Datasets:
File size: 4,290 Bytes
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license: cc-by-nc-4.0
tags:
- TAAC2026
- recommendation
- tsfile
- timeseries
- format:tsfile
modality:
- tabular
- timeseries
pretty_name: TAAC2026 Data Sample 1000 TsFile
configs:
- config_name: default
data_files:
- split: train
path: data/*.tsfile
size_categories:
- 1K<n<10K
---
# TAAC2026 Data Sample 1000 TsFile
This dataset is a TsFile conversion of
[`TAAC2026/data_sample_1000`](https://huggingface.co/datasets/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`](https://huggingface.co/datasets/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
```python
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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