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Having done a bit of data engineering in my day, I'm growing more and more allergic to the DataFrame API (which I used 24/7 for years). From what I've seen over the past ~10 years, 90+% of use cases would be better served by SQL, both from the development perspective as well as debugging, onboarding, sharing, migrating etc.
Give an analyst AWS Athena, DuckDB, Snowflake, whatever, and they won't have to worry about looking up what m6.xlarge is and how it's different from c6g.large.
I agree with this 100%. The creator of duckdb argues that people using pandas are missing out of the 50 years of progress in database research, in the first 5 minutes of his talk here [1].
I've been using Malloy [2], which compiles to SQL (like Typescript compiles to Javascript), so instead of editing a 1000 line SQL script, it's only 18 lines of Malloy.
I'd love to see a blog post comparing a pandas approach to cleaning to an SQL/Malloy approach.
[1] https://www.youtube.com/watch?v=PFUZlNQIndo [2] https://www.malloydata.dev/
> The creator of duckdb argues that people using pandas are missing out of the 50 years of progress in database research, in the first 5 minutes of his talk here.
That's pandas. Polars builds on much of the same 50 years of progress in database research by offering a lazy DataFrame API which does query optimization, morsel-based columnar execution, predicate pushdown into file I/O, etc, etc.
Disclaimer: I work for Polars on said query execution.
In the same talk, Mark acknowledges that "for data science workflows, database systems are frustrating and slow." Granted DuckDB is an attempt to fix that, most data scientists don't get to choose what database the data is stored in.
That is a false dichotomy. You can use SQL tools but still have to choose the instance type.
Especially when considering testability and composability, using a DataFrame API inside regular languages like Python is far superior IMO.
We all have allergies. I'm allergic to 1000 line SQL queries which include functions that are only usable for a specific flavor and version of SQL.
Fun aside - I actually used polars for a bit - first time I tried it, I actually thought it was broken, because it finished processing so quickly I thought it silently exited or something.
So I'm definitely a fan, IF you need the DataFrame API. My point was that most people don't need it and it's oftentimes standing in the way. That's all.
I think your argument focuses a lot on the scenario where you already have cleaned data (i.e., data warehouse). I and many other data engineers agree, you're better off with hosting it on SQL RDBMS.
However, before that, you need a lot of code to clean the data and raw data does not fit well into a structured RDBMS. Here you choose to either map your raw data into row view or a table view. You're now left with the choice of either inventing your own domain object (row view) or use a dataframe (table view).
I agree, but there are other possibilities in between those two extremes, like Quivr [1]. Schemas are good, but they can be defined in Python and you get a lot more composability and modularity than you would find in SQL (or pandas, realistically).
100% agree. I've also worked as a data engineer and came to the same conclusion. I wrote up a blog which went into a bit more depth on the topic here: https://www.robinlinacre.com/recommend_sql/
I recently had to create a reproducible version of incredibly complicated and messy R concoctions our data scientists came up with.
I did it with pandas without much experience with it and a lot of AI help (essentially to fill in the blanks the data scientists had left, because they only had to do the calculation once).
I then created a polars version which uses lazyframes. It ended up being about 20x faster than the first version. I did try to do some optimizations by hand to make the execution planner work even better which I believe paid off.
If you have to do a large non interactive analytical calculation (i.e. not in a notebook) polars seems to be way ahead imo!
I do wish that it was just as easy to use as a rust library though.. the focus however seems to be on being competitive in python land mainly.
Out of curiosity, what makes a rust library easier to use? Could you expand on that?
OG polars announcement: https://news.ycombinator.com/item?id=23768227
Never forget! Crazy to see how far it's come. And how lackluster the initial reception on HN was back then.
Love it!
Still don't get why one of the biggest player in the space, Databricks is overinvesting in Spark. For startups, Polars or DuckDB are completely sufficient. Other companies like Palantir already support bring your own compute.
That's a good question! Especially after Frank McSherry's COST paper [1], it's hard to imagine where the sweet spot for Spark is. I guess for Databricks it makes sense to push Spark, since they are the ones who created it. In a way, it's their competitive advantage.
[1]: https://www.usenix.org/system/files/conference/hotos15/hotos...
Databricks is targeting large enterprises, who have a variety of users. Having both Python and SQL as first class languages is a selling point.
I absolutely love Polars. I work on some unholy dirty data and the ease of use, chaining, speed are a godsend. One dataset that previously took 40 minutes in Pandas now takes two minutes in Polars. Granted, the Pandas query could be optimized, but out of the box, Polars eats pandas when it comes to speed and efficiency.
I basically ditched SQL for most of my analytical work because it's way easier to understand for my juniors (we're not technically a tech team) so it's a total win in my eyes.
Been a polars fan for a loooong time. Happy to see the team ship their product and I hope it does well!
