Datasets:
Dataset Viewer
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
actual_rows: int64
columns: int64
dataset: string
generated_at: string
output: string
output_bytes: int64
output_sha256: string
row_groups: int64
source: string
source_archives: list<item: struct<archive: string, archive_bytes: int64, archive_sha256: string, month: int64, rows_ (... 39 chars omitted)
child 0, item: struct<archive: string, archive_bytes: int64, archive_sha256: string, month: int64, rows_used: int64 (... 27 chars omitted)
child 0, archive: string
child 1, archive_bytes: int64
child 2, archive_sha256: string
child 3, month: int64
child 4, rows_used: int64
child 5, url: string
child 6, year: int64
target_rows: int64
recorded_at: timestamp[s]
benchmark: string
environment: struct<rustc: string, cargo: string, target: string, os: string, logical_cpus: int64>
child 0, rustc: string
child 1, cargo: string
child 2, target: string
child 3, os: string
child 4, logical_cpus: int64
options: struct<feature: string, profile: string, parquet_batch_rows: int64, acta_row_block_target: int64, en (... 95 chars omitted)
child 0, feature: string
child 1, profile: string
child 2, parquet_batch_rows: int64
child 3, acta_row_block_target: int64
child 4, encoding: string
child 5, codec: string
child 6, zstd_level: int64
child 7, primary_column: string
child 8, primary_type: string
timing: struct<elapsed_seconds: double, input_mib_per_second: double, rows_per_second: int64, acta_over_parq (... 65 chars omitted)
child 0, elapsed_seconds: double
child 1, input_mib_per_second: double
child 2, rows_per_second: int64
child 3, acta_over_parquet_ratio: double
child 4, size_reduction_percent: double
child 5, scope: string
to
{'benchmark': Value('string'), 'recorded_at': Value('timestamp[s]'), 'dataset': {'source': Value('string'), 'manifest': Value('string'), 'rows': Value('int64'), 'columns': Value('int64'), 'parquet_row_groups': Value('int64'), 'parquet_bytes': Value('int64'), 'parquet_sha256': Value('string')}, 'output': {'format': Value('string'), 'acta_bytes': Value('int64'), 'acta_sha256': Value('string'), 'acta_blocks': Value('int64'), 'frames': Value('int64'), 'incomplete_tail': Value('bool')}, 'options': {'feature': Value('string'), 'profile': Value('string'), 'parquet_batch_rows': Value('int64'), 'acta_row_block_target': Value('int64'), 'encoding': Value('string'), 'codec': Value('string'), 'zstd_level': Value('int64'), 'primary_column': Value('string'), 'primary_type': Value('string')}, 'timing': {'elapsed_seconds': Value('float64'), 'input_mib_per_second': Value('float64'), 'rows_per_second': Value('int64'), 'acta_over_parquet_ratio': Value('float64'), 'size_reduction_percent': Value('float64'), 'scope': Value('string')}, 'environment': {'rustc': Value('string'), 'cargo': Value('string'), 'target': Value('string'), 'os': Value('string'), 'logical_cpus': Value('int64')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
actual_rows: int64
columns: int64
dataset: string
generated_at: string
output: string
output_bytes: int64
output_sha256: string
row_groups: int64
source: string
source_archives: list<item: struct<archive: string, archive_bytes: int64, archive_sha256: string, month: int64, rows_ (... 39 chars omitted)
child 0, item: struct<archive: string, archive_bytes: int64, archive_sha256: string, month: int64, rows_used: int64 (... 27 chars omitted)
child 0, archive: string
child 1, archive_bytes: int64
child 2, archive_sha256: string
child 3, month: int64
child 4, rows_used: int64
child 5, url: string
child 6, year: int64
target_rows: int64
recorded_at: timestamp[s]
benchmark: string
environment: struct<rustc: string, cargo: string, target: string, os: string, logical_cpus: int64>
child 0, rustc: string
child 1, cargo: string
child 2, target: string
child 3, os: string
child 4, logical_cpus: int64
options: struct<feature: string, profile: string, parquet_batch_rows: int64, acta_row_block_target: int64, en (... 95 chars omitted)
child 0, feature: string
child 1, profile: string
child 2, parquet_batch_rows: int64
child 3, acta_row_block_target: int64
child 4, encoding: string
child 5, codec: string
child 6, zstd_level: int64
child 7, primary_column: string
child 8, primary_type: string
timing: struct<elapsed_seconds: double, input_mib_per_second: double, rows_per_second: int64, acta_over_parq (... 65 chars omitted)
child 0, elapsed_seconds: double
child 1, input_mib_per_second: double
child 2, rows_per_second: int64
child 3, acta_over_parquet_ratio: double
child 4, size_reduction_percent: double
child 5, scope: string
to
{'benchmark': Value('string'), 'recorded_at': Value('timestamp[s]'), 'dataset': {'source': Value('string'), 'manifest': Value('string'), 'rows': Value('int64'), 'columns': Value('int64'), 'parquet_row_groups': Value('int64'), 'parquet_bytes': Value('int64'), 'parquet_sha256': Value('string')}, 'output': {'format': Value('string'), 'acta_bytes': Value('int64'), 'acta_sha256': Value('string'), 'acta_blocks': Value('int64'), 'frames': Value('int64'), 'incomplete_tail': Value('bool')}, 'options': {'feature': Value('string'), 'profile': Value('string'), 'parquet_batch_rows': Value('int64'), 'acta_row_block_target': Value('int64'), 'encoding': Value('string'), 'codec': Value('string'), 'zstd_level': Value('int64'), 'primary_column': Value('string'), 'primary_type': Value('string')}, 'timing': {'elapsed_seconds': Value('float64'), 'input_mib_per_second': Value('float64'), 'rows_per_second': Value('int64'), 'acta_over_parquet_ratio': Value('float64'), 'size_reduction_percent': Value('float64'), 'scope': Value('string')}, 'environment': {'rustc': Value('string'), 'cargo': Value('string'), 'target': Value('string'), 'os': Value('string'), 'logical_cpus': Value('int64')}}
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.
Acta benchmark data
Canonical large artifacts for reproducing the Acta v0.2 BTS flight benchmark. The code, downloader, conversion command, schema mapping, and validation procedure live in the Acta GitHub repository.
Contents
bts_flight/v0.2/ contains:
bts_flights_15750000.parquet: 15,750,000 rows, 51 columns, 381,644,436 bytes;bts_flights_15750000.acta: recorded Acta v0.2 output using Zstandard level 6, 325,340,952 bytes;manifest.json: source archive URLs, source checksums, row counts, and the Parquet checksum;benchmark.json: the recorded Acta checksum, compression level, and benchmark result.
Checksums
33c7d1fd28faa95d002e05ebf778e6345c4eb1c9d39416224ee08967bad23f27 bts_flights_15750000.parquet
ee5c07e11197665895065d3b998f30d2a3984bd6920e9121d04db67e55821d29 bts_flights_15750000.acta
Provenance and use
The Parquet input is a curated, strongly typed subset of the U.S. Bureau of
Transportation Statistics Reporting Carrier On-Time Performance data. The
original monthly archives are available from the BTS PREZIP archive.
The repository's downloader reconstructs the input from those archives and
records their checksums in manifest.json.
This dataset is provided for software benchmarking and format validation. It is not an official BTS publication. Users should review the source agency's terms and attribution requirements before redistributing or using the data.
- Downloads last month
- -