如何使用 Poetry 建立新的 Harlequin 適配器

歡迎來到 LETSQL 教學系列的第一篇文章！

在這篇部落格文章中，我們脫離了通常的資料管道主題，以 DataFusion 為例示範如何使用 Poetry 建立和發布 Python 套件。

介紹

Harlequin 是 SQL 資料庫的 TUI 用戶端，以其對 SQL 資料庫的輕量級廣泛支援而聞名。它是用於資料探索和分析工作流程的多功能工具。 Harlequin 提供了一個互動式 SQL 編輯器，具有自動完成、語法突出顯示和查詢歷史記錄等功能。它還具有可以顯示大型結果集的結果檢視器。然而，Harlequin 之前沒有 DataFusion 適配器。值得慶幸的是，添加一個真的很容易。

在這篇文章中，我們將透過為 DataFusion 建立 Harlequin 適配器來示範這些概念。並且，透過這樣做，我們還將介紹 Poetry 的基本功能、專案設定以及在 PyPI 上發布套件的步驟。

要充分利用本指南，您應該對虛擬環境、Python 套件和模組以及 pip 有基本的了解。
我們的目標是：

• 介紹詩歌及其優點
• 使用 Poetry 建立一個項目
• 為 DataFusion 開發 Harlequin 適配器
• 準備包裝並發佈到 PyPI

最後，您將獲得 Poetry 的實務經驗並了解現代 Python 套件管理。

本文實作的程式碼可以在 GitHub 上找到，也可以在 PyPI 中找到。

丑角

Harlequin 是一個在終端機中運作的 SQL IDE。它提供了傳統命令列資料庫工具的強大且功能豐富的替代方案，使其適用於資料探索和分析工作流程。

有關 Harlequin 的一些重要事項：

• Harlequin 支援多個資料庫適配器，可將您連接到 DuckDB、SQLite、PostgreSQL、MySQL 等。
• Harlequin 提供了一個互動式 SQL 編輯器，具有自動完成、語法突出顯示和查詢歷史記錄等功能。它還具有一個結果檢視器，可以顯示大型結果集。
• Harlequin 以更強大且用戶友好的介面取代了傳統的基於終端的資料庫工具。
• Harlequin 使用適配器外掛程式作為任何資料庫的通用介面。

數據融合

DataFusion 是一種快速、可擴展的查詢引擎，用於使用 Apache Arrow 記憶體格式在 Rust 中建立高品質的以資料為中心的系統。

DataFusion 提供 SQL 和 Dataframe API、卓越的效能、對 CSV、Parquet、JSON 和 Avro 的內建支援、廣泛的客製化以及出色的社群。

它附帶了自己的 CLI，可以在此處找到更多資訊。

詩

Poetry 是一款功能豐富的現代工具，可簡化 Python 專案的依賴管理和打包，使開發更加確定性和高效。
來自文件：

Poetry 是 Python 中的依賴管理和打包工具。它允許您聲明您的專案所依賴的庫，並且它將為您管理（安裝/更新）它們。
Poetry 提供了一個鎖定檔案來確保可重複安裝，並可以建立您的專案進行分發。

為 Harlequin 建立新適配器

Harlequin 適配器是一個 Python 套件，允許 Harlequin 與資料庫系統一起使用。

適配器是一個 Python 套件，它在 harlequin.adapters 群組中聲明一個入口點。此入口點應引用 HarlequinAdapter 抽象基底類別的子類別。
這使得 Harlequin 能夠發現已安裝的適配器並在運行時實例化選定的適配器

除了 HarlequinAdapter 類別之外，套件還必須提供 HarlequinConnection 和 HarlequinCursor 的實作。更詳細的描述可以在這個
指導。

Harlequin 適配器模板

開發 Harlequin 適配器的第一步是從現有的 harlequin-adapter-template 產生一個新的儲存庫

GitHub 範本是作為新專案起點的儲存庫。它們提供預先配置的文件、結構和設置，這些文件、結構和設置可以複製到新存儲庫，從而可以快速設置項目，而無需分叉的開銷。
此功能簡化了根據既定模式創建一致、結構良好的項目的過程。

harlequin-adapter-template 附帶一個詩歌.lock 文件和一個 pyproject.toml 文件，以及一些用於定義所需類別的樣板程式碼。

適配器編碼

在討論編碼細節之前，讓我們先探討一下包分發所需的基本文件。

封裝配置

pyproject.toml 檔案現在是配置 Python 套件以進行發布和其他工具的標準。這一 TOML 格式的檔案在 PEP 518 和 PEP 621 中引入，將多個設定檔合併為一個。它透過使其更加健壯和標準化來增強依賴管理。

