Bistro

Data

data processing engine

SHOW HN2018

95points
29comments
#456of 2,155 in 2018
Top 22%of 2018 by points

How HN took it

MixedNo clear lean

4 positive, 6 neutral or asking, 1 mixed, 6 critical, out of 17 comments about the project. Thread score -0.13.

Idea: 4 for, 2 againstDocs: 0 for, 3 againstExecution: 0 for, 1 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

  • Ideas around linking and column operations are interesting

    From 3 comments
    • Ok so you're defining new operations on top of existing primitives. Makes sense! The concepts look more interesting than the library right now (it doesn't move the needle for me…
      agibsonccc on HN
    • I'm working on a columnar Python DSL right now, I think of it like SQLAlchemy for Pandas/Spark/Flink. My goal is to create a language that makes the cumbersome parts of the…
      munro on HN
    • This is neat. Vectorspace based AI calculations will benefit from this approach. Great work!
      KasianFranks on HN

Objections

  • Not clear what problem it solves or why use it over SQL or pandas

    From 3 comments
    • An example would be great. Can you show how to do a given task with SQL, map/reduce, and your framework? Because right now I have no idea why I’d choose to learn this new stuff…
      jitl on HN
    • The core looks close enough to dataframes that I'd be curious to know how you compare to tablesaw: https://github.com/jtablesaw/tablesaw This looks neat but I'm not sure why I…
      agibsonccc on HN
    • Might new a cool idea, but not nearly fleshed out enough. I think a larger example instead of just individuals lines of code would be useful. Show a toy widget sales spreadsheet.…
      jnordwick on HN
  • Lacks documentation and examples

    From 2 comments
    • Agreed -- a motivating example in the Readme would really help people totally new to this concept grasp the how/why of the project.
      hoprocker on HN
    • Might new a cool idea, but not nearly fleshed out enough. I think a larger example instead of just individuals lines of code would be useful. Show a toy widget sales spreadsheet.…
      jnordwick on HN
  • Code style issues

    From 1 comment
    • Sorry for being that guy, but I just clicked into a random file in src to read the code, and found the code style (indentation etc.) to be quite weird…
      krat0sprakhar on HN

Requests

  • Persistence and connectors such as Arrow, Parquet or JDBC

    From 2 comments
    • The arrow integration and persistence and connecting this to jdbc would be an MVP to me that would at least allow folks to imitate pandas. While you do have new ideas here if…
      agibsonccc on HN
    • At the physical (storage) level it is in-memory and organized as a column store, that is, internally it stores a list of tables (with no data) and a list of columns (each being a…
      asavinov on HN
  • Python DSL or Python API

    From 2 comments
    • I'm working on a columnar Python DSL right now, I think of it like SQLAlchemy for Pandas/Spark/Flink. My goal is to create a language that makes the cumbersome parts of the…
      munro on HN
    • You are right - it is an MVP, and the goal is to choose a direction. In fact, I am still not sure in which direction to go: * JavaScript data processing framework for in-browser…
      asavinov on HN
  • Backend agnostic execution such as Spark or Flink

    From 1 comment
    • Ok so you're defining new operations on top of existing primitives. Makes sense! The concepts look more interesting than the library right now (it doesn't move the needle for me…
      agibsonccc on HN

Questions

  • How does it differ from views or derived columns

    From 1 comment
    • I skimmed the readme but didn't see the answer to what I regard as a basic question. How is this different from a view? I can easily make derived columns based on functions and…
      nickpeterson on HN
  • How does it compare to tablesaw, SAS DATA steps, or Spark

    From 2 comments
    • The core looks close enough to dataframes that I'd be curious to know how you compare to tablesaw: https://github.com/jtablesaw/tablesaw This looks neat but I'm not sure why I…
      agibsonccc on HN
    • How do that compare to SAS software (https://en.wikipedia.org/wiki/SAS_(software)) ? Particularly the "DATA" steps.
      julienfr112 on HN
  • Does it support in-memory only, sharding, or time series

    From 2 comments
    • Is it in-memory? Does it support replication or sharding? What's the main use-case? How does it differ from ORC, Parquet or Arrow? The repository doesn't have any information.
      buremba on HN
    • Might new a cool idea, but not nearly fleshed out enough. I think a larger example instead of just individuals lines of code would be useful. Show a toy widget sales spreadsheet.…
      jnordwick on HN

Alternatives named

  • tablesaw

    From 1 comment
    • The core looks close enough to dataframes that I'd be curious to know how you compare to tablesaw: https://github.com/jtablesaw/tablesaw This looks neat but I'm not sure why I…
      agibsonccc on HN
  • pandas

    From 1 comment
    • Bistro is not about a physical model and columnar (physical) representation although it relies on it. It is about logical level of representation and processing. There are at…
      asavinov on HN
  • Spark

    From 1 comment
    • The core looks close enough to dataframes that I'd be curious to know how you compare to tablesaw: https://github.com/jtablesaw/tablesaw This looks neat but I'm not sure why I…
      agibsonccc on HN
  • SAS

    From 1 comment
    • How do that compare to SAS software (https://en.wikipedia.org/wiki/SAS_(software)) ? Particularly the "DATA" steps.
      julienfr112 on HN
  • Vertica

    From 1 comment
    • Interesting idea. Columnar ETL can be quite efficient in some scenarios because frequently an ETL transformation (e.g. calculating a new column) effectively modifies an existing…
      dgudkov 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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