OSError: Invalid flatbuffers message.
MEMBER
Thanks for reporting, it looks like an issue with `pyarrow.ipc.open_stream`

Can you try installing `datasets` from this pull request and see if it helps ? https://github.com/huggingface/datasets/pull/7348
2025-01-02T14:30:34Z
https://github.com/huggingface/datasets/issues/7346#issuecomment-2567862186
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> Thanks for reporting, it looks like an issue with `pyarrow.ipc.open_stream`
> 
> Can you try installing `datasets` from this pull request and see if it helps ? #7348

Thank you very much. Here, it also needed to be changed to `except (OSError, pa.lib.ArrowInvalid):`. And then the bug was fixed.
https://github.com/huggingface/datasets/blob/2826a040a05e19fca894253b78a932d4fcb4a584/src/datasets/packaged_modules/arrow/arrow.py#L48
2025-01-09T13:03:44Z
https://github.com/huggingface/datasets/issues/7346#issuecomment-2580104756
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Cool ! we will do a new release soon :) in the meantime you can use `datasets` from `main`
2025-01-09T14:25:28Z
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### Describe the bug

When loading a large 2D data (1000 × 1152) with a large number of (2,000 data in this case) in `load_dataset`, the error message `OSError: Invalid flatbuffers message` is reported. 

When only 300 pieces of data of this size (1000 × 1152) are stored, they can be loaded correctly. 

When 2,000 2D arrays are stored in each file, about 100 files are generated, each with a file size of about 5-6GB. But when 300 2D arrays are stored in each file, **about 600 files are generated, which is too many files**.

### Steps to reproduce the bug

error：
```python
---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
Cell In[2], line 4
      1 from datasets import Dataset
      2 from datasets import load_dataset
----> 4 real_dataset = load_dataset("arrow", data_files='tensorData/real_ResidueTensor/*', split="train")#.with_format("torch") # , split="train"
      5 # sim_dataset = load_dataset("arrow", data_files='tensorData/sim_ResidueTensor/*', split="train").with_format("torch")
      6 real_dataset

File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/load.py:2151](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/load.py#line=2150), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)
   2148     return builder_instance.as_streaming_dataset(split=split)
   2150 # Download and prepare data
-> 2151 builder_instance.download_and_prepare(
   2152     download_config=download_config,
   2153     download_mode=download_mode,
   2154     verification_mode=verification_mode,
   2155     num_proc=num_proc,
   2156     storage_options=storage_options,
   2157 )
   2159 # Build dataset for splits
   2160 keep_in_memory = (
   2161     keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
   2162 )

File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py:924](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py#line=923), in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, dl_manager, base_path, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
    922 if num_proc is not None:
    923     prepare_split_kwargs["num_proc"] = num_proc
--> 924 self._download_and_prepare(
    925     dl_manager=dl_manager,
    926     verification_mode=verification_mode,
    927     **prepare_split_kwargs,
    928     **download_and_prepare_kwargs,
    929 )
    930 # Sync info
    931 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())

File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py:978](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/builder.py#line=977), in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
    976 split_dict = SplitDict(dataset_name=self.dataset_name)
    977 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 978 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
    980 # Checksums verification
    981 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:

File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/packaged_modules/arrow/arrow.py:47](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/datasets/packaged_modules/arrow/arrow.py#line=46), in Arrow._split_generators(self, dl_manager)
     45 with open(file, "rb") as f:
     46     try:
---> 47         reader = pa.ipc.open_stream(f)
     48     except pa.lib.ArrowInvalid:
     49         reader = pa.ipc.open_file(f)

File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py:190](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py#line=189), in open_stream(source, options, memory_pool)
    171 def open_stream(source, *, options=None, memory_pool=None):
    172     """
    173     Create reader for Arrow streaming format.
    174 
   (...)
    188         A reader for the given source
    189     """
--> 190     return RecordBatchStreamReader(source, options=options,
    191                                    memory_pool=memory_pool)

File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py:52](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.py#line=51), in RecordBatchStreamReader.__init__(self, source, options, memory_pool)
     50 def __init__(self, source, *, options=None, memory_pool=None):
     51     options = _ensure_default_ipc_read_options(options)
---> 52     self._open(source, options=options, memory_pool=memory_pool)

File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.pxi:1006](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/ipc.pxi#line=1005), in pyarrow.lib._RecordBatchStreamReader._open()

File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi:155](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi#line=154), in pyarrow.lib.pyarrow_internal_check_status()

File [~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi:92](http://localhost:8899/lab/tree/RTC%3Anew_world/esm3/~/miniforge3/envs/esmIne3/lib/python3.12/site-packages/pyarrow/error.pxi#line=91), in pyarrow.lib.check_status()

OSError: Invalid flatbuffers message.
```

reproduce：Here is just an example result, the real 2D matrix is the output of the ESM large model, and the matrix size is approximate
```python
import numpy as np
import pyarrow as pa

random_arrays_list = [np.random.rand(1000, 1152) for _ in range(2000)]
table = pa.Table.from_pydict({
    'tensor': [tensor.tolist() for tensor in random_arrays_list]
})

import pyarrow.feather as feather
feather.write_feather(table, 'test.arrow')

from datasets import load_dataset
dataset = load_dataset("arrow", data_files='test.arrow', split="train")
```

### Expected behavior

`load_dataset` load the dataset as normal as `feather.read_feather`
```python
import pyarrow.feather as feather
feather.read_feather('tensorData/real_ResidueTensor/real_tensor_1.arrow')
```

Plus `load_dataset("parquet", data_files='test.arrow', split="train")` works fine

### Environment info

- `datasets` version: 3.2.0
- Platform: Linux-6.8.0-49-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.26.5
- PyArrow version: 18.1.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
