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Comment by stevesimmons | Hacker News Reader

Arrow + Parquet is brilliant!

Right now I'm writing tools in Python (Python!) to analyse several 100TB datasets in S3. Each dataset is made up of 1000+ 6GB parquet files (tables UNLOADed from AWS Redshift db). Parquet's columnar compression gives a 15x reduction in on-disk size. Parquet also stores chunk metadata at the end of each file, allowing reads to skip over most data that isn't relevant.

And once in memory, the Arrow format gives zero-copy compatibility with Numpy and Pandas.

If you try this with Python, make sure you use the latest 2.0.0 version of PyArrow [1]. Two other interesting libraries for manipulating PyArrow Tables and ChunkedArrays are fletcher [2] and graphique[3].

[1] I use: conda install -c conda-forge pyarrow python-snappy

[2] https://github.com/xhochy/fletcher

[3] https://github.com/coady/graphique

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Yeah, Parquet is awesome. One of the things we really want to do here is to push DataFusion (the Rust based SQL execution engine) to work on Parquet files, but to push down predicates and other things and operate on the data while it's compressed.

You pay such a high overhead marshalling that data into an Arrow RecordBatch. Best thing ever is to work with the Parquet file and not even decompress the chunks that you don't need. Of course, this assumes that you're writing summary statistics as part of the metadata, which we plan to do.

There's rudimentary statistics support, but I've found that it accounts for a lot of the write time (I wrote some tests last weekend, I can ping your team when I put them on GH).

Improving our stats writing could yield a lot of benefits. I'll open JIRAs for this in the next few days.

Parquet + Arrow reminds me of a fast SQL engine on data lake called Dremio.

https://www.dremio.com/webinars/apache-arrow-calcite-parquet...

They also have an OSS version in GitHub.

They're heavily into Arrow. A few years ago they contributed Gandiva, an LLVM expression compiler for super fast processing. https://arrow.apache.org/blog/2018/12/05/gandiva-donation/

It's one of the reasons I like being all in on Arrow. Why do everything ourselves when a ton of other smart people are working on this too?

Talking about Gandiva, something that's open for taking: https://issues.apache.org/jira/browse/ARROW-5315 (creating Gandiva bindings for Rust).

I think DataFusion is mature enough that we could plug in Gandiva into it.

Disclaimer: I work on the Arrow Rust implementation

Ah yes, of course, hi Nevi :). Thank you again for all your work on the Rust implementation. We're obviously big fans.

Gandiva bindings is definitely something we should look into, but I'm guessing there's much lower hanging fruit within DataFusion in terms of optimizing, particularly for our use case.

Thanks Paul :)

I think with compute functions/kernels, we're sitting under a grapevine, so we'll be able to add a lot to Arrow without yet needing Gandiva bindings. The Rust stdsimd [0] work will also enable us to better use SIMD in ~ a year from now (I hope)

[0] https://github.com/rust-lang/stdsimd

I am confused about Parquet wrt to the rest of your stack. Is it just that Parquet happens to be the Redshift export format? Or are you actually using Arrow and Parquet at the same time in some manner?

Parquet is the persistent storage format for when the data is written to disk or object storage. Arrow is the in-memory format and a set of tools built around working with it.

So InfluxDB IOx will use both, Arrow in-memory for fast access, and Parquet on storage for persistence.

Ok, I didn't realize that Arrow was in-memory and not also on-disk. As serializing between the two didn't make sense to me. I would have thought that Arrow would also be an on-disk format (mmap'd) so that there would be little to no conversion losses.

Why convert to an on-disk format (Parquet) and not save the in-memory representation to storage directly?

Parquet is awesome, but it's sad their Rust parquet crate only works on nightly rustc, which is a no-no in many cases. Not sure why they chose to go that way (well, because of trait specialization, technically speaking - which is not landing and not going to land anytime soon).

There are contributors and committers in the Arrow project working to resolve this. We recently removed specialization from the core Arrow crate and we plan on doing the same for the Parquet crate.

Yes, thanks a million for doing that! I've read the 'despecialization' of arrow-rust PR which actually seemed to end up being quite simple (but it's a breaking change, so a 3.0 candidate?)

Hope that something similar can be done in parquet so that arrow-parquet-df can all compile on stable in 3.0, that would unlock it for many potential users.

Thanks for sharing graphique, that is very neat.

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