opensearch-project / opensearch-project/data-prepper

Support for data normalization in arrays

Open
#4,291 10 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

enhancement question
Dominant language
Java
Stars
374
Forks
354
Avg merge
3d 18h
Merged PRs (30d)
8

Description

Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is. Ex. It would be nice to have [...]

Hi, I have a question about ingestion pipeline: is there a standard way to process arrays of data in ingestion processor that is recommended by data prepper? for example, if I have a {‘category’: [‘a’, ‘b’, ‘c’]}, how can I break elements in the array down?

Describe the solution you'd like
A clear and concise description of what you want to happen.

in the example {‘category’: [‘a’, ‘b’, ‘c’]}, ideally it can be broken down like {‘category’: ‘a’,‘category’: ‘b’,‘category’: ‘c’ }?

Describe alternatives you've considered (Optional)
A clear and concise description of any alternative solutions or features you've considered.

Additional context
Add any other context or screenshots about the feature request here.

common use case is the transition from NoSql database arrays to Sql database to support table joins in data warehousing.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reviewing the ingestion pipeline's processor behavior for array-valued fields and how normalized records are represented. Define and implement the requested behavior for {category: [a, b, c]}, then verify that each array element can become a separate category value suitable for SQL joins.

Written by the indexing model from the issue text.

Assessment

Tech stack
java
Domain
data-engineering
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.