Super glad to see nested common subplan elimination finally land. We might need to tune somewhat, but the potential query time savings are huge! And that's (by far) not even the biggest thing we'll launch this week. Stay tuned!
Polars@pola.rs · 2mo agoWe've released Python Polars 1.41. Some of the highlights: • Faster Parquet metadata decoding • Nested common subplan elimination • LazyFrame.gather() Blog post: pola.rs/posts/polars...
We shipped a major release of Polars Cloud Live query profiles. See all data flowing through and exactly which nodes take up your compute. Aside from that it also lands: - Streaming shuffles - Defaulting to our cost based planner - Streaming and broadcasting ASOF joins github.com/pola-rs/pola...
Release Polars Cloud client 0.5.0 · pola-rs/polars-cloud-client
Highlights Launch of the Compute Dashboard This release marks the launch of the compute dashboard that is tied to the cluster directly. This allows for direct compute metrics and advanced query pr...
github.com
In 1-2 weeks we land live query profiling in Polars Cloud. See exactly how many rows are consumed and produced per operation. Which operation takes most runtime, and watch the data flow through live, like water. 😍
ClickBench now runs the Polars streaming engine. Polars is the fastest solution on that benchmark on Parquet file(s) 😎 The speed is there. This year, we will tackle out of core (spill to disk) and distributed to truly tackle scale. benchmark.clickhouse.com#system=-ahi|...
The pre-release of Polars 1.36 is out. Please give it a try so that we can ensure a stable final release with minimal regressions. It lands a lot of goodies: - Extension types - Lazy pivots - Streaming group_by_dynamic - Float16 support - Nested .over() expressions github.com/pola-rs/pola...
Release Python Polars 1.36.0-beta.2 · pola-rs/polars
🏆 Highlights Add Extension types (#25322) 🚀 Performance improvements Reduce HuggingFace API calls (#25521) Use strong hash instead of traversal for CSPE equality (#25537) Fix panic in is_between...
github.com
Polars 1.34.0 is out! Any Polars query can be turned into a generator! Aside from that Polars now properly supports decimal types, scan_iceberg is completely native, cross joins can maintain order and much more. Changelogs here: - github.com/pola-rs/pola... - github.com/pola-rs/pola...
Polars 1.32 is out and it lands a lot! Let's go through a few: 1/4 Selectors are now implemented in Rust and we can finally select arbitrary nested types:
Join me the 24th in SF for a @pola.rs meetup! I will be having a talk about Polars, Polars-Cloud and the upcoming distributed engine. NVIDIA will also be doing a talk about their GPU acceleration with Polars-CuDF Hope to see you there! lu.ma/60b6wfs8
Polars Meetup - Polars Cloud and Acceleration · Luma
Join the second edition of our Polars Meetup with talks from Ritchie Vink (Polars) and Vyas Ramasubramani (NVIDIA) to discuss accelerating and scaling…
lu.ma
This Thursday I will join Lawrence Mitchell from @nvidia on the podium during the NVIDIA GTC in Paris. We'll discuss how we made Polars work on the GPU and how it will scale to multi-GPU in the future. On se voit là-bas ! vivatechnology.com/sessions/ses...
GP1085: Scaling DataFrames With Polars - Theme: undefined
Location: NVIDIA GTC PARIS - Pavillon 7 - June 12 1:00 PM 1:45 PM - CET | Resume: Room: N03 Polars is a query engine with a DataFrame frontend designed for fast, efficient data processing. This sess...
vivatechnology.com
Polars has gotten 4x faster than Polars! 🚀 In the last months, the team has worked incredibly hard on the new-streaming engine and the results pay off. It is incredibly fast, and beats the Polars in-memory engine by a factor of 4 on a 96vCPU machine.
** Sponsor announcement ** Polars is a Supporter of RustWeek! Find out more about them here: pola.rs Thank you @pola.rs for your support! 🙏 More info about RustWeek and tickets: rustweek.org #rustweek #rustlang
This weeks Polars release we shipped initial Unity Catalog support. This makes integration with Databricks much smoother. Writing features are under development and will follow soon. Full release notes: github.com/pola-rs/pola...
Learning polars has been ... actually a joy? It just makes sense to my #rstats #dplyr trained data muscles #databs #python kevinheavey.github.io/modern-polars/
Modern Polars
A side-by-side comparison of the Polars and Pandas libraries.
kevinheavey.github.io
This weeks Polars release has a huge improvement for window functions. They can be an order of magnitude faster. And we can run 20/22 TPC-H queries on the new streaming engine and all on Polars cloud. More will follow soon! ;) See the full release docs here: github.com/pola-rs/pola...
Release Python Polars 1.20.0 · pola-rs/polars
⚠️ Deprecations Make parameter of str.to_decimal keyword-only (#20570) 🚀 Performance improvements Extend functionality on BitmapBuilder and use in Growables (#20754) Specialize first/last agg fo...
github.com
✨ New temporal feature in the next Polars release! ⏲️ dt.replace lets you replace components of Date / Datetime columns ⚡🦀 It's an expressified vectorised rustified version of the Python standard library datetime.replace
We removed serde from our Series struct and saw a significant drop in Polars' binary size (of all features activated). The amount of codegen is huge. 😮
We finally support writing to cloud storage natively and seamlessly!
Join us this Friday if you're eager to see what it can be like to design a recommender while limiting ourselves to just a DataFrame API. It is somewhat unconventional, but a great excuse to show off a Polars trick or two. www.youtube.com/watch?v=U3Fi...
Making a recommender by just using Polars!
YouTube video by probabl
youtube.com
How to “expand” ranges like "3-5" across new rows with the values 3, 4, 5? This comes straight from our Discord server (discord.com/invite/4UfP5...)
Why is there a `struct` data type? A single expression produces a single column, so expressions like `value_counts` need to output structs to map the values to their counts. With that said, do you understand why `.struct.unnest` doesn't break the 1 expr = 1 column principle?
![A screenshot of a Shinylive app using Polars code:
from shiny import App, render, ui
import polars as pl
from pathlib import Path
app_ui = ui.page_fluid(
ui.input_select("cyl", "Select Cylinders", choices=["4", "6", "8"]),
ui.output_data_frame("filtered_data")
)
def server(input, output, session):
df = pl.read_csv(Path(__file__).parent / "mtcars.csv")
@output
@render.data_frame
def filtered_data():
return (df
.filter(pl.col("cyl") == int(input.cyl()))
.select(["mpg", "cyl", "hp"]))
app = App(app_ui, server)](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:hnkswprmt42fkt2huzbkcpcx/bafkreifcr4vfugeei25pfex52gvvvxnukyy4sms4ytpk6isepn5c3j473i)