hckrnws

if I could tell myself in 2015 who had just found the feather library and was using it to power my unhinged topic modeling for power point slides work, and explained what feather would become (arrow) and the impact it would have on the date ecosystem. I would have looked at 2026 me like he was a crazy person.

Yet today I feel it was 2016 dataders who is the crazy one lol

Indeed. feather was a library to exchange data between R and pandas dataframes. People tend to bash pandas but its creator (Wes McKinney) has changed the data ecosystem for the better with the learnings coming from pandas.

I know pandas has a lot of technical warts and shortcomings, but I'm grateful for how much it empowered me early in my data/software career, and the API still feels more ergonomic to me due to the years of usage - plus GeoPandas layering on top of it.

Really, prefer DuckDB SQL these days for anything that needs to perform well, and feel like SQL is easier to grok than python code most of the time.

chdb's new DataStore API looks really neat (drop in pandas replacement) and exactly how I envisioned a faster pandas could be without sacrificing its ergonomics

Do people bash pandas? If so, it reminds me of Bjarne's quip that the two types of programming languages are the ones people complain about and the ones nobody uses.

polars people do - although I wouldn't call polars something that nobody uses.

I also use polars in new projects. I think Wes McKinney also uses it. If I remember correctly I saw him commenting on some polars memory related issues on GitHub. But a good chunk of polars' success can be attributed to Arrow which McKinney co-created. All the gripes people have with pandas, he had them too and built something powerful to overcome those.

I saw Wes speak in the early days of Pandas, in Berkeley. He solved problems that others just worked around for decades. His solutions are quirky but the work was very solid. His career advanced a lot IMHO for substantial reasons.. Wes personally marched through swamps and reached the other side.. others complain and do what they always have done.. I personally agree with the criticisms of the syntax, but Pandas is real and it was not easy to build it.

What's the difference between feather and parquet in terms of usage? I get the design philosophy, but how would you use them differently?

parquet is optimized for storage and compresses well (=> smaller files)

feather is optimized for fast reading

Given the cost of storage is getting cheaper, wouldn't most firms want to use feather for analytic performance? But everyone uses parquet.

You can, still, gain a lot of performance by doing less I/O.

What people have done in the face of cheaper storage is store more data.

Storage getting cheaper did not really reach the cloud providers and for self-hosting it has recently gotten even more expensive due to AI bs.

And now there's Lance! https://lance.org/

https://stackoverflow.com/questions/48083405/what-are-the-di...

I read that. But afaik, feather format is stable now. Hence my confusion. I use parquet at work a lot, where we store a lot of time series financial data. We like it. Creating the Parquet data is a pain since it's not append-able.

Generally Parquet files are combined in an LSM style, compacting smaller files into larger ones. Parquet isn't really meant for the "journal" of level-0 append-one-record style storage, it's meant for the levels that follow.

So feather for journaling and parquet for long term processing?

You basically can't do row by row appends to any columnar format stored in a single file. You could kludge around it by allocating arenas inside the file but that's still a huge write amplification, instead of writing a row in a single block you'd have to write a block per column.

You can do row by row appends to a Feather (Arrow IPC — the naming is confusing). It works fine. The main problem is that the per-append overhead is kind of silly — it costs over 300 bytes (IIRC) per append.

I wish there was an industry standard format, schema-compatible with Parquet, that was actually optimized for this use case.

Creating a new record batch for a single row is also a huge kludge leading to lot of write amplification. At that point, you're better off storing rows than pretending it's columnar.

I actually wrote a row storage format reusing Arrow data types (not Feather), just laying them out row-wise not columnar. Validity bits of the different columns collected into a shared per-row bitmap, fixed offsets within a record allow extracting any field in a zerocopy fashion. I store those in RocksDB, for now.

https://git.kantodb.com/kantodb/kantodb/src/branch/main/crat...

https://git.kantodb.com/kantodb/kantodb/src/branch/main/crat...

https://git.kantodb.com/kantodb/kantodb/src/branch/main/crat...

Agreed.

There is room still for an open source HTAP storage format to be designed and built. :-)

Have you considered something like iceberg tables?

Yes, but parquet hates small files.

Its nice to see useful, impactful interchange formats getting the attention and resources they need, and ecosystems converging around them. Optimizing serialization/deserialization might seem like a "trivial" task at first, but when moving petabytes of data they quickly become the bottlenecks. With common interchange formats, the benefits of these optimizations are shared across stacks. Love to see it.

