example finished. May take a moment to export.")

Logging with OTel SDKs

OpenTelemetry SDKs also provide a way to capture and enrich logs. While traditional logging often focuses on plain text, OTel logs can be structured and correlated with traces and metrics.

The SDK offers a Logger (or integrates with existing logging frameworks) to create Log Records. These records can then be processed and exported alongside your traces and metrics, providing a unified view.

OTel Logging Code Example

This Python example shows how to integrate OpenTelemetry with the standard Python logging library. Logs emitted through the standard logger are then processed and exported by OTel.

import logging
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import ConsoleLogExporter, SimpleLogRecordProcessor

# 1. Configure the LoggerProvider
provider = LoggerProvider()
processor = SimpleLogRecordProcessor(ConsoleLogExporter())
provider.add_log_record_processor(processor)
set_logger_provider(provider)

# 2. Get a Python standard logger
#    and attach the OTel handler
handler = LoggingHandler(logger_provider=provider)
logging.getLogger().addHandler(handler)
logging.getLogger().setLevel(logging.INFO)

# 3. Emit a log record
logging.info("This is an info log from OTel SDK!")
logging.warning("A warning occurred.")

print("Logging example finished.")

OTel SDKs Quick Check

OpenTelemetry SDKs are essential for instrumenting applications. Which of the following is NOT a core component or function provided by an OpenTelemetry SDK?

Recap: OTel SDKs

You've learned that OpenTelemetry SDKs are the foundational tools for instrumenting your applications across different programming languages.

They provide the APIs to generate traces, metrics, and logs.
They handle the processing and exporting of this data to your chosen observability backend.
By using SDKs, you ensure your application's observability data adheres to the OpenTelemetry standard, making it portable and vendor-agnostic.

Next, you'll dive deeper into how to effectively instrument your applications.

常见问题解答

「使用 OTel SDK 为应用添加检测」课时是免费的吗？

是的 — 「使用 OTel SDK 为应用添加检测」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) 课程的其余内容，请升级到 CoddyKit PRO。 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) 课程共包含 4 节课。

「使用 OTel SDK 为应用添加检测」这节课中我会学到什么？

概览适用于各种编程语言的 OpenTelemetry SDK。了解如何使用它们生成日志、指标和追踪数据。你通过在浏览器中直接运行的动手代码来练习 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) 需要有经验吗？

无需任何先前经验。CoddyKit 上的 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「使用 OTel SDK 为应用添加检测」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) 课中编写并运行代码吗？

能。每节 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

← 返回 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry)