mlsql

AI

natural language to sql

SHOW HN2019

209points
45comments
#184of 2,195 in 2019
Top 9%of 2019 by points

How HN took it

CuriousMostly questions and neutral comments

3 positive, 17 neutral or asking, 0 mixed, 9 critical, out of 29 comments about the project. Thread score -0.20.

Idea: 2 for, 5 againstExecution: 0 for, 2 againstPerformance: 0 for, 2 against

The thread, comment by comment

Each row is a top-level comment and its replies, in HN's order. Select a cell to open that comment on HN.

positive neutral or mixed critical about another commenter or something else the maker

What HN said

What people liked

  • Interesting open source example of natural language to SQL work.

    From 3 comments
    • It's great to see a nice open source example of work in this area. It would be helpful if there were also some examples of where the model fails either by not being able to…
      michaelmior on HN
    • Very interesting. I did a similar project for my machine learning class in grad school. I'm curious what kind of architecture/model framework you used for this.
      o10449366 on HN
    • does it only work with a question that returns a single answer or it can be used to build something like…
      c8g on HN

Objections

  • Requires too many resources (4 GB Docker image).

    From 2 comments
    • Nice. Very interesting. But 4 gigs just to get I running. That's probably more than the DB I'm trying to query.
      amolo on HN
    • Yes, I don’t know anything about how/what this is running, but I immediately can’t use it based on requirements. Too bad.
      jtbayly on HN
  • Natural language is too imprecise compared to explicit SQL.

    From 2 comments
    • I might be too much of a control freak to give up the explicit precision of SQL.
      rezeroed on HN
    • I think anyone who uses sql would find this tool (and a lot of similar abstractions) too imprecise, verbose, and error prone. What's the longest bridge in mariland - or was it…
      sheeshkebab on HN
  • Complex real-world queries are hard to express in English.

    From 1 comment
    • I feel that SQL queries that people will need help with are not the SQL 101 examples shown here. Looking at the queries I write as a product manager, I wouldn't have the…
      ohadron on HN

Requests

  • Show examples of where the model fails.

    From 1 comment
    • It's great to see a nice open source example of work in this area. It would be helpful if there were also some examples of where the model fails either by not being able to…
      michaelmior on HN
  • Provide a demo environment.

    From 1 comment
    • From a product managers pov this looks very interesting - is there a demo environment available somewhere to play with this?
      monkeydust on HN

Questions

  • What architecture or model framework was used?

    From 1 comment
    • Very interesting. I did a similar project for my machine learning class in grad school. I'm curious what kind of architecture/model framework you used for this.
      o10449366 on HN
  • How does it handle case-sensitive string matching?

    From 2 comments
    • The example is probably wrong or not working since the English questions uses "throgs neck" in lower case instead of the captialized "Throgs Neck" version. Those are two…
      LunaSea on HN
    • Aren't the queries case insensitive by default?
      laurent123456 on HN

Alternatives named

  • Microsoft SQL Server English Query

    From 1 comment
    • Ah, this brings back fun memories. Over a decade ago, Microsoft SQL Server had English Query to do the same thing. Developers had to define all the words & relationships that…
      BrentOzar on HN
  • SQLova (naver/sqlova)

    From 1 comment
    • This is a wrapper for a pretrained model. Probably a better github link would be https://github.com/naver/sqlova/
      mynegation on HN
  • ThoughtSpot

    From 1 comment
    • does it only work with a question that returns a single answer or it can be used to build something like…
      c8g on HN

Category, keywords, summaries and labels on this page were made by AI (Thread Reader v2) and not checked by hand. Each point links to the comments it came from.

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