Native tuple sketch functions (tuple_*, Spark 4.2): audit the crate against Java and implement

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难度
5/5
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一周以上
新手友好度
12/100
Issue 类型
功能
描述清晰度
基本清楚
活跃度
活跃
技术栈
java, rust, scala, spark

调研方向

This is a multi-part audit and implementation across the Rust tuple feature of the datasketches crate and Comet's Spark expression support, so it is not a starting point. Begin with the Spark 4.2 tuple function definitions and the existing theta sketch expressions in the Comet source, then compare the crate's summary types and union/intersection policies against DataSketches-Java 6.2.0 fixtures. Done means each tuple_* function runs natively with a Spark fallback before 4.2, backed by committed fixtures and tests.

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描述

area:aggregation area:expressions enhancement

What is the problem the feature request solves?

Part of #7397.

Spark 4.2 adds Tuple sketch functions: tuple_sketch_agg, tuple_union_agg, tuple_intersection_agg, the tuple_difference / tuple_union / tuple_intersection scalars, and tuple_sketch_estimate, tuple_sketch_summary and tuple_sketch_theta, each in integer and double summary variants. None of them run natively in Comet. The crate's tuple feature arrived in datasketches 0.4.0.

Describe the potential solution

  1. Score the family with suggest-native-expression first. This is the lowest priority of the four, because it only exists in the newest Spark version Comet supports.
  2. Do the theta work first, since a tuple sketch is a theta sketch with a summary attached and shares its hashing and set-operation behavior.
  3. Audit the crate's tuple feature against DataSketches-Java 6.2.0 using committed fixtures. The summary types and the policies that combine summaries on union and intersection need to match Spark's exactly, and the serialized summary format has to be readable in both directions.
  4. Implement, test and document as for the other families, with a fallback on Spark versions before 4.2.
主要语言
Scala
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环境准备

从这里开始

  1. 先读完整个 Issue,再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库,在一个分支上完成修改。
  4. 提交 Pull Request,并在描述里引用这个 Issue 编号。

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