Db-benchmark Alternatives

db-benchmark reviews and mentions

• Database-Like Ops Benchmark
  1 project | news.ycombinator.com | 28 Jan 2024
• Polars
  11 projects | news.ycombinator.com | 8 Jan 2024

  Real-world performance is complicated since data science covers a lot of use cases.

  If you're just reading a small CSV to do analysis on it, then there will be no human-perceptible difference between Polars and Pandas. If you're reading a larger CSV with 100k rows, there still won't be much of a perceptible difference.

  Per this (old) benchmark, there are differences once you get into 500MB+ territory: https://h2oai.github.io/db-benchmark/

• DuckDB performance improvements with the latest release
  8 projects | news.ycombinator.com | 6 Nov 2023

  I do think it was important for duckdb to put out a new version of the results as the earlier version of that benchmark [1] went dormant with a very old version of duckdb with very bad performance, especially against polars.

  [1] https://h2oai.github.io/db-benchmark/

• Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
  16 projects | news.ycombinator.com | 7 Oct 2023

  https://news.ycombinator.com/item?id=33270638 :

  > Apache Ballista and Polars do Apache Arrow and SIMD.

  > The Polars homepage links to the "Database-like ops benchmark" of {Polars, data.table, DataFrames.jl, ClickHouse, cuDF, spark, (py)datatable, dplyr, pandas, dask, Arrow, DuckDB, Modin,} but not yet PostgresML? https://h2oai.github.io/db-benchmark/ *

  LLM -> Vector database: https://en.wikipedia.org/wiki/Vector_database

  /? inurl:awesome site:github.com "vector database"

• Pandas vs. Julia – cheat sheet and comparison
  7 projects | news.ycombinator.com | 17 May 2023

  I agree with your conclusion but want to add that switching from Julia may not make sense either.

  According to these benchmarks: https://h2oai.github.io/db-benchmark/, DF.jl is the fastest library for some things, data.table for others, polars for others. Which is fastest depends on the query and whether it takes advantage of the features/properties of each.

  For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.

• Any faster Python alternatives?
  6 projects | /r/learnprogramming | 12 Apr 2023

  Same. Numba does wonders for me in most scenarios. Yesterday I've discovered pola-rs and looks like I will add it to the stack. It's API is similar to pandas. Have a look at the benchmarks of cuDF, spark, dask, pandas compared to it: Benchmarks

• Pandas 2.0 (with pyarrow) vs Pandas 1.3 - Performance comparison
  1 project | /r/datascience | 8 Apr 2023

  The syntax has similarities with dplyr in terms of the way you chain operations, and it’s around an order of magnitude faster than pandas and dplyr (there’s a nice benchmark here). It’s also more memory-efficient and can handle larger-than-memory datasets via streaming if needed.

• Pandas v2.0 Released
  5 projects | news.ycombinator.com | 3 Apr 2023

  If interested in benchmarks comparing different dataframe implementations, here is one:

  https://h2oai.github.io/db-benchmark/

• Database-like ops benchmark
  1 project | /r/dataengineering | 16 Feb 2023
• Python "programmers" when I show them how much faster their naive code runs when translated to C++ (this is a joke, I love python)
  2 projects | /r/ProgrammerHumor | 17 Jan 2023

  Bad examples. Both numpy and pandas are notoriously un-optimized packages, losing handily to pretty much all their competitors (R, Julia, kdb+, vaex, polars). See https://h2oai.github.io/db-benchmark/ for a partial comparison.

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