Apache Arrow is 10 years old (arrow.apache.org)
163 points by tosh 9 hours ago
data_ders 7 hours ago
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
ayhanfuat 7 hours ago
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.
jtbaker 5 hours ago
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.
0xcafefood 7 hours ago
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.
postexitus 7 hours ago
HoldOnAMinute an hour ago
I read that entire page and I could not tell you what Apache Arrow is, or what it does.
depr an hour ago
All you had to do was click the logo to go to the homepage
aynyc 4 hours ago
What's the difference between feather and parquet in terms of usage? I get the design philosophy, but how would you use them differently?
tosh 4 hours ago
parquet is optimized for storage and compresses well (=> smaller files)
feather is optimized for fast reading
aynyc 3 hours ago
Given the cost of storage is getting cheaper, wouldn't most firms want to use feather for analytic performance? But everyone uses parquet.
yencabulator 2 hours ago
outside1234 3 hours ago
twic an hour ago
And now there's Lance! https://lance.org/
dionian 4 hours ago
aynyc 4 hours ago
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.
yencabulator 2 hours ago
dionian 43 minutes ago
pm90 5 hours ago
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.
sudoshred 7 minutes ago
Intuitively appreciating that these "boring fundamentals" are the default bottlenecks is a aign of senior+ swe capability.
aerzen 4 hours ago
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.
mempko 5 hours ago
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.
kccqzy 4 hours ago
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.
thinkharderdev 3 hours ago
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
dionian 4 hours ago
stumbled upon it recently while optimizing parquet writes. It worked flawlessly and 10-20x'd my throughput
actionfromafar 7 hours ago
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.
skeeter2020 7 hours ago
>> 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.
mandeepj 5 hours ago
> 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?
daddykotex 5 hours ago
cestith 3 hours ago
shadow28 2 hours ago
actionfromafar 6 hours ago
Thanks! This is all probably me using the familiar sqlite hammer where I really shouldn't.
nu11ptr 7 hours ago
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.
tormeh 7 hours ago
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.
skeeter2020 7 hours ago
tosh 6 hours ago
Take a look at parquet.
You can also store arrow on disk but it is mainly used as in-memory representation.
data_ders 7 hours ago
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!