Poetry，利用 pyproject.toml 處理專案的虛擬環境、解決依賴關係並建立套件。

模板的pyproject.toml如下：

[tool.poetry]
name = "harlequin-myadapter"
version = "0.1.0"
description = "A Harlequin adapter for 2da0dc96d989c23227dfa4db76fedb27."
authors = ["Ted Conbeer 6da378a5ba0e452039972dddc494b9b0"]
license = "MIT"
readme = "README.md"
packages = [
    { include = "harlequin_myadapter", from = "src" },
]

[tool.poetry.plugins."harlequin.adapter"]
my-adapter = "harlequin_myadapter:MyAdapter"

[tool.poetry.dependencies]
python = ">=3.8.1,f681e766a553307a093124d04136e013=4.6.0", python = "79314caf0de6d0ee2e7396a84eea2e08 None:
        self.conn_str = conn_str
        self.options = options

    def connect(self) -> DataFusionConnection:
        conn = DataFusionConnection(self.conn_str, self.options)
        return conn

The second one is the HarlequinConnection, particularly the methods execute and get_catalog.

#| eval: false
#| code-fold: false
#| code-summary: implementation of execution of HarlequinConnection

 def execute(self, query: str) -> HarlequinCursor | None:
     try:
         cur = self.conn.sql(query)  # type: ignore
         if str(cur.logical_plan()) == "EmptyRelation":
             return None
     except Exception as e:
         raise HarlequinQueryError(
             msg=str(e),
             title="Harlequin encountered an error while executing your query.",
         ) from e
     else:
         if cur is not None:
             return DataFusionCursor(cur)
         else:
             return None

For brevity, we've omitted the implementation of the get_catalog function. You can find the full code in the adapter.py file within our GitHub repository.

Finally, a HarlequinCursor implementation must be provided as well:

#| eval: false
#| code-fold: false
#| code-summary: implementation of HarlequinCursor

class DataFusionCursor(HarlequinCursor):
    def __init__(self, *args: Any, **kwargs: Any) -> None:
        self.cur = args[0]
        self._limit: int | None = None

    def columns(self) -> list[tuple[str, str]]:
        return [
            (field.name, _mapping.get(field.type, "?")) for field in self.cur.schema()
        ]

    def set_limit(self, limit: int) -> DataFusionCursor:
        self._limit = limit
        return self

    def fetchall(self) -> AutoBackendType:
        try:
            if self._limit is None:
                return self.cur.to_arrow_table()
            else:
                return self.cur.limit(self._limit).to_arrow_table()
        except Exception as e:
            raise HarlequinQueryError(
                msg=str(e),
                title="Harlequin encountered an error while executing your query.",
            ) from e

Making the plugin discoverable

Your adapter must register an entry point in the harlequin.adapters group using the packaging software you use to build your project.
If you use Poetry, you can define the entry point in your pyproject.toml file:

[tool.poetry.plugins."harlequin.adapter"]
datafusion = "harlequin_datafusion:DataFusionAdapter"

An entry point is a mechanism for code to advertise components it provides to be discovered and used by other code.

Notice that registering a plugin with Poetry is equivalent to the following pyproject.toml specification for entry points:

[project.entry-points."harlequin.adapter"]
datafusion = "harlequin_datafusion:DataFusionAdapter"

Testing

The template provides a set of pre-configured tests, some of which are applicable to DataFusion while others may not be relevant. One test that's pretty cool checks if the plugin can be discovered, which is crucial for ensuring proper integration:

#| eval: false
#| code-fold: false
if sys.version_info f92766c91ae8e699e6db3bc44a8ef3ea None:
    PLUGIN_NAME = "datafusion"
    eps = entry_points(group="harlequin.adapter")
    assert eps[PLUGIN_NAME]
    adapter_cls = eps[PLUGIN_NAME].load()
    assert issubclass(adapter_cls, HarlequinAdapter)
    assert adapter_cls == DataFusionAdapter

To make sure the tests are passing, run:

poetry run pytest

The run command executes the given command inside the project’s virtualenv.

Building and Publishing to PyPI

With the tests passing, we're nearly ready to publish our project. Let's enhance our pyproject.toml file to make our package more discoverable and appealing on PyPI. We'll add key metadata including:

1. A link to the GitHub repository
2. A path to the README file
3. A list of relevant classifiers

These additions will help potential users find and understand our package more easily.

classifiers = [
    "Development Status :: 3 - Alpha",
    "Intended Audience :: Developers",
    "Topic :: Software Development :: User Interfaces",
    "Topic :: Database :: Database Engines/Servers",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: Implementation :: CPython"
]
readme = "README.md"
repository = "https://github.com/mesejo/datafusion-adapter"

For reference:

• The complete list of classifiers is available on PyPI's website.
• For a detailed guide on writing pyproject.toml, check out this resource.
• The formal, technical specification for pyproject.toml can be found on packaging.python.org.

Building

We're now ready to build our library and verify its functionality by installing it in a clean virtual environment. Let's start with the build process:

poetry build

This command will create distribution packages (both source and wheel) in the dist directory.

