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README
Apache-2.0

Ushas

介绍

  • Ushas 是一款在spark基础上进行封装,强化数据血缘治理的组件。传统数据治理中针对spark的表级别血缘判断虽然能一定程度上解决数据的依赖关系,但是对于精确到字段之间的关系识别则显得捉襟见肘。开发此组件的用意是为了能够加强spark在列级血缘上的追踪优势。Ushas代表了我们追求的不仅仅是简单的判断,而是能够精确地捕捉血缘

知识铺垫

我们做的事

软件架构

  • [module]assembly

    assembly模块是为了能够更加方便的获取打包内容,这里移植了spark的原生代码,可以一键自动化打包,在target/scala目录下获取所有jar包
  • [module]dev

    dev模块是为了配置checkstyle的代码规范检测,spark有内置的scala代码规范要求,我们这里也沿用了他的所有要求,输出目录为target/checkstyle-output.xml
  • [module]examples

    example模块是为了提供列级血缘的应用范例
  • [directory]sql

    sql里面包含所有的spark catalyst解析,列级血缘的主要工作都集中在sql包含的三个模块上

安装教程

  1. idea maven 对spark-catalyst module 进行Gnerated Source Code 生成sqlbase.g4的语法树文件

  2. 在跑样例文件时,先设置参数 -DLocal ,再设置 Include with provided scope,用默认本地形式和本地包运行spark

  3. 样例文件位置 examples/src/main/scala/org/apache/spark/examples/lineage/SparkLineageExample.scala

  4. 打包时,如果需要添加hive的插件支持,需要在spark profile中勾选hive

  5. 因为本身就是spark的项目中进行的分离,所以只需要将 spark-hive_2.12-3.1.2.jar,spark-catalyst_2.12-3.1.2.jar 进行替换,即可完成列级血缘的快速部署

效果展示

  1. 准备样例sql : select * from (select substr(a+1,0,1) as c,a+3 as d from (select 1 as a,2 as b))

  2. 样例输出:

c#2
+- c#2
   +- Alias ( substring(cast((a#0 + 1) as string), 0, 1) AS c#2 )
      +- a#0
         +- Alias ( 1 AS a#0 )
  1. 在spark-shell中如何查看列级血缘(API方法)

    df.queryExecution.analyzed.lineageChildren(0).treeString

  2. 在pyspark中如何查看列级血缘(API方法)

    df._jdf.queryExecution().analyzed().lineageChildren().apply(0).treeString()

你可以做的事

  1. 优化现有的代码结构,目前的项目代码结构和spark2.4的结构一致,可以对模块进行针对性的修改,将主要代码集中在一起

  2. 添加新的模块,目前只提供了spark列级血缘的解析方式,但是对血缘的自动存储和展示这块完全是留给commiter的一片空白区域

  3. 现有的rule寻找逻辑优化,目前的寻找对应列关系主要是依托了exprid的全局唯一性,这里因为是遍历寻找,所以会增加耗时,看是否可以进行优化

  4. spark目前是靠UnresolveLogicplan 替换成 resolveLogicplan进行resolve标记,而我们是通过简单的在逻辑计划的lineageresolve中标记为True,并没有给一个规范的样例类,这块若要优化工程量非常大

  5. example中有 通过spark 插件化进行注入的样例,本是希望能够尽少的动源码,在外部进行Rule规则的植入,但是结果不尽如人意,可以看看如何进行插件化的配置

  6. 目前是对hive的数据源进行了寻址,也就是说目前的列级血缘可以对hive 的数据源进行准确的Catalog识别,但是别的外接数据源没有做任何的定义,这块可以由commiter进行丰富

参与贡献

  1. Fork 本仓库

  2. 新建 Feat_xxx 分支

  3. 提交代码

  4. 新建 Pull Request

  5. 帮助

    如何进行issue 和 pr 的关联

    https://docs.github.com/cn/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue

    如何配置 checkstyle 代码检查

    https://blog.csdn.net/qq_31424825/article/details/100050445

    计算机编程术语 (中英文对照)

    https://blog.csdn.net/Dreamcode/article/details/2223043

关于

  1. 为什么选择将项目进行裁剪,而不是将整个spark代码进行迁移?

    目前的需求是支持列级血缘,Ushas也旨在先将列级血缘进行处理和完善,之后不排除会做更多的优化和迭代,逐步的扩大项目架构。 为了精简化打包,快速纳入使用,一次打包只需要5min时间

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简介

This project is used for tracking lineage when using spark. Our team is aimed at enhancing the ability of column relation during logical plan analysis.Spark列级血缘(字段血缘)关系处理。 展开 收起
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