testcontainers / testcontainers/testcontainers-python
New Container: Spark Connect
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- Python
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描述
What is the new container you'd like to have?
Spark connect introduces a decoupled client-server architecture to allow remote connectivity to spark server, official documentation is here.
It's used by data engineers to distribute data transformation jobs into multiple clusters. Spark connect is an addition to spark with leverages the jvm.
Benefits of having this in container would enable data engineers:
- to be able to tests their workflows without having to go through a cloud provider like Databricks
- prevent the manual setup of jvm which can be quite cumbersome
The most commonly used docker image is apache/spark.
Why not just use a generic container for this?
The implementation of the spark connect server with DockerContainer would expose extra configurations. On corporate projects, the following implementation is required
kwargs = {
"entrypoint": "/opt/spark/sbin/start-connect-server.sh org.apache.spark.deploy.master.Master --packages org.apache.spark:spark-connect_2.12:3.5.2,io.delta:delta-core_2.12:2.3.0 --conf spark.driver.extraJavaOptions='-Divy.cache.dir=/tmp -Divy.home=/tmp' --conf spark.connect.grpc.binding.port=8081",
}
with (
DockerContainer(
"apache/spark",
)
.with_bind_ports(8081, 8081)
.with_env("SPARK_NO_DAEMONIZE", "True")
.with_volume_mapping(pytest_tmp_dir, pytest_tmp_dir, "rw")
.with_kwargs(**kwargs) as container
):
_ = wait_for_logs(container, "SparkConnectServer: Spark Connect server started at")
yield container
The added complexity is due to configuration of the entrypoint, one would need to have expertise in spark connect to launch the server and ensure the proper port exports. There is a compatibility versions to guarantee between spark and the delta-core jar package.
Other references:
Some resources here
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调研方向
首先定位现有的容器集成以及示例中使用的 DockerContainer API。检查 apache/spark 镜像、Spark Connect 入口点、端口 8081 以及 wait_for_logs 的使用方式。完成的标准是:能够使用所需配置启动 Spark Connect 容器,并可靠地检测到其启动。
由索引模型根据 Issue 内容生成。
评估
- 技术栈
- docker, python, spark
- 领域
- data-engineering, devops
- Issue 类型
- 功能
- 难度
- 4/5
- 预计耗时
- 3-5 天
- 活跃度
- 停滞
- 描述清晰度
- 基本清楚
- 新手友好度
- 35/100