feathr-ai/feathr

[BUG] non-feature columns get converted into string columns when get result dataframe

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#942 opened on Jan 5, 2023

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 (0 comments) (0 reactions) (0 assignees)Scala (244 forks)batch import
buggood first issue

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Description

Willingness to contribute

No. I cannot contribute a bug fix at this time.

Feathr version

0.9.0

System information

  • OS Platform and Distribution (e.g., Linux Ubuntu 20.0): Linux Ubuntu 20.0)
  • Python version: 3.8
  • Spark version, if reporting runtime issue: 3.3.1

Describe the problem

non-feature columns of the original source data get converted into string column, e.g. 0 (int) -> "0", True (bool) -> "True" string values when getting the feature result.

Tracking information

No response

Code to reproduce bug

No response

What component(s) does this bug affect?

  • Python Client: This is the client users use to interact with most of our API. Mostly written in Python.
  • Computation Engine: The computation engine that execute the actual feature join and generation work. Mostly in Scala and Spark.
  • Feature Registry API: The frontend API layer supports SQL, Purview(Atlas) as storage. The API layer is in Python(FAST API)
  • Feature Registry Web UI: The Web UI for feature registry. Written in React

Contributor guide

Research direction
Investigate the Computation Engine component (Scala Spark) to identify where non feature columns are cast to strings. Look at the feature join and generation logic, possibly in Scala files under the computation engine directory. Check for explicit type conversions or schema inference issues. Since there is no reproduction code, you may need to create a minimal test case to replicate the bug.
Tech stack
pythonscala
Domain
databackend
Issue type
Bug
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
Scala basicsSpark concepts
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
30