[BUG] read_parquet ignores prefetch_options={"method": "parquet"} for remote files

[Arthur031221]

Describe the bug
On a remote fsspec filesystem, read_parquet(..., prefetch_options={"method": "parquet"}) downloads the whole file instead of only the selected row groups and columns, and it rewrites the dict the caller passed in.

The cause is this line in python/cudf/cudf/io/parquet.py:

prefetch_options = prefetch_options.update({...})

dict.update returns None, so get_reader_filepath_or_buffer gets prefetch_options=None and _prefetch_remote_buffers falls back to method="all". The line came in with #16657, so the parquet prefetcher has never run from read_parquet.

Steps/Code to reproduce bug

import fsspec
import numpy as np
import pandas as pd
import cudf
from fsspec.implementations.memory import MemoryFileSystem

fs = MemoryFileSystem()
path = "memory://bucket/data.parquet"
pdf = pd.DataFrame({"a": np.arange(400_000), "b": np.arange(400_000) * 1.5})
with fs.open(path, "wb") as f:
    pdf.to_parquet(f, row_group_size=100_000, index=False)
print("file size:", fs.size(path))

fetched = []
cat_ranges = MemoryFileSystem.cat_ranges
def counting_cat_ranges(self, *args, **kwargs):
    out = cat_ranges(self, *args, **kwargs)
    fetched.extend(len(b) for b in out)
    return out
MemoryFileSystem.cat_ranges = counting_cat_ranges

opts = {"method": "parquet"}
df = cudf.read_parquet(path, row_groups=[1], prefetch_options=opts)
print("rows:", len(df), "bytes fetched:", sum(fetched))
print("prefetch_options after the call:", opts)

Output:

file size: 4857088
rows: 100000 bytes fetched: 4857088
prefetch_options after the call: {'method': 'parquet', 'columns': None, 'row_groups': [1]}

Expected behavior
The parquet prefetcher is used, so the whole file is not downloaded for one row group, and opts is left as {'method': 'parquet'}.

Environment overview (please complete the following information)

• Environment location: Bare-metal, Linux, RTX 5090 (driver 610.43.02)
• Method of cuDF install: pip, nightly cudf-cu13 26.12.00a177.post260927053240 (commit 1c84b4e)

Environment details

• NVIDIA GeForce RTX 5090, driver 610.43.02, CUDA 13 (cu13 wheels)
• Python 3.12.13, pandas 3.0.6, pyarrow 25.0.1, fsspec 2026.9.0
• The line is unchanged on main at d75cb7f.

Additional context
I have a fix with tests ready and can open a PR. With it, the script above fetches 3,210,801 bytes and leaves opts unchanged. Replacing the update call with a new dict is not enough on its own: once the prefetcher runs, two more cases fail with Parquet header parsing failed on the file above, which is larger than fsspec's 1 MB footer sample:

• columns=["b"], filters=[("a", ">", x)]: the filter columns are added to columns after the prefetch, so the bytes of a are never fetched. Moving that block before the prefetch fixes it.
• case_sensitive_names=False with columns=["B"]: the prefetcher matches names exactly and fetches no column data. Passing columns=None to it in that case fixes it.

One question on the default. The same block is meant to pick "parquet" by default for a uniform row_groups selection, including one derived from filters, but because of this bug those reads have always used "all". Turning that default on is not a clear win: the parquet prefetcher reads a 1 MB footer sample and then fetches the footer range again with the data. On memory://, a 242,769-byte file read with row_groups=[0] goes from 242,769 bytes in one cat_ranges call with "all" to 485,538 bytes in two calls with "parquet". dask-cudf also passes per-fragment row_groups to cudf.read_parquet. Would you prefer to keep "all" as the default and only honour an explicit method="parquet", to enable the default as #16657 intended, or to remove the parquet prefetcher since the option is marked experimental?