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Apache Arrow is 10 years old
- deleted 9mo ago[deleted]
- actionfromafar 9mo agoI 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.
- nu11ptr 9mo agoIf 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 9mo agoYou 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 9mo agoThis 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.
- data_ders 9mo agoyeah 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!
- skeeter2020 9mo 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.
- actionfromafar 9mo agoThanks! This is all probably me using the familiar sqlite hammer where I really shouldn't.
- mandeepj 9mo 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 9mo agoNot 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.
- cestith 9mo agoIf 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.
- tosh 9mo agoTake a look at parquet. You can also store arrow on disk but it is mainly used as in-memory representation.
- data_ders 9mo agoif 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 9mo agoIndeed. 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.
- 0xcafefood 9mo agoDo 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 9mo agopolars people do - although I wouldn't call polars something that nobody uses.
- ayhanfuat 9mo agoI 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.
- mistrial9 9mo agoI 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.
- pm90 9mo agoIts 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 9mo agoIntuitively appreciating that these "boring fundamentals" are the default bottlenecks is a aign of senior+ swe capability.
- mempko 9mo agoWe 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.
- dionian 9mo agostumbled upon it recently while optimizing parquet writes. It worked flawlessly and 10-20x'd my throughput
- kccqzy 9mo agoWe 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 9mo agoThis 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 https://github.com/apache/datafusion/issues/15967
- pitrou 9mo agoHey, Arrow developer here. If you get a segfault with our codebase, then please report an issue on our GitHub issue tracker. (if you have already done so and it wasn't resolved, feel free to ping me on it)
- aynyc 9mo agoWhat's the difference between feather and parquet in terms of usage? I get the design philosophy, but how would you use them differently?
- dionian 9mo agohttps://stackoverflow.com/questions/48083405/what-are-the-differences-between-feather-and-parquet/48097717#48097717 https://stackoverflow.com/questions/48083405/what-are-the-di...
- aynyc 9mo agoI 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 9mo agoGenerally 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.
- aynyc 9mo agoSo feather for journaling and parquet for long term processing?
- yencabulator 9mo agoYou 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.
- aerzen 9mo agoI 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.
- HoldOnAMinute 9mo agoI read that entire page and I could not tell you what Apache Arrow is, or what it does.
- depr 9mo agoAll you had to do was click the logo to go to the homepage
- gchamonlive 9mo agoThe 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.
- final_aeon 9mo agoI laugh everytime I have to explain that "Apache Arrow format is more efficient than JSON. Yes, the format is called 'Apache Arrow.'"
- vivzkestrel 9mo agostupid question: why hasnt apache arrow taken over to the point where we are not longer dealing with json?
- kbaker 9mo agoBecause it's a binary format?
- benrutter 9mo agoI think a big reason (aside from intertia) is that arrow is designed for tables. Json sends a lot more than just that and can support whatever octagonal junitsu squid shaped data you want to fit into it. Also, a good proportion of web apis are sending pretty small data sizes. On mass there might be an improvement if everything was more efficiently represented, but evaluating on a case by case basis, the data size often isn't the bottleneck.
- lmeyerov 9mo agoWe contributed the first JS impl and were helping with the nvidia gpu bits when it was starting. Some of our architectural decisions back then were awful as we were trying to figure out how make Graphistry work, but Arrow + GPU dataframes remain gifts that keep giving.