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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
dataset_id: string
study_accession: string
title: string
description: string
producer: null
publication: null
enrichment: null
pipeline_version: string
published_with: string
created_at: timestamp[s]
schema_version: string
counts: struct<studies: int64, samples: int64, runs: int64, features: int64, reads: int64>
child 0, studies: int64
child 1, samples: int64
child 2, runs: int64
child 3, features: int64
child 4, reads: int64
processing: struct<pipeline_version: string, tool_versions: struct<ampliconflow: string, python: string, dnaio: (... 9963 chars omitted)
child 0, pipeline_version: string
child 1, tool_versions: struct<ampliconflow: string, python: string, dnaio: string>
child 0, ampliconflow: string
child 1, python: string
child 2, dnaio: string
child 2, engine: string
child 3, engine_version: string
child 4, mode: string
child 5, identity: double
child 6, min_feature_size: int64
child 7, reference: string
child 8, reference_sha256: string
child 9, comparability_reference: string
child 10, comparability_reference_sha256: string
child 11, sintax_cutoff: double
child 12, chimera_rate: null
child 13, chimera_method: string
child 14, sintax_randseed: int64
child 15, filters: struct<DRR751718: struct<trunc_len: int64, max_ee: double, trunc_q: int64, min_len: int64, reason: s (... 6446 chars omitted)
child 0, DRR751718: struct<trunc_len: int64, max_ee: double, trunc_q: int64, min_len: int64, reason: string, ev
...
, by: string, was: string, now: string, reason: s (... 7 chars omitted)
child 0, item: struct<field: string, date: timestamp[s], by: string, was: string, now: string, reason: string>
child 0, field: string
child 1, date: timestamp[s]
child 2, by: string
child 3, was: string
child 4, now: string
child 5, reason: string
rank_abundance_features: int64
features_nonzero: int64
rank_abundance: list<item: struct<feature_id: string, reads: int64, genus: string>>
child 0, item: struct<feature_id: string, reads: int64, genus: string>
child 0, feature_id: string
child 1, reads: int64
child 2, genus: string
sample_reads: list<item: struct<sample_accession: string, reads: int64>>
child 0, item: struct<sample_accession: string, reads: int64>
child 0, sample_accession: string
child 1, reads: int64
top_phylum: list<item: struct<name: string, reads: int64>>
child 0, item: struct<name: string, reads: int64>
child 0, name: string
child 1, reads: int64
reads_per_sample: struct<min: int64, median: int64, max: int64>
child 0, min: int64
child 1, median: int64
child 2, max: int64
downloads: list<item: struct<label: string, path: string>>
child 0, item: struct<label: string, path: string>
child 0, label: string
child 1, path: string
top_genus: list<item: struct<name: string, reads: int64>>
child 0, item: struct<name: string, reads: int64>
child 0, name: string
child 1, reads: int64
to
{'dataset_id': Value('string'), 'title': Value('string'), 'counts': {'studies': Value('int64'), 'samples': Value('int64'), 'runs': Value('int64'), 'features': Value('int64'), 'reads': Value('int64')}, 'features_nonzero': Value('int64'), 'reads_per_sample': {'min': Value('int64'), 'median': Value('int64'), 'max': Value('int64')}, 'sample_reads': List({'sample_accession': Value('string'), 'reads': Value('int64')}), 'rank_abundance': List({'feature_id': Value('string'), 'reads': Value('int64'), 'genus': Value('string')}), 'rank_abundance_features': Value('int64'), 'top_phylum': List({'name': Value('string'), 'reads': Value('int64')}), 'top_genus': List({'name': Value('string'), 'reads': Value('int64')}), 'downloads': List({'label': Value('string'), 'path': Value('string')}), 'producer': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
dataset_id: string
study_accession: string
title: string
description: string
producer: null
publication: null
enrichment: null
pipeline_version: string
published_with: string
created_at: timestamp[s]
schema_version: string
counts: struct<studies: int64, samples: int64, runs: int64, features: int64, reads: int64>
child 0, studies: int64
child 1, samples: int64
child 2, runs: int64
child 3, features: int64
child 4, reads: int64
processing: struct<pipeline_version: string, tool_versions: struct<ampliconflow: string, python: string, dnaio: (... 9963 chars omitted)
child 0, pipeline_version: string
child 1, tool_versions: struct<ampliconflow: string, python: string, dnaio: string>
child 0, ampliconflow: string
child 1, python: string
child 2, dnaio: string
child 2, engine: string
child 3, engine_version: string
child 4, mode: string
child 5, identity: double
child 6, min_feature_size: int64
child 7, reference: string
child 8, reference_sha256: string
child 9, comparability_reference: string
child 10, comparability_reference_sha256: string
child 11, sintax_cutoff: double
child 12, chimera_rate: null
child 13, chimera_method: string
child 14, sintax_randseed: int64
child 15, filters: struct<DRR751718: struct<trunc_len: int64, max_ee: double, trunc_q: int64, min_len: int64, reason: s (... 6446 chars omitted)
child 0, DRR751718: struct<trunc_len: int64, max_ee: double, trunc_q: int64, min_len: int64, reason: string, ev
...
, by: string, was: string, now: string, reason: s (... 7 chars omitted)
child 0, item: struct<field: string, date: timestamp[s], by: string, was: string, now: string, reason: string>
child 0, field: string
child 1, date: timestamp[s]
child 2, by: string
child 3, was: string
child 4, now: string
child 5, reason: string
rank_abundance_features: int64
features_nonzero: int64
rank_abundance: list<item: struct<feature_id: string, reads: int64, genus: string>>
child 0, item: struct<feature_id: string, reads: int64, genus: string>
child 0, feature_id: string
child 1, reads: int64
child 2, genus: string
sample_reads: list<item: struct<sample_accession: string, reads: int64>>
child 0, item: struct<sample_accession: string, reads: int64>
child 0, sample_accession: string
child 1, reads: int64
top_phylum: list<item: struct<name: string, reads: int64>>
child 0, item: struct<name: string, reads: int64>
child 0, name: string
child 1, reads: int64
reads_per_sample: struct<min: int64, median: int64, max: int64>
child 0, min: int64
child 1, median: int64
child 2, max: int64
downloads: list<item: struct<label: string, path: string>>
child 0, item: struct<label: string, path: string>
child 0, label: string
child 1, path: string
top_genus: list<item: struct<name: string, reads: int64>>
child 0, item: struct<name: string, reads: int64>
child 0, name: string
child 1, reads: int64
to
{'dataset_id': Value('string'), 'title': Value('string'), 'counts': {'studies': Value('int64'), 'samples': Value('int64'), 'runs': Value('int64'), 'features': Value('int64'), 'reads': Value('int64')}, 'features_nonzero': Value('int64'), 'reads_per_sample': {'min': Value('int64'), 'median': Value('int64'), 'max': Value('int64')}, 'sample_reads': List({'sample_accession': Value('string'), 'reads': Value('int64')}), 'rank_abundance': List({'feature_id': Value('string'), 'reads': Value('int64'), 'genus': Value('string')}), 'rank_abundance_features': Value('int64'), 'top_phylum': List({'name': Value('string'), 'reads': Value('int64')}), 'top_genus': List({'name': Value('string'), 'reads': Value('int64')}), 'downloads': List({'label': Value('string'), 'path': Value('string')}), 'producer': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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