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Datasets:
zjt24
/
data_sample_1000

Tags:
tsfile
time-series
recommendation
taac2026
License:
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The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "tsfile/tsfile_py_cpp.pyx", line 567, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
              tsfile.exceptions.FileOpenError: 28: 
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
                  with self._open_reader(file) as reader:
                       ~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
                  return TsFileReader(file)
                File "tsfile/tsfile_reader.pyx", line 323, in tsfile.tsfile_reader.TsFileReaderPy.__init__
              SystemError: <class '_weakrefset.WeakSet'> returned a result with an exception set
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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TAAC2026 Data Sample 1000 TsFile

This dataset is a TsFile conversion of TAAC2026/data_sample_1000, the TAAC2026 demo recommendation dataset with 1,000 user-item interaction records.

Modalities: Time-series. The source dataset is a flat Parquet file where all features are top-level columns. Some top-level columns are variable-length list features, so the conversion stores scalar interaction features and sequence features in separate TsFile tables instead of expanding the lists into more than 130,000 wide columns.

Source Dataset

  • Original dataset: TAAC2026/data_sample_1000
  • Source file: demo_1000.parquet
  • Source license: cc-by-nc-4.0
  • Source tags: TAAC2026, recommendation
  • Source scale: 1,000 rows, 120 top-level columns, about 39 MB
  • Source schema groups: 5 ID/label columns, 46 user integer features, 10 user dense features, 14 item integer features, and 45 domain sequence features

Converted Files

The upload contains 9 TsFile files under data/.

File Table Rows Notes
data/data_sample_1000_scalar.tsfile data_sample_1000_scalar 1,000 Scalar interaction features
data/data_sample_1000_user_int_lists.tsfile data_sample_1000_user_int_lists 11,560 11 user integer list features
data/data_sample_1000_user_dense_lists.tsfile data_sample_1000_user_dense_lists 318,538 10 user dense list features
data/data_sample_1000_item_lists.tsfile data_sample_1000_item_lists 2,086 1 item list feature
data/data_sample_1000_domain_a_seq.tsfile data_sample_1000_domain_a_seq 701,086 9 domain A sequence features
data/data_sample_1000_domain_b_seq.tsfile data_sample_1000_domain_b_seq 570,758 14 domain B sequence features
data/data_sample_1000_domain_c_seq.tsfile data_sample_1000_domain_c_seq 449,431 12 domain C sequence features
data/data_sample_1000_domain_d_seq_1.tsfile data_sample_1000_domain_d_seq 1,048,576 Domain D sequence shard 1
data/data_sample_1000_domain_d_seq_2.tsfile data_sample_1000_domain_d_seq 51,283 Domain D sequence shard 2

The sequence tables contain 3,153,318 rows in total. domain_d_seq was split into two TsFile shards by the TsFile conversion tool.

Schema Design

Scalar event table:

  • Time: epoch milliseconds derived from source timestamp * 1000
  • TAG columns: event_index, user_id
  • FIELD columns: item_id, label_type, label_time, event_timestamp, and all source scalar user/item features

Sequence tables:

  • Time: sequence_index within each source interaction event
  • TAG columns: event_index, user_id, item_id
  • FIELD columns: event_timestamp, label_time, label_type, plus the list feature values for that sequence family
  • event_timestamp preserves the source timestamp value in seconds

Conversion Notes

  • No source columns are intentionally dropped.
  • Source timestamp is renamed to event_timestamp and also used to create the scalar event table's TsFile Time.
  • Variable-length list columns are reshaped into per-family sequence tables. This preserves list positions while avoiding an extremely wide table.
  • Missing list positions caused by unequal sequence lengths are stored as nulls.
  • The converted layout keeps one event_index per original source row so users can join scalar and sequence tables back to the original interaction record.

Validation

Local validation confirmed that all 9 TsFile files are non-empty. TsFile metadata row counts match the staged Parquet row counts for every table, including both shards of data_sample_1000_domain_d_seq.

Minimal Read Example 

from tsfile import TsFileReader

path = "data/data_sample_1000_scalar.tsfile"
with TsFileReader(path) as reader:
    schemas = reader.get_all_table_schemas()
    print(schemas.keys())
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