#4 从companyapp-api:pr-api导入缓存清单
#4 sha256:7c6bf1eebafe5af983d68e3fb7d72c271b8a80918f9799979ebd2b2bea604d10
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#5 [python-base 1/1] 从docker.io/library/python:3.9-slim导入
#5 sha256:f876c6f14c8c365d299789228d8a0c38ac92e17ea62116c830f5b7c6bc684e47
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# ./api/docker/Dockerfile
# 创建具有共享环境变量的Python基础镜像
FROM python:3.9-slim as python-base
ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PIP_NO_CACHE_DIR=off \
    PIP_DISABLE_PIP_VERSION_CHECK=on \
    PIP_DEFAULT_TIMEOUT=100 \
    POETRY_HOME="/opt/poetry" \
    POETRY_VIRTUALENVS_IN_PROJECT=true \
    POETRY_NO_INTERACTION=1 \
    PYSETUP_PATH="/opt/pysetup" \
    VENV_PATH="/opt/pysetup/.venv";
ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH";
# builder-base用于构建依赖项
FROM python-base as builder-base
RUN apt-get update \
    && apt-get install --no-install-recommends -y \
        curl \
        build-essential 
        
# 安装Poetry - 遵守$POETRY_VERSION和$POETRY_HOME
ENV POETRY_VERSION=1.4.1 GET_POETRY_IGNORE_DEPRECATION=1
RUN curl -sSL https://install.python-poetry.org | python3 -
# 我们在这里复制Python要求以进行缓存，并且只在运行时使用Poetry安装
WORKDIR $PYSETUP_PATH
COPY ./poetry.lock ./pyproject.toml ./
RUN poetry install --no-dev     
# 'development'阶段安装所有开发依赖项，可用于开发代码。
# 例如，使用docker-compose在/app下挂载本地卷
FROM python-base as development
# 复制poetry和venv到镜像中
COPY --from=builder-base $POETRY_HOME $POETRY_HOME
COPY --from=builder-base $PYSETUP_PATH $PYSETUP_PATH
# 复制我们的入口点
# COPY ./docker/docker-entrypoint.sh /docker-entrypoint.sh
RUN chmod +x . /opt/pysetup/.venv/bin/activate
# venv已经安装了运行时依赖项，我们可以更快地安装
WORKDIR $PYSETUP_PATH
RUN poetry install
WORKDIR /app
COPY . .
EXPOSE 5000 5672
CMD ["python", "src/manage.py", "runserver", "0.0.0.0:5000"]
# 'unit-tests'阶段使用unittest和coverage运行我们的单元测试。   
FROM development AS unit-tests
RUN coverage run --omit='src/manage.py,src/config/*,*/.venv/*,*/*__init__.py,*/tests.py,*/admin.py' src/manage.py test src --tag=ut &&
    coverage report

# ./pipelines/pr.yaml
# # 这由PR和分支策略触发
触发器: 无
# 读取基本变量模板
变量:
  imageRepository: companyapp
  dockerfilePath: $(Build.SourcesDirectory)
  vmImageName: ubuntu-latest
# 使用ubuntu-latest镜像
池:
  vmIMage: $(vmImageName)
阶段:
- 阶段: 构建
  显示名称: 为测试构建镜像...
  工作:
  - 任务: BuildingAndCache
    显示名称: 构建和缓存测试镜像...
    步骤: 
    - 任务: Cache@2
      显示名称: 创建缓存...
      输入: 
        key: 'docker | "$(Agent.OS)" | cache'
        path: $(Pipeline.Workspace)/docker
        cacheHitVar: CACHE_RESTORED
    - 任务: Docker@2
      显示名称: 构建测试镜像...
      输入:
        command: 'build'
        repository: $(imageRepository)-$(service)
        dockerfile: $(dockerFilePath)/$(service)/docker/Dockerfile
        buildContext: $(dockerFilePath)/$(service)
        arguments: |
          --target development
        tags: |
          pr-$(service)
      env:
        DOCKER_BUILDKIT: 1
    - bash: |
        mkdir -p $(Pipeline.Workspace)/docker
        docker save -o $(Pipeline.Workspace)/docker/cache.tar $(imageRepository)-$(service):pr-$(service)
      显示名称: 保存镜像到缓存...
      # condition: and(not(canceled()), not(failed()), ne(variables.CACHE_RESTORED, 'true'))
        
- 阶段: UnitTest
  显示名称: 运行单元测试...
  工作:
  - 任务: UnitTesting
    显示名称: 运行单元测试...
    步骤: 
    - 任务: Cache@2
      显示名称: 检查现有镜像的缓存...
      输入: 
        key: 'docker | "$(Agent.OS)" | cache'
        path: $(Pipeline.Work
<details>
<summary>英文:</summary>
Reworking our Azure Pipelines to try and speed them up. 
Currently it is rebuilding the same dependent stages for our `unit-testing` and `integration-testing` stages... which is really slow and inefficient.
- I&#39;m building the image which `--target development`, storing it in cache with `Cache@2`. 
- In our `UnitTest` stage, a task successfully loads it from cache using `docker load -i` and confirmed with `docker images`.
- Then it gets to the actual running of the `unit-tests` stage where I use `--cache-from=` and `--target unit-tests`.
- In the pipeline I can see it acknowledging the cache, but it still builds the stages the cached image should already have:

As for the files I'm working with...

**Any suggestions for what I'm doing wrong and how to resolve it?**
---
The resources I've been consulting:
- https://docs.docker.com/build/building/multi-stage/
- https://github.com/michaeloliverx/python-poetry-docker-example/blob/master/docker/Dockerfile
- https://learn.microsoft.com/en-us/azure/devops/pipelines/release/caching?view=azure-devops#docker-images
- https://stackoverflow.com/questions/59266670/how-to-enable-docker-layer-caching-in-azure-devops
- Tried ChatGPT and all it is doing is providing what I'm currently doing as an answer.
</details>
# 答案1
**得分**: 0
已经找到问题，这更多是一个疏忽：`Dockerfile` 寻找的图像与在 `pr.yaml` 中构建的图像的名称不匹配。
我需要更新：

以匹配此处正在构建的图像的名称：

- bash: |
    mkdir -p $(Pipeline.Workspace)/docker
    docker save -o $(Pipeline.Workspace)/docker/cache.tar $(imageRepository)-$(service):pr-$(service)
  displayName: 将镜像保存到缓存中...

正在构建的图像的名称是：

因此，`Dockerfile` 应该是：

一旦我这样做了，`unit-tests` 状态只需要 10 秒，而不是 1 分 20 秒。
<details>
<summary>英文:</summary>
Figured it out and was more of an oversight: the image the `Dockerfile` was looking for didn&#39;t match the name of the image that was built in the `pr.yaml`.
I needed to update:

To match the name of the image being built here:

- bash: |
    mkdir -p $(Pipeline.Workspace)/docker
    docker save -o $(Pipeline.Workspace)/docker/cache.tar $(imageRepository)-$(service):pr-$(service)
  displayName: Saving image to cache...

The name of the image being built was:

So the `Dockerfile` should have been:

Once I did that, the `unit-tests` state took 10s instead of 1m20s.
</details>