compare-go-json
db-benchmark
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compare-go-json | db-benchmark | |
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5 | 91 | |
18 | 320 | |
- | 2.2% | |
0.0 | 0.0 | |
almost 2 years ago | 9 months ago | |
Go | R | |
- | Mozilla Public License 2.0 |
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compare-go-json
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The fastest tool for querying large JSON files is written in Python (benchmark)
For me OjG (https://github.com/ohler55/ojg) has been great. I regularly use it on files that can not be loaded into memory. The best JSON file format for multiple record is one JSON document per record all in the same file. OjG doesn't care if they are on different lines. It is fast (https://github.com/ohler55/compare-go-json) and uses a fairly complete JSONPath implementation for searches. Similar to jq but using JSONPath instead of a proprietary query language.
I am biased though as I wrote OjG to handle what other tools were not able to do.
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OjG now has a tokenizer that is almost 10 times faster than json.Decode
jsoniter is json-iterator/go. It is the 3rd column at https://github.com/ohler55/compare-go-json
The title says it all. The new tokenizer here https://github.com/ohler55/ojg and the benchmarks and comparison to other JSON packages is here https://github.com/ohler55/compare-go-json.
You'll find some examples in second link that does the benchmark. https://github.com/ohler55/compare-go-json.
db-benchmark
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Polars
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/
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DuckDB performance improvements with the latest release
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.
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Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
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"
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Pandas vs. Julia – cheat sheet and comparison
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.
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Any faster Python alternatives?
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
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Pandas v2.0 Released
If interested in benchmarks comparing different dataframe implementations, here is one:
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Python "programmers" when I show them how much faster their naive code runs when translated to C++ (this is a joke, I love python)
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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Best alternative to Pandas 2023?
And what's your rating scale? Objectively, pandas loses in performance against everything relevant. It has a wonky syntax that requires using lambda all over the place or to retype your df name at least twice for many operations.
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Tutorial on Intro to Rust Programming
There has been an upward trend in opensource tools written in Rust with interfaces to python eg: pydantic (moved to Rust in the recent release), polars which is very fast as indicated in the H2Oai benchmarks.
- How do I work with GIGANTIC csv files (20-100 gigabytes)?
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
arrow-datafusion - Apache Arrow DataFusion SQL Query Engine
jsoniter - A high-performance 100% compatible drop-in replacement of "encoding/json"
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
databend - 𝗗𝗮𝘁𝗮, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜. Modern alternative to Snowflake. Cost-effective and simple for massive-scale analytics. https://databend.com
orjson - Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
DataFramesMeta.jl - Metaprogramming tools for DataFrames
sktime - A unified framework for machine learning with time series
disk.frame - Fast Disk-Based Parallelized Data Manipulation Framework for Larger-than-RAM Data
arrow2 - Transmute-free Rust library to work with the Arrow format
datatable - A Python package for manipulating 2-dimensional tabular data structures
DataFrame - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage