A comparison between fastparquet and pyarrow?

After some searching I failed to find a thorough comparison of fastparquet and pyarrow.

I found this blog post (a basic comparison of speeds).

and a github discussion that claims that files created with fastparquet do not support AWS-athena (btw is it still the case?)

when/why would I use one over the other? what are the major advantages and disadvantages ?

my specific use case is processing data with dask writing it to s3 and then reading/analyzing it with AWS-athena.

Solution 1:

I used both fastparquet and pyarrow for converting protobuf data to parquet and to query the same in S3 using Athena. Both worked, however, in my use-case, which is a lambda function, package zip file has to be lightweight, so went ahead with fastparquet. (fastparquet library was only about 1.1mb, while pyarrow library was 176mb, and Lambda package limit is 250mb).

I used the following to store a dataframe as parquet file:

from fastparquet import write

parquet_file = path.join(filename + '.parq')
write(parquet_file, df_data)

Solution 2:

However, since the question lacks concrete criteria, and I came here for a good "default choice", I want to state that pandas default engine for DataFrame objects is pyarrow (see pandas docs).