delta-rs
db-benchmark
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delta-rs | db-benchmark | |
---|---|---|
28 | 91 | |
1,820 | 319 | |
6.1% | 0.9% | |
9.7 | 0.0 | |
about 10 hours ago | 10 months ago | |
Rust | R | |
Apache License 2.0 | Mozilla Public 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.
delta-rs
- Delta-rs – a Rust-based implementation of deltalake
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Delta Lake vs. Parquet: A Comparison
I work at Databricks, but am pretty must just an OSS nerd, mainly focusing on Delta Rust recently: https://github.com/delta-io/delta-rs
I did some keyword research and wrote this post cause lots of folks are doing searches for Delta Lake vs Parquet. I'm just trying to share a fair summary of the tradeoffs with folks who are doing this search. It's a popular post and that's why I figured I would share it here.
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Working with Rust
Seeing a lot of great libraries coming out with python bindings in the data world e.g delta-rs Polars. I see it growing in this space as a C++ alternative
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Ideas/Suggestions around setting up a data pipeline from scratch
If I’m not misunderstanding, you could both decode the gRPC protobuf AND write to delta lake in Rust. Tonic, Delta-rs.
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Delta-rs with upserts
https://github.com/delta-io/delta-rs/issues/850 … looks like it’s on the roadmap!
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Read and filter delta files on Azure from a .net application
Microsoft talk a lot about OneLake and that the delta file format will be the standard during the build conference. Is it only me that find it strange that their marketing team talks so much about the delta format when they do not even provide a library to work with the delta format from .net? It would be easy for them to maintain bindings to https://github.com/delta-io/delta-rs but also provide a reader that support V-Order https://learn.microsoft.com/en-us/fabric/data-engineering/delta-optimization-and-v-order?tabs=sparksql
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Polars query engine 0.29.0 released
I know someone will be adding this on the python side in the coming weeks. On the rust side you can use delta-rs with polars. Though you would be compiling both arrow2 and arrow-rs, so that's quite heavy.
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Delta Lake without Databricks?
You don’t need DBX to use Delta Lake. You can use S3 as the backend and just use the Python Delta Lake library. It works great! https://github.com/delta-io/delta-rs
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Seeking Recommendations for a Master Data Management Tool
Maybe if I get some free time soon I can formalize into a working example. Been wanting an excuse to try similar concept in delta-rs and polars/duckdb vs databricks/spark vs iceberg/polars.
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Opportunity to contribute to a popular Rust data project (delta-rs)
delta-rs is a native Rust library for Delta Lake. It's a better way to store data than Parquet files and is fundamentally important library for the Rust data ecosystem. It's tightly integrated with Polars and Datafusion and there is a lot of interesting Rust work to be done.
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.
What are some alternatives?
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
roapi - Create full-fledged APIs for slowly moving datasets without writing a single line of code.
arrow-datafusion - Apache DataFusion SQL Query Engine
materialize - The data warehouse for operational workloads.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
databend - 𝗗𝗮𝘁𝗮, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜. Modern alternative to Snowflake. Cost-effective and simple for massive-scale analytics. https://databend.com
kafka-delta-ingest - A highly efficient daemon for streaming data from Kafka into Delta Lake
DataFramesMeta.jl - Metaprogramming tools for DataFrames
delta-oss
sktime - A unified framework for machine learning with time series