PySpark 的 Python 依赖管理：把环境一同送到执行器-未完纪

原作者：Apache Spark 文档贡献者。本文完整译写自官方 Python Package Management，核对日期为 2026-10-05；读取时页面标示为 PySpark 4.2.0 文档。latest 是会变化的地址，实际部署应以集群安装版本为准。

在 YARN、Kubernetes 等集群上运行 PySpark 应用，不能只保证驱动程序所在机器装了依赖：执行器同样需要应用代码和使用的库。本文保留原文的原生文件分发、Conda、Virtualenv、PEX 和 uv 全部章节，其中前三种环境封装路线给出了完整的提交示例。命令均为文档示例，本次没有建立集群或执行作业。

依赖包、分发参数与执行器解释器的对应关系。原创示意图，非运行截图。

为什么驱动端装好依赖还不够

以 Pandas UDF 为例，它的底层实现使用 pyarrow。如果执行器没有安装这个库，就可能出现 ModuleNotFoundError: No module named 'pyarrow'。原文使用下面的 app.py，按 id 分组，对数值列求均值：

import pandas as pd
from pyspark.sql.functions import pandas_udf
from pyspark.sql import SparkSession

def main(spark):
    df = spark.createDataFrame(
        [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
        ("id", "v"))

    @pandas_udf("double")
    def mean_udf(v: pd.Series) -> float:
        return v.mean()

    print(df.groupby("id").agg(mean_udf(df['v'])).collect())

if __name__ == "__main__":
    main(SparkSession.builder.getOrCreate())

后文所有提交命令都假定已经保存此文件，并且驱动端已有与集群匹配的 Spark/PySpark 运行环境。collect() 会把结果拉回驱动端，这个五行数据的示例很小；不能把相同写法直接推广到不受控的大结果集。

一、用 PySpark 原生功能分发 Python 文件

PySpark 可以把单个 .py 文件、ZIP 格式的 Python 包以及 .egg 文件上传给执行器，入口有三个：

设置 spark.submit.pyFiles。
提交时使用 --py-files。
在应用中调用 SparkContext.addPyFile()。

这适合附加自定义 Python 代码：可以分发单个文件，也可以把一个包压缩后上传。addPyFile() 还允许在作业已经启动之后上传代码。

它并不是通用环境管理器。此路径不支持直接加入 Wheel 包，因此不能据此解决带原生代码的依赖。特别是 pyarrow 这类依赖，不能只把下载到的 wheel 随意放进 --py-files 就认为环境完整了。

二、使用 Conda 打包解释器和依赖

Conda 管理 Python 环境；conda-pack 可把现有环境做成可重定位归档。原例为驱动端和执行器建立环境，并把 Python 解释器及相关依赖一起封装：

conda create -y -n pyspark_conda_env -c conda-forge pyarrow pandas conda-pack
conda activate pyspark_conda_env
conda pack -f -o pyspark_conda_env.tar.gz

编者注：-y 自动确认安装，-f 强制写入归档。执行前应核对软件源与输出路径，避免覆盖需要保留的包。原例未固定 Python、pandas、Arrow 版本；实际应用应固定兼容版本，并在与执行器相容的操作系统、CPU 架构和系统库环境中构建。可重定位不等于跨所有平台可运行。

使用 --archives，或者设置 spark.archives（在 YARN 对应 spark.yarn.dist.archives），Spark 会在执行器上自动解压归档。提交应用的原例是：

export PYSPARK_DRIVER_PYTHON=python # YARN/Kubernetes cluster 模式不要设置
export PYSPARK_PYTHON=./environment/bin/python
spark-submit --archives pyspark_conda_env.tar.gz#environment app.py

#environment 是归档解压后的目录别名，必须与 PYSPARK_PYTHON 中的 ./environment/bin/python 对上。PYSPARK_DRIVER_PYTHON 指定驱动端解释器，PYSPARK_PYTHON 指定 PySpark 使用的 Python；两者不能不分部署模式地照抄。YARN 或 Kubernetes 的 cluster 模式应去掉第一行，并清除此前已经设置的驱动解释器变量。

