question about Arrow: the format seems to be not very space efficient.
I tried converting one of my parquet files from datalake from parquet to arrow and size difference is staggering. 20mb parquet -> 700mb arrow.
doesnt seem fit for datalake at all
I tried converting one of my parquet files from datalake from parquet to arrow and size difference is staggering. 20mb parquet -> 700mb arrow.
doesnt seem fit for datalake at all
> Parquet and Arrow are complementary technologies, and they make some different design tradeoffs. In particular, Parquet is a storage format designed for maximum space efficiency, whereas Arrow is an in-memory format intended for operation by vectorized computational kernels.
> The major distinction is that Arrow provides O(1) random access lookups to any array index, whilst Parquet does not. In particular, Parquet uses dremel record shredding, variable length encoding schemes, and block compression to drastically reduce the data size, but these techniques come at the loss of performant random access lookups.
if I load my parquet into memory - I will have O(1) random access to any row just as well.
plus, considering that Arrow recommends to work in chunks of 1000 rows per file, I am curious to learn exact tasks for which Arrow is optimizing for.
the only use case I can think of is transferring data between systems written in different languages/runtimes and doing zero serialization/deserialization, just send/receive memory buffers that are nicely mapped to dataframes.
Arrow is absolutely designed for Interop between languages - often devs want to develop their core in something like Python, and the platform devs want to work on Scala. Arrow lets you write all the distributed system and data shuffling in Scala, but then users can access the same records in their user-defined code with minimal overhead
Deep Dive into Common Open Formats for Analytical DBMSs https://www.vldb.org/pvldb/vol16/p3044-liu.pdf