Polars is certainly better than pandas doing things locally. But that is a low bar. I’ve not had great experience using Polars on large enough datasets. I almost always end up using duckdb. If I am using SQL at the end of the day, why bother starting with Polars? With AI these days, it’s ridiculously fast to put together performant SQLs. Heck you can even make your own grammar and be done with it.
SQL is definitely easier and faster to compose than any dataframe syntax but I think pandas syntax (via slicing API) is faster to type and in most cases more intuitive but I still use polars for all df-related tasks in my workflow since it's more structured and composable (although needs more time to construct but that's a cost I'm willing to take when not simply prototyping). When in an ipython session, sql via duckdb is king. Also: python -m chdb "describe 'file.parquet'" (or any query) is wonderful
I guess if it’s too large to be performant than SQL can be the way to go. I avoid sql for one off tasks though as I can more easily grok transformations in polars code than sql queries.
you can use Ibis if you want a dataframe UI on top of DuckDB (or a number of other query engines, including Polars)
I guess could be a good contender for replacing spark, however, I suspect the fact spark is free and open source, which forms a community around it, means that dpolars might struggle to gain traction, when it's gated by a credit card.
I would expect a better contender for Spark to be something that's actually open source, such as https://github.com/apache/datafusion-ballista
I don't understand. Can I use distributed Polars with my own machines or do I have to buy cloud compute to run distributed queries (I don't want that). If not, is this planned?
On-premises is in the works. We expect this in a couple of months. Currently it is managed on AWS only.
Hmm so how does the polars SQLContext stack up against duckdb? And can both cope with a distributed polars?
It feels like we are on the path to reinventing BigQuery.
Hi, I am the original author and CEO of Polars. We are not focused on SQL at this time and provide a DataFrame native API.
Polars cloud will for the moment only support our DataFrame API. SQL might come later on the roadmap, but since this market is very saturated, we don't feel there is much need there.
Polars is great, absolute best of luck with the launch
Out of curiosity and because I don't want to create a test account right now:
How does billing with "Deploy on AWS" work? Do I need to bring my own AWS account and Polars is payed for the image through AWS or am I billed by Polars and they pass a share to AWS. In other words do I have a contract primarily with AWS or Polars?
Your billing partner is AWS. Polars' markup is on your AWS bill.
Cool. But abstract away the infra knowledge to the actual instance types. Instead I’d expect the polars cloud abstraction to find me the most cost effective (spot instance) that meets my cpu and memory reqs and disk reqs. Why do I have to give it — looking at the example — the AWS instance type?
You don't have to. Passing cpu and memory works as well.
pc.ComputeContext{
cpus=4,
memory=16
}
Maybe just me, but for anyone else who was confused
- Polars (Pola.rs) - the DataFrames library that now has a cloud version
- Polar (Polar.sh) - Payments and MoR service built on top of Stripe
- Polar, the authorization DSL created by Oso
It's a common name
Is there any distributed polars for non Polars Cloud?
EDIT: nevermind see same question in this thread. The answer is no!
How does Polars compare to FireDucks?
https://fireducks-dev.github.io/docs/benchmarks/
insert obama awards obama meme
I thought this was about my favorite sparkling water brand at first glance.
can i run a distributed computation in pola.rs cloud on my own AWS infra? or do I need to run it on-prem?
So competing with SnowFlake?
EDIT: I think the below is correct, but I’ve just seen in the main product landing page that for a certain benchmark it’s an order of magnitude cheaper AND faster than AWS glue, so that’s the target market by the looks of things.
——
I don’t think so - probably more in the realms of spark and, based on the roadmap, airflow.
For me it would be about doing big data analytics / dashboarding / ML or DS data prep.
My understanding is that Snowflake plays a lot in the data warehouse/lakehouse space, so is more central to data ops / cataloguing / SSOT type work.
But hey that’s all first impressions from the press release.
can you dive a bit deeper into the comparison with spark rdd
I am not an expert on Spark RDDs, but AFAIK they are a more low-level data structure that offer resilience and a lower level map-reduce API.
Polars Cloud maps the Polars API/DSL to distributed compute. This is more akin to Spark's high level DataFrame API.
With regard to implementation, we create stages that run parts of Polars IR (internal representation) on our OSS streaming engine. Those stages run on 1 or many workers create data that will be shuffled in between stages. The scheduler is responsible for creating the distributed query plan and work distribution.
SnowFlake, Polars, DucksDB, FireBase, FireDuck...
I guess the next product will be IceDuck.
What is wrong with you DB people :))).
How does it relate to Apache DataFusion/Ballista?
It's an open-core competitor that started more from the "DataFrames for Python" end of the spectrum, where DataFusion went pretty strong into "we can handle SQL" (while still having a dataframes API).