在普通 Python shell 或 notebook 中，原文使用下面的写法。应在创建 Spark 上下文之前配置环境变量；已有会话不能被想当然地视为已经切换环境。

import os
from pyspark.sql import SparkSession
from app import main

os.environ['PYSPARK_PYTHON'] = "./environment/bin/python"
spark = SparkSession.builder.config(
    "spark.archives",  # 在 YARN 中使用 'spark.yarn.dist.archives'
    "pyspark_conda_env.tar.gz#environment").getOrCreate()
main(spark)

交互式 pyspark shell 的对应命令如下：

export PYSPARK_DRIVER_PYTHON=python
export PYSPARK_PYTHON=./environment/bin/python
pyspark --archives pyspark_conda_env.tar.gz#environment

三、使用 Virtualenv 与 venv-pack

Virtualenv 用于建立隔离 Python 环境。自 Python 3.3 起，其中一部分能力成为标准库的 venv 模块。PySpark 可以配合 venv-pack，像使用 conda-pack 一样归档和分发环境：

python -m venv pyspark_venv
source pyspark_venv/bin/activate
pip install pyarrow pandas venv-pack
venv-pack -o pyspark_venv.tar.gz

这里使用的是 POSIX shell 的激活路径和命令。归档内有依赖与解释器入口，但 venv-pack 把 Python 解释器保存为符号链接，所以集群各节点必须安装相同且路径兼容的 Python 解释器。它不是包含独立 Python 二进制的跨机器万能包。

提交时同样利用 --archives 或相应配置自动解包：

export PYSPARK_DRIVER_PYTHON=python # YARN/Kubernetes cluster 模式不要设置
export PYSPARK_PYTHON=./environment/bin/python
spark-submit --archives pyspark_venv.tar.gz#environment app.py

在 YARN、Kubernetes cluster 模式下，依然不能设置 PYSPARK_DRIVER_PYTHON。Python shell 或 notebook 写法为：

import os
from pyspark.sql import SparkSession
from app import main

os.environ['PYSPARK_PYTHON'] = "./environment/bin/python"
spark = SparkSession.builder.config(
    "spark.archives",  # 在 YARN 中使用 'spark.yarn.dist.archives'
    "pyspark_venv.tar.gz#environment").getOrCreate()
main(spark)

交互 shell 写法为：

export PYSPARK_DRIVER_PYTHON=python
export PYSPARK_PYTHON=./environment/bin/python
pyspark --archives pyspark_venv.tar.gz#environment

四、使用 PEX 生成可执行依赖包

PEX 可把指定 Python 依赖组织成 .pex 文件。它类似一个可直接调用的 Python 环境，但仍要使用宿主上的 Python 解释器。原文构建命令是：

pip install pyarrow pandas pex
pex pyspark pyarrow pandas -o pyspark_pex_env.pex

得到的文件可以像解释器一样接收 -c：

./pyspark_pex_env.pex -c "import pandas; print(pandas.__version__)"

原文该命令下展示的输出是 1.1.5。这是旧的示例输出，不是当前安装会得到的版本，也不是本文的实测结果。由于构建命令没有锁定版本，不能用这行输出证明依赖可复现。

.pex 本身不包含 Python 解释器，集群各节点仍需要相同、兼容的解释器。它是普通文件而非待解压目录，所以应通过 spark.files（YARN 为 spark.yarn.dist.files）或者 --files 传输：

export PYSPARK_DRIVER_PYTHON=python # YARN/Kubernetes cluster 模式不要设置
export PYSPARK_PYTHON=./pyspark_pex_env.pex
spark-submit --files pyspark_pex_env.pex app.py

普通 Python shell 或 notebook 使用：

import os
from pyspark.sql import SparkSession
from app import main

os.environ['PYSPARK_PYTHON'] = "./pyspark_pex_env.pex"
spark = SparkSession.builder.config(
    "spark.files",  # 在 YARN 中使用 'spark.yarn.dist.files'
    "pyspark_pex_env.pex").getOrCreate()
main(spark)

