Better way to add options or implementations to the json encoders

[paddymul]

I want to be able to add new json implementations to fast_parquet.json. I currently am monkeypatching the implementation to add extra options to the JSON implementation, and use the pandas ujson_dumps

from fastparquet import json as fp_json
from io import BytesIO
import logging
logger = logging.getLogger()


class MyJsonImpl(fp_json.BaseImpl):
    def __init__(self):
        from pandas._libs.json import ujson_dumps

        #for some reason the following line causes errors, so I have to reimport ujson_dumps
        # from pandas._libs.json import ujson_dumps
        # self.dumps = ujson_dumps

    def dumps(self, data):
        from pandas._libs.json import ujson_dumps
        return ujson_dumps(data, default_handler=str).encode("utf-8")

    def loads(self, s):
        return self.api.loads(s)


def to_parquet(df):
    data: BytesIO = BytesIO()

    # data.close doesn't work in pyodide, so we make close a no-op
    orig_close = data.close
    data.close = lambda: None
    # I don't like this copy.  modify to keep the same data with different names
    df2 = df.copy()
    df2['index'] = df2.index
    df2.columns = [str(x) for x in df2.columns]
    obj_columns = df2.select_dtypes([pd.CategoricalDtype(), 'object']).columns.to_list()
    encodings = {k:'json' for k in obj_columns}

    orig_get_cached_codec = fp_json._get_cached_codec
    def fake_get_cached_codec():
        return MyJsonImpl()

    fp_json._get_cached_codec = fake_get_cached_codec
    try:
        df2.to_parquet(data, engine='fastparquet', object_encoding=encodings)
    except Exception as e:
        logger.error("error serializing to parquet %r", e)
        raise
    finally:
        data.close = orig_close
        fp_json._get_cached_codec = orig_get_cached_codec


    data.seek(0)
    return data.read()

This would be cleaner if I could call something like fastparquet.json.register_impl(MyJsonImpl)

Here is my testcase for my to_parquet function

def test_serialize_naive_json():
    d = date(year=1999, month=10, day=3)
    d2 = date(year=1999, month=10, day=3)
    df = pd.DataFrame({'a': [pd.DataFrame, Exception, lambda x: x+10],
                       'b': [d, d2, None]})

    #just make sure we don't throw an error
    output = to_parquet(df)
    #and make sure output isn't empty. I don't want to hardcode a
    #response here
    assert len(output) > 20

---

For my usecase, I really need parquet encoding to always work and produce something. I use fastparquet for my Buckaroo table UI and I can get any type of DataFrame from the wild passed into it. Fastparquet produces a parquet buffer that is sent to the browser to read. A column that is just strings or empty is greatly preferable to an exception or a broken parquet file.

[martindurant]

Sure, I don't mind providing the explicit ability to swap out the JSON de/ser . Having a top-level function like that would set it globally for all calls into fastparquet - I think this is OK and preferable to yet another kwargs in the read/write functions.

[paddymul]

Thank you.

I think for the customization I'm looking for, it is more straightforward to swap out the whole implementation rather than figure out how kwargs migrate through pandas, to fastparquet, to json encoding.