Datasets:

---

maknee

/

msturing-30M-clustered

like 0

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
dataset_name: string
variant: string
distance: string
data_type: string
dimensions: int64
base_vectors: int64
query_vectors: int64
gt_k: int64
diskann_params: struct<R: int64, L: int64, pq_bytes: int64>
shard_count: int64
hf_root: string
placement: struct<initial: struct<N0: list<item: string>, N1: list<item: string>, N2: list<item: string>, N3: list<item: string>, N4: list<item: string>>, post_compaction_no_rebalance: struct<N0: list<item: string>, N1: list<item: string>, N2: list<item: string>, N3: list<item: string>, N4: list<item: string>>>
notes: list<item: string>
generated_at_unix: int64
vs
variant_dir: string
dataset_name: string
shard_count: int64
search_smoke: list<item: struct<command: list<item: string>, returncode: int64, elapsed_sec: double, stdout_tail: string, stderr_tail: string, ok: bool, shard_idx: int64>>
transform_sparse_checks: list<item: struct<shard_idx: int64, indices: struct<path: string, exists: bool, apparent_bytes: int64, actual_bytes: int64, actual_to_apparent_ratio: double>, vectors: struct<path: string, exists: bool, apparent_bytes: int64, actual_bytes: int64, actual_to_apparent_ratio: double>>>
all_ok: bool
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 604, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              dataset_name: string
              variant: string
              distance: string
              data_type: string
              dimensions: int64
              base_vectors: int64
              query_vectors: int64
              gt_k: int64
              diskann_params: struct<R: int64, L: int64, pq_bytes: int64>
              shard_count: int64
              hf_root: string
              placement: struct<initial: struct<N0: list<item: string>, N1: list<item: string>, N2: list<item: string>, N3: list<item: string>, N4: list<item: string>>, post_compaction_no_rebalance: struct<N0: list<item: string>, N1: list<item: string>, N2: list<item: string>, N3: list<item: string>, N4: list<item: string>>>
              notes: list<item: string>
              generated_at_unix: int64
              vs
              variant_dir: string
              dataset_name: string
              shard_count: int64
              search_smoke: list<item: struct<command: list<item: string>, returncode: int64, elapsed_sec: double, stdout_tail: string, stderr_tail: string, ok: bool, shard_idx: int64>>
              transform_sparse_checks: list<item: struct<shard_idx: int64, indices: struct<path: string, exists: bool, apparent_bytes: int64, actual_bytes: int64, actual_to_apparent_ratio: double>, vectors: struct<path: string, exists: bool, apparent_bytes: int64, actual_bytes: int64, actual_to_apparent_ratio: double>>>
              all_ok: bool