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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
planner_id: string
id: string
synthetic: bool
inputs: struct<workload: int64, throughput: int64, streams: int64, buffer_percent: int64>
child 0, workload: int64
child 1, throughput: int64
child 2, streams: int64
child 3, buffer_percent: int64
expected: struct<adjusted_workload: double, capacity_per_day: int64, days: int64, band: string>
child 0, adjusted_workload: double
child 1, capacity_per_day: int64
child 2, days: int64
child 3, band: string
reproduced: struct<adjusted_workload: double, capacity_per_day: int64, days: int64, band: string>
child 0, adjusted_workload: double
child 1, capacity_per_day: int64
child 2, days: int64
child 3, band: string
publisher: struct<name: string, website: string>
child 0, name: string
child 1, website: string
license: string
schema_version: string
planners: list<item: struct<id: string, title: struct<en: string, ru: string>, unit: struct<en: string, ru: st (... 458 chars omitted)
child 0, item: struct<id: string, title: struct<en: string, ru: string>, unit: struct<en: string, ru: string>, scop (... 446 chars omitted)
child 0, id: string
child 1, title: struct<en: string, ru: string>
child 0, en: string
child 1, ru: string
child 2, unit: struct<en: string, ru: string>
child 0, en: string
child 1, ru: string
child 3, scope: struct<en: string, ru: string>
child 0, en: string
child 1, ru: string
child 4, publisher: struct<name: string, website: string>
child 0, name: string
child 1, website: string
child 5, defaults: struct<workload: int64, throughput: int64, streams: int64, buffer_percent: int64>
child 0, workload: int64
child 1, throughput: int64
child 2, streams: int64
child 3, buffer_percent: int64
child 6, domain_index: int64
child 7, scenarios: list<item: struct<id: string, synthetic: bool, inputs: struct<workload: int64, throughput: int64, st (... 135 chars omitted)
child 0, item: struct<id: string, synthetic: bool, inputs: struct<workload: int64, throughput: int64, streams: int6 (... 123 chars omitted)
child 0, id: string
child 1, synthetic: bool
child 2, inputs: struct<workload: int64, throughput: int64, streams: int64, buffer_percent: int64>
child 0, workload: int64
child 1, throughput: int64
child 2, streams: int64
child 3, buffer_percent: int64
child 3, expected: struct<adjusted_workload: double, capacity_per_day: int64, days: int64, band: string>
child 0, adjusted_workload: double
child 1, capacity_per_day: int64
child 2, days: int64
child 3, band: string
to
{'schema_version': Value('string'), 'publisher': {'name': Value('string'), 'website': Value('string')}, 'license': Value('string'), 'planners': List({'id': Value('string'), 'title': {'en': Value('string'), 'ru': Value('string')}, 'unit': {'en': Value('string'), 'ru': Value('string')}, 'scope': {'en': Value('string'), 'ru': Value('string')}, 'publisher': {'name': Value('string'), 'website': Value('string')}, 'defaults': {'workload': Value('int64'), 'throughput': Value('int64'), 'streams': Value('int64'), 'buffer_percent': Value('int64')}, 'domain_index': Value('int64'), 'scenarios': List({'id': Value('string'), 'synthetic': Value('bool'), 'inputs': {'workload': Value('int64'), 'throughput': Value('int64'), 'streams': Value('int64'), 'buffer_percent': Value('int64')}, 'expected': {'adjusted_workload': Value('float64'), 'capacity_per_day': Value('int64'), 'days': Value('int64'), 'band': 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
planner_id: string
id: string
synthetic: bool
inputs: struct<workload: int64, throughput: int64, streams: int64, buffer_percent: int64>
child 0, workload: int64
child 1, throughput: int64
child 2, streams: int64
child 3, buffer_percent: int64
expected: struct<adjusted_workload: double, capacity_per_day: int64, days: int64, band: string>
child 0, adjusted_workload: double
child 1, capacity_per_day: int64
child 2, days: int64
child 3, band: string
reproduced: struct<adjusted_workload: double, capacity_per_day: int64, days: int64, band: string>
child 0, adjusted_workload: double
child 1, capacity_per_day: int64
child 2, days: int64
child 3, band: string
publisher: struct<name: string, website: string>
child 0, name: string
child 1, website: string
license: string
schema_version: string
planners: list<item: struct<id: string, title: struct<en: string, ru: string>, unit: struct<en: string, ru: st (... 458 chars omitted)
child 0, item: struct<id: string, title: struct<en: string, ru: string>, unit: struct<en: string, ru: string>, scop (... 446 chars omitted)
child 0, id: string
child 1, title: struct<en: string, ru: string>
child 0, en: string
child 1, ru: string
child 2, unit: struct<en: string, ru: string>
child 0, en: string
child 1, ru: string
child 3, scope: struct<en: string, ru: string>
child 0, en: string
child 1, ru: string
child 4, publisher: struct<name: string, website: string>
child 0, name: string
child 1, website: string
child 5, defaults: struct<workload: int64, throughput: int64, streams: int64, buffer_percent: int64>
child 0, workload: int64
child 1, throughput: int64
child 2, streams: int64
child 3, buffer_percent: int64
child 6, domain_index: int64
child 7, scenarios: list<item: struct<id: string, synthetic: bool, inputs: struct<workload: int64, throughput: int64, st (... 135 chars omitted)
child 0, item: struct<id: string, synthetic: bool, inputs: struct<workload: int64, throughput: int64, streams: int6 (... 123 chars omitted)
child 0, id: string
child 1, synthetic: bool
child 2, inputs: struct<workload: int64, throughput: int64, streams: int64, buffer_percent: int64>
child 0, workload: int64
child 1, throughput: int64
child 2, streams: int64
child 3, buffer_percent: int64
child 3, expected: struct<adjusted_workload: double, capacity_per_day: int64, days: int64, band: string>
child 0, adjusted_workload: double
child 1, capacity_per_day: int64
child 2, days: int64
child 3, band: string
to
{'schema_version': Value('string'), 'publisher': {'name': Value('string'), 'website': Value('string')}, 'license': Value('string'), 'planners': List({'id': Value('string'), 'title': {'en': Value('string'), 'ru': Value('string')}, 'unit': {'en': Value('string'), 'ru': Value('string')}, 'scope': {'en': Value('string'), 'ru': Value('string')}, 'publisher': {'name': Value('string'), 'website': Value('string')}, 'defaults': {'workload': Value('int64'), 'throughput': Value('int64'), 'streams': Value('int64'), 'buffer_percent': Value('int64')}, 'domain_index': Value('int64'), 'scenarios': List({'id': Value('string'), 'synthetic': Value('bool'), 'inputs': {'workload': Value('int64'), 'throughput': Value('int64'), 'streams': Value('int64'), 'buffer_percent': Value('int64')}, 'expected': {'adjusted_workload': Value('float64'), 'capacity_per_day': Value('int64'), 'days': Value('int64'), 'band': 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.
SHAR Production Capacity Planners
This dataset by SHAR Production contains 100 deterministic synthetic workload scenarios for 25 bilingual production capacity planners. Each planner calculates adjusted workload, daily capacity, estimated days and a transparent planning band from explicit inputs.
scenarios.jsonl contains inputs, expected outputs and reproduced results. catalog.json contains bilingual planner definitions and units.
Synthetic scenarios and results use CC-BY-4.0. The source implementation and documentation use MIT. Outputs are planning estimates, not delivery guarantees. No client data, credentials, analytics or performance claims are included. AI assistance was used under SHAR Production's publishing responsibility.
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