Intuitively appreciating that these "boring fundamentals" are the default bottlenecks is a aign of senior+ swe capability.

I like arrow for its type system. It's efficient, complete and does not have "infinite precision decimals". Considering Postgres's decimal encoding, using i256 as the backing type is so much saner approach.

We use Apache Arrow at my company and it's fantastic. The performance is so good. We have terabytes of time-series financial data and use arrow to store it and process it.

We use Apache Arrow at my company too. It is part of a migration from an old in-house format. When it works it’s good. But there are just way too many bugs in Arrow. For example: a basic arrow computation on strings segfaults because the result does not fit in Arrow’s string type, only the large string type. Instead of casting it or asking the user to cast it, it just segfaults. Another example: a different basic operation causes an exception complaining about negative buffer sizes when using variable-length binary type.

This will obviously depend on which implementation you use. Using the rust arrow-rs crate you at least get panics when you overflow max buffer sizes. But one of my enduring annoyances with arrow is that they use signed integer types for buffer offsets and the like. I understand why it has to be that way since it's intended to be cross-language and not all languages have unsigned integer types. But it does lead to lots of very weird bugs when you are working in a native language and casting back and forth from signed to unsigned types. I spent a very frustrating day tracking down this one in particular https://github.com/apache/datafusion/issues/15967

stumbled upon it recently while optimizing parquet writes. It worked flawlessly and 10-20x'd my throughput

I had to look up what Arrow actually does, and I might have to run some performance comparisons vs sqlite.

It's very neat for some types of data to have columns contiguous in memory.

>> some performance comparisons vs sqlite.

That's not really the purpose; it's really a language-independent format so that you don't need to change it for say, a dataframe or R. It's columnar because for analytics (where you do lots of aggregations and filtering) this is way more performant; the data is intentionally stored so the target columns are continuous. You probably already know, but the analytics equivalent of SQLite is DuckDB. Arrow can also eliminate the need to serialize/de-serialize data when sharing (ex: a high performance data pipeline) because different consumers / tools / operations can use the same memory representation as-is.

> Arrow can also eliminate the need to serialize/de-serialize data when sharing (ex: a high performance data pipeline) because different consumers / tools / operations can use the same memory representation as-is.

Not sure if I misunderstood, what are the chances those different consumers / tools / operations are running in your memory space?

Not an expert, so I could be wrong, but my understanding is that you could just copy those bytes directly from the wire to your memory and treat those as the Arrow payload you're expecting it to be.

You still have to transfer the data, but you remove the need for a transformation before writing to the wire, and a transformation when reading from the wire.

If you are in control of two processes on a single machine instance, you could share the memory between a writer and a read-only consumer.

The key phrase though would seem to be “memory representation”m and not “same memory”. You can spit the in-memory representation out to an Arrow file or an Arrow stream, take it in, and it’s in the same memory layout in the other program. That’s kind of the point of Arrow. It’s a standard memory layout available across applications and even across languages, which can be really convenient.

Arrow supports zero-copy data sharing - checkout the Arrow IPC format and Arrow Flight.

Thanks! This is all probably me using the familiar sqlite hammer where I really shouldn't.

If I recall, Arrow is more or less a standardized representation in memory of columnar data. It tends to not be used directly I believe, but as the foundation for higher level libraries (like Polars, etc.). That said, I'm not an expert here so might not have full info.

You can absolutely use it directly, but it is painful. The USP of Arrow ist that you can pass bits of memory between Polars, Datafusion, DuckDB, etc. without copying. It's Parquet but for memory.

This is true, and as a result IME the problem space is much smaller than Parquet, but it can be really powerful. The reality is most of us don't work in environments where Arrow is needed.

Take a look at parquet.

You can also store arrow on disk but it is mainly used as in-memory representation.

yeah not necessarily compute (though it has a kernel)!

it's actually many things IPC protocol wire protocol, database connectivity spec etc etc.

in reality it's about an in-memory tabular (columnar) representation that enables zero copy operations b/w languages and engines.

and, imho, it all really comes down to standard data types for columns!

I read that entire page and I could not tell you what Apache Arrow is, or what it does.

The post celebrates Apache Arrow's 10 years anniversary, so it's assuming you already know what it is and what it does, which I think is fair. If you don't you can always refer to the docs.

All you had to do was click the logo to go to the homepage