db-benchmark
databend
Our great sponsors
db-benchmark | databend | |
---|---|---|
91 | 32 | |
319 | 7,157 | |
0.9% | 2.2% | |
0.0 | 10.0 | |
10 months ago | 4 days ago | |
R | Rust | |
Mozilla Public License 2.0 | GNU General Public License v3.0 or later |
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
-
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/
-
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.
-
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"
-
Pandas vs. Julia โ 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.
-
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
-
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 itโs around an order of magnitude faster than pandas and dplyr (thereโs a nice benchmark here). Itโs also more memory-efficient and can handle larger-than-memory datasets via streaming if needed.
-
Pandas v2.0 Released
If interested in benchmarks comparing different dataframe implementations, here is one:
- Database-like ops benchmark
-
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.
databend
-
Solutions to manage runaway Snowflake costs?
Databend vs. Snowflake: https://github.com/datafuselabs/databend/issues/13059
-
I Accidentally Saved My Company Half a Million Dollars
Indeed, under a pay-as-you-go model, if there's a lack of precise control over the warehouse, such as a 10-minute suspension, it could lead to significant waste. This is because most queries might only take a few seconds, and the rest of the time is wasted. If you find Snowflake expensive, consider Databend. It's an open-source, cost-efficient alternative to Snowflake, and it maintains a consistent product experience with Snowflake.
Open-source: https://github.com/datafuselabs/databend
- Databend โ The Open Source Alternative to Snowflake Worth Considering
-
Anyone have experience with Databend (local or cloud)?
They're advertising as an open source direct competitor with Snowflake, with the ability to store data in parquet files. Github repo (5.6k stars) here.
-
An interesting SQL function in Databend: AI_TO_SQL
Databend has recently introduced an SQL function that generates SQL statements from natural language. This feature can significantly reduce the time required for writing and debugging SQL statements.
- Faster than Rust and C++: the PERFECT hash table
-
Parsing SQL with Rust
Hi, we used to use sqlparser in [Databend](https://github.com/datafuselabs/databend). But at last we decide to write our own sqlparser using nom-rule.
-
Databend v1.0
Link to Github: https://github.com/datafuselabs/databend
- Databend 1.0 Release | Blog | Databend
- Open source Snowflake alternative in Rust
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
arrow-datafusion - Apache DataFusion SQL Query Engine
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
duckdb-rs - Ergonomic bindings to duckdb for Rust
DataFramesMeta.jl - Metaprogramming tools for DataFrames
datafuse - An elastic and reliable Cloud Warehouse, offers Blazing Fast Query and combines Elasticity, Simplicity, Low cost of the Cloud, built to make the Data Cloud easy [Moved to: https://github.com/datafuselabs/databend]
sktime - A unified framework for machine learning with time series
barrel - ๐ข A database schema migration builder for Rust
arrow2 - Transmute-free Rust library to work with the Arrow format
ClickHouse - ClickHouseยฎ is a free analytics DBMS for big data