The wheel file should have a name like harlequin_datafusion-0.1.1-py3-none-any.whl. This follows the standard naming convention:

• harlequin_datafusion is the package (or distribution) name
• 0.1.1 is the version number
• py3 indicates it's compatible with Python 3
• none compatible with any CPU architecture
• any with no ABI (pure Python)

To test the installation, create a new directory called test_install. Then, set up a fresh virtual environment with the following command:

python -m venv .venv

To activate the virtual environment on MacOS or Linux:

source .venv/bin/activate

After running this command, you should see the name of your virtual environment (.venv) prepended to your command prompt, indicating that the virtual environment is now active.

To install the wheel file we just built, use pip as follows:

pip install /path/to/harlequin_datafusion-0.1.1-py3-none-any.whl

Replace /path/to/harlequin_datafusion-0.1.1-py3-none-any.whl with the actual path to the wheel file you want to install.

If everything works fined, you should see some dependencies installed, and you should be able to do:

harlequin -a datafusion

Congrats! You have built a Python library. Now it is time to share it with the world.

Publishing to PyPI

The best practice before publishing to PyPI is to actually publish to the Test Python Package Index (TestPyPI)

To publish a package to TestPyPI using Poetry, follow these steps:

1. Create an account at TestPyPI if you haven't already.

2. Generate an API token on your TestPyPI account page.

3. Register the TestPyPI repository with Poetry by running:
   

   poetry config repositories.test-pypi https://test.pypi.org/legacy/
   

4. To publish your package, run:
   

   poetry publish -r testpypi --username __token__ --password <token>
   

Replace d6fb5a6237ab04b68d3c67881a9080fa with the actual token value you generated in step 2. To verify the publishing process, use the following command:

python -m pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple 232e112a1ffb9f21e3b1b7ffee4c43c2

This command uses two key arguments:

• --index-url: Directs pip to find your package on TestPyPI.
• --extra-index-url: Allows pip to fetch any dependencies from the main PyPI repository.

Replace 232e112a1ffb9f21e3b1b7ffee4c43c2 with your specific package name (e.g., harlequin-datafusion if following this post). For additional details, consult the information provided in this post.

To publish to the actual Python Package Index (PyPI) instead:

1. Create an account at https://pypi.org/ if you haven't already.

2. Generate an API token on your PyPI account page.

3. Run:
   

   poetry publish --username __token__ --password <token>
   

The default repository is PyPI, so there's no need to specify it.

Is worth noting that Poetry only supports the Legacy Upload API when publishing your project.

Automated Publishing on GitHub release

Manually publishing each time is repetitive and error-prone, so to fix this problem, let us create a GitHub Action to
publish each time we create a release.

Here are the key steps to publish a Python package to PyPI using GitHub Actions and Poetry:

1. Set up PyPI authentication: You must provide your PyPI credentials (the API token) as GitHub secrets so the GitHub Actions workflow can access them. Name these secrets something like PYPI_TOKEN.

2. Create a GitHub Actions workflow file: In your project's .github/workflows directory, create a new file like publish.yml with the following content:
   

   name: Build and publish python package

   on:
     release:
       types: [ published ]

   jobs:
     publish-package:
       runs-on: ubuntu-latest
       permissions:
         contents: write
       steps:
         - uses: actions/checkout@v3
         - uses: actions/setup-python@v4
           with:
             python-version: '3.10'

         - name: Install Poetry
           uses: snok/install-poetry@v1

         - run: poetry config pypi-token.pypi "${{ secrets.PYPI_TOKEN }}"

         - name: Publish package
           run: poetry publish --build --username __token__

The key is to leverage GitHub Actions to automate the publishing process and use Poetry to manage your package's dependencies and metadata.

Conclusion

Poetry is a user-friendly Python package management tool that simplifies project setup and publication. Its intuitive command-line interface streamlines environment management and dependency installation. It supports plugin development, integrates with other tools, and emphasizes testing for robust code. With straightforward commands for building and publishing packages, Poetry makes it easier for developers to share their work with the Python community.

At LETSQL, we're committed to contributing to the developer community. We hope this blog post serves as a straightforward guide to developing and publishing Python packages, emphasizing best practices and providing valuable resources.
To subscribe to our newsletter, visit letsql.com.

Future Work

As we continue to refine the adapter, we would like to provide better autocompletion and direct reading from files (parquet, csv) as in the DataFusion-cli. This requires a tighter integration with the Rust library without going through the Python bindings.

Your thoughts and feedback are invaluable as we navigate this journey. Share your experiences, questions, or suggestions in the comments below or on our community forum. Let's redefine the boundaries of data science and machine learning integration.

Acknowledgements

Thanks to Dan Lovell and Hussain Sultan for the comments and the thorough review.

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