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

了解现代日志格式

了解结构化日志记录以及 JSON 等常见格式。理解与纯文本相比，结构化日志为何更适合机器解析和分析。

了解现代日志格式 是 CoddyKit 上的免费 System Observability: Logging, Metrics & Tracing (ELK + OpenTelemetry) 课时。这是第 1 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 Application Logs?

Imagine your application as a busy worker. How do you know what it's doing? That's where logs come in!

Logs are like a diary for your software. They record events, operations, and status messages as your application runs. They tell you:

When something happened
What action was performed
If an error occurred

These records are crucial for debugging, monitoring performance, and understanding system behavior.

The Traditional Way: Plain Text

Historically, logs were often simple lines of text. Each event was written as a human-readable string.

For example, a login event might look like this:

2023-10-27 10:30:00 INFO User 'alice' logged in from IP 192.168.1.100

This format is straightforward and easy for a human to read when looking at a few lines.

Plain Text: Hard for Machines

While plain text logs are human-friendly at a glance, they pose a big challenge for computers.

To find all logins from 'alice' or count errors from a specific IP address, a machine would need to:

Guess the date format
Extract the log level ('INFO')
Parse the username ('alice')
Identify the IP address

This process, called parsing, is complex and prone to errors because there's no fixed structure.

Hello, Structured Logging!

This is where structured logging comes to the rescue! It's a modern approach that outputs log data in a consistent, machine-readable format.

Instead of free-form text, each log entry is an object with clearly defined fields (like 'timestamp', 'level', 'user_id', 'message').

Think of it like organizing your notes into a spreadsheet instead of a jumbled notebook. Each piece of information has its own column.

JSON: Your Log's New Structure

The most popular format for structured logging today is JSON (JavaScript Object Notation).

JSON is lightweight, human-readable, and incredibly easy for machines to parse. It represents data as key-value pairs.

Here's how our 'alice' login event might look as a JSON log:

{"timestamp": "2023-10-27T10:30:00Z", "level": "INFO", "message": "User logged in", "user": "alice", "ip_address": "192.168.1.100"}

The Power of Structured Logs

Using structured formats like JSON unlocks powerful capabilities:

Easier Machine Parsing: Computers can directly read and understand each data field.
Efficient Searching: Quickly find logs where user="alice" or level="ERROR".
Better Analysis: Aggregate data, count events, and build dashboards based on specific fields.
No More Guessing: No need for complex regular expressions to extract data, reducing errors.

This transforms logs from simple text files into rich, queryable data.

Example: Outputting a JSON Log

Here's a simple Java example demonstrating how you might output a structured log in JSON format. In real applications, you'd use a logging library to handle this.

Try running it to see the structured output!

public class StructuredLogger {
  public static void main(String[] args) {
    // This is a simplified way to output a JSON log string.
    // Real-world apps use dedicated logging libraries for this.
    String jsonLog = "{\"timestamp\": \"2023-10-27T10:30:00Z\", \"level\": \"INFO\", \"message\": \"User logged in successfully\", \"user_id\": 123, \"ip_address\": \"192.168.1.100\"}";
    System.out.println(jsonLog);
  }
}

Inside a JSON Log Object

Let's break down the JSON log from the previous example:

{"timestamp": "2023-10-27T10:30:00Z", "level": "INFO", "message": "User logged in successfully", "user_id": 123, "ip_address": "192.168.1.100"}

Each piece of information is a key-value pair.
"timestamp" is the key, "2023-10-27T10:30:00Z" is its value.
"level" is the key, "INFO" is its value.

This explicit labeling makes every detail instantly accessible to machines.

Essential Fields in Structured Logs

While you can add any relevant data, some fields are commonly found and highly useful in structured logs:

timestamp: The exact time the event occurred (often in ISO 8601 format).
level: The severity of the log (e.g., DEBUG, INFO, WARN, ERROR, FATAL).
message: A human-readable description of the event.
service: The name of the application or service generating the log.
transaction_id: A unique ID to link related events across different services.
user_id: The ID of the user involved in the event.

Quick Check: Why Structured Logs?

You've learned about the differences between plain text and structured logs. Think about the key benefits.

Recap: Logs Get Organized!

Congratulations! You've taken a crucial step in understanding modern application logging.

We saw that traditional plain text logs are simple but hard for computers to analyze.
Structured logging provides a consistent, machine-readable format for log data.
JSON is the most popular structured format, using key-value pairs.
Structured logs enable powerful searching, filtering, and automated analysis, making your logs far more valuable.

Next, we'll explore how these structured logs are collected and managed in centralized systems!

常见问题解答

「了解现代日志格式」课时是免费的吗？

是的 — 「了解现代日志格式」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。

「了解现代日志格式」这节课中我会学到什么？

了解结构化日志记录以及 JSON 等常见格式。理解与纯文本相比，结构化日志为何更适合机器解析和分析。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 1 节课，共 4 节。

「了解现代日志格式」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)