> For the complete documentation index, see [llms.txt](https://listen-lavender.gitbook.io/prefect-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://listen-lavender.gitbook.io/prefect-docs/advancetutorial/logging.md).

# 日志部署

## 日志

日志记录是任何生产环境的关键方面。Prefect公开了一系列用于自定义日志行为的配置设置和实用函数。

### 添加日志

可能想添加其他日志的最常见位置是自定义task中。可以在两个位置访问task的logger，具体取决于创建task的方式：

* 如果task是作为**Task**基类的子类实现的，则**self.logger**属性包含**Task**基类的logger
* 如果task是通过@task装饰器实现的，则可以从上下文访问logger：logger=prefect.context.get("logger")

### 配置

Prefect用户配置文件提供了一种在本地运行时轻松更改显示日志的方式。在用户配置文件中，添加具有以下结构的日志记录部分：

```bash
[logging]
# The logging level: NOTSET, DEBUG, INFO, WARNING, ERROR, or CRITICAL
level = "INFO"

# The log format
format = "[%(asctime)s] %(levelname)s - %(name)s | %(message)s"
```

另外，可以设置以下环境变量：

```bash
export PREFECT__LOGGING__LEVEL="INFO"
export PREFECT__LOGGING__FORMAT="[%(asctime)s] %(levelname)s - %(name)s | %(message)s"
```

### 添加处理器

除了更改日志格式外，还可以通过在执行之前直接与logger对象进行交互来使日志满足更多需要。例如，可以将新的处理器添加到logger中（注意日志处理器允许将日志发送到多个可配置的目标位置）。

这可以通过位于**prefect.utilities.logging**中的**get\_logger**实用程序轻松完成。可以通过不带任何参数的**get\_logger()**来访问根logger。注意task logger和flow logger分别与名称为“task”和“flow”的logger相关联。

## 例子

让我们来看一个基本的例子。首先，我们将在本地计算机上创建一个虚拟Web服务器，并将日志发布到：

```bash
# spins up a local webserver running at http://0.0.0.0:8000/
python3 -m http.server
```

接下来，创建一个logger处理器，并将该处理器添加到task的日志对象中。

```python
import logging
import requests

import prefect
from prefect import task, Flow
from prefect.utilities.logging import get_logger


class MyHandler(logging.StreamHandler):
    def emit(self, record):
        requests.post("http://0.0.0.0:8000/", params=dict(msg=record.msg))


@task(name="Task A")
def task_a():
    return 3


@task(name="Task B")
def task_b(x):
    logger = prefect.context.get("logger")
    logger.debug("Beginning to run Task B with input {}".format(x))
    y = 3 * x + 1
    logger.debug("Returning the value {}".format(y))
    return y


with Flow("logging-example") as flow:
    result = task_b(task_a)


# now attach our custom handler to Task B's logger
task_logger = get_logger("Task")
task_logger.addHandler(MyHandler())


if __name__ == "__main__":
    flow.run()
```

如果我们将此代码存储在名为**logging\_example.py**的文件中，则可以使用环境变量更新日志格式和级别，并开始以下流程运行：

```bash
export PREFECT__LOGGING__LEVEL="DEBUG"
export PREFECT__LOGGING__FORMAT="%(levelname)s - %(name)s | %(message)s"

python logging_example.py
```

像往常一样，我们应该看到日志发布到**stdout**。但是，如果导航到运行Web服务器的窗口，则还会看到类似以下内容：

```bash
127.0.0.1 - "POST /?msg=Beginning+to+run+Task+B+with+input+3 HTTP/1.1" 501
127.0.0.1 - "POST /?msg=Returning+the+value+10 HTTP/1.1" 501
```

我们看到501状态代码，因为我们的网络服务器未实现任何路由。

## 进一步

可以通过此示例进一步为日志创建真正的存储解决方案来。此外如果在Prefect Cloud上运行，则可以选择Prefect存储日志，并通过方便的GraphQL接口访问日志！

* [Prefect官网](https://www.prefect.io/)
* [英版原文](https://docs.prefect.io/core/advanced_tutorials/custom-logs.html)
* [联系译者](https://github.com/listen-lavender)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://listen-lavender.gitbook.io/prefect-docs/advancetutorial/logging.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.