交互式 shell 的命令几乎相同：

export PYSPARK_DRIVER_PYTHON=python
export PYSPARK_PYTHON=./pyspark_pex_env.pex
pyspark --files pyspark_pex_env.pex

原文还链接了使用 SparkSession.builder 与 PEX 部署独立 PySpark 的端到端 Docker 示例，其使用建立在 PEX 之上的 cluster-pack，以自动处理创建及上传 PEX 的中间步骤。该外部工程不属于本文已执行或已验证的部署结果。

五、uv run：保留原文短例，并说明缺口

原文新增章节介绍 uv：它可以根据 Python 脚本内联声明的依赖建立环境，在首次运行时解析和安装依赖，从而减少手动环境管理。文档先建议创建名为 uv_run 的包装脚本，展示的命令语法为：

exec uv run [--python <version>] "$@"

编者注：方括号表示可选参数，<version> 表示要替换的值，不能把整行元语法直接作为 shell 脚本运行。如果不指定版本，去掉可选部分后是 exec uv run "$@"；如果指定，则应写成实际选定的版本。原文接着要求赋予脚本执行权限：

chmod +x uv_run
export PYSPARK_PYTHON=./uv_run
pyspark app.py

原文所附脚本用 PEP 723 内联脚本元数据声明依赖：

#!/usr/bin/env -S uv run --script
#
# /// script
# dependencies = [
#   "pandas==2.2.3",
# ]
# ///

import pandas as pd
from pyspark.sql import SparkSession
from pyspark.sql.functions import pandas_udf

spark = SparkSession.builder.master("local[*]").appName("UvRunExample").getOrCreate()

df = spark.createDataFrame(
    [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
    ("id", "v"),
)

@pandas_udf("double")
def mean_udf(v: pd.Series) -> float:
    return v.mean()

print(df.groupby("id").agg(mean_udf(df["v"])).collect())

spark.stop()

编者核查：这段元数据只声明 pandas，脚本还导入 PySpark，Pandas UDF 还需要 Arrow；原例没有交代它们如何进入同一运行环境，也没有完整说明集群节点上的 uv、包装脚本分发、网络依赖解析和 worker 启动方式。local[*] 明确选择本地模式，不是 YARN/Kubernetes 集群部署证明。原文的 pyspark app.py 也不能未经版本核对便当作通用脚本提交方式；本文主要路线使用的是前述 spark-submit。

因此这一节保留为源文提供的探索性短例，不能把它单独称作“复制即可运行”的完整方案。若采用 uv，应先补齐并锁定依赖、确认解释器与 Spark 的兼容条件，再在隔离环境验证 driver 和 worker 的实际启动链。本文没有为缺失条件编造成功结果。

选择路线时核对什么

路线	如何分发	解释器条件	适用边界
原生文件分发	`--py-files` / `addPyFile`	由节点现有环境提供	适合自定义 Python 代码，不解决原生 wheel 依赖
Conda	`--archives`	归档携带环境解释器	仍需 OS、架构、系统库相容
venv-pack	`--archives`	解释器链接依赖节点安装	节点解释器与链接目标必须匹配
PEX	`--files`	不内置解释器	需要兼容 Python 与原生依赖平台
uv 短例	本页未给完整集群分发流程	需额外核实 uv 与 worker 启动环境	依赖声明不完整，本次不视为独立复现方案

不要把凭证、个人配置或无关文件一并封进环境归档。分发的 Python 包最终会在集群执行，应核实包来源、版本锁定与归档内容。这里没有发现示例内存在实际硬编码秘密或由外部输入直接构造 shell 命令的情况，但这不构成完整安全审计结论。

来源与许可

审核方式为静态阅读与来源比对；未安装软件、未打包环境、未连接集群、未执行作业。所有示例输出均已注明来自原文。

原始版权与许可

Apache Spark；Copyright 2014 and onwards The Apache Software Foundation. This product includes software developed at The Apache Software Foundation (http://www.apache.org/). 此处保留与本文引用内容相关的原始 NOTICE；官方 NOTICE 来源。其他组件的密码学与Metrics说明不属于本文转载内容。

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