db-benchmark
influxdb_iox
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db-benchmark | influxdb_iox | |
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91 | 14 | |
319 | 1,803 | |
0.9% | - | |
0.0 | 9.9 | |
10 months ago | 7 months ago | |
R | Rust | |
Mozilla Public License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
db-benchmark
- Database-Like Ops 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.
[1] https://h2oai.github.io/db-benchmark/
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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 2.0 (with pyarrow) vs Pandas 1.3 - Performance comparison
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.
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Pandas v2.0 Released
If interested in benchmarks comparing different dataframe implementations, here is one:
https://h2oai.github.io/db-benchmark/
- Database-like ops benchmark
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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.
influxdb_iox
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InfluxDB 3.0 Infinite Observability with qryn-iox
Watch out for the AGPL minio <https://github.com/metrico/iox-community/blob/155a14bb5e8e32...> the almost certainly AGPL grafana <https://github.com/grafana/grafana/blob/v10.1.1/LICENSE> and always eye anyone who uses :latest images with healthy suspicion
That said, influx_iox itself appears to be Apache 2 (and/or MIT?) https://github.com/influxdata/influxdb_iox/blob/main/LICENSE...
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InfluxDB 3 is out, OSS commits have been tried up - is this the end?
have you looked at https://github.com/influxdata/influxdb_iox ? that's where the development for the new version is done.
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InfluxData releases InfluxDB 3.0 product suite for time series analytics
As I understand, InfluxDB 3 is just a re-branding of InfluxDB IOx. Then its' performance can be not very good comparing to Prometheus-like systems.
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Production grade databases in Rust
InfluxDB iox
- Anyone had a success story of replacing C++ with Go?
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InfluxDB announces their new storage engine written in Rust
Don't know how much is open or closed, but they were doing some development in the open: https://github.com/influxdata/influxdb_iox
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Welcome to InfluxDB IOx: InfluxData’s New Storage Engine
Just want to say congratulations to the team!
2 years and 9,500+ commits is a hell of a feat.
https://github.com/influxdata/influxdb_iox
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Rust is showing a lot of promise in the DataFrame / tabular data space
Already is: https://github.com/influxdata/influxdb_iox Just still a work in progress.
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Anyone using RDS IAM authentication in their app?
It looks like this crate is the workaround for that. But there's a PR on SQLX opened a couple days ago that will fix the issue.
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Rust and what it needs to gain space in computation-oriented applications
You should check out polars, datafusion, influxdb iox and databend, all written in native Rust and powered by the Apache Arrow format. Polars in particular is pretty dam fast and has bindings for Python.
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
databend - 𝗗𝗮𝘁𝗮, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜. Modern alternative to Snowflake. Cost-effective and simple for massive-scale analytics. https://databend.com
arrow-datafusion - Apache DataFusion SQL Query Engine
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
DataFramesMeta.jl - Metaprogramming tools for DataFrames
orioledb - OrioleDB – building a modern cloud-native storage engine (... and solving some PostgreSQL wicked problems) 🇺🇦
sktime - A unified framework for machine learning with time series
mimir - ⚡ Supercharged Flutter/Dart Database