| --- |
| license: apache-2.0 |
| task_categories: |
| - time-series-forecasting |
| tags: |
| - tsfile |
| - format:tsfile |
| - timeseries |
| modality: timeseries |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: app_flow.tsfile |
| --- |
| |
| # App Flow |
|
|
| This dataset consists of hourly maximum traffic flow for 128 systems deployed on 16 logic data centers, resulting in 1083 different time series in total. |
| The length of each series is more than 4 months. Each time series is divided into two segments for training and testing with a ratio of 32:1. |
| This dataset was collected at Ant Group and does not contain any Personal Identifiable Information and is desensitized and encrypted. |
|
|
| ## TsFile Conversion |
|
|
| - Original dataset: [`kashif/App_Flow`](https://huggingface.co/datasets/kashif/App_Flow) |
| - Modalities: Time-series |
| - Converted data files are listed in the YAML metadata above. |
| - Source README text and dataset-specific metadata are retained; the source |
| Usage section is replaced with the executable TsFile Python SDK example below. |
|
|
| - The encrypted source CSV is decoded as provided; `app_name`, `zone`, and generated `event_rank` are TAG columns, `time` becomes millisecond `Time`, and the CSV export index is retained as the `source_row_id` FIELD. |
| - The source contains 1,189 observed `(app_name, zone)` combinations (the source card states 1,083) and 4,766 concurrent duplicate time keys. `event_rank` distinguishes those concurrent rows without changing their original minute timestamps; 4,766 additional rows remain intentionally represented across distinct event-rank devices. |
| - All source rows and values are retained and sorted by `app_name`, `zone`, `Time`, and `event_rank`. |
|
|
| ## Schema (TsFile structure) |
|
|
| - `Time` (INT64, milliseconds) is the original CSV `time` value. |
| - `app_name`, `zone`, and `event_rank` are TAG dimensions. |
| - `value` and `source_row_id` are FIELD measurements; `source_row_id` is the original CSV export row number. |
|
|
| ## Usage |
|
|
| Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file: |
|
|
| ```python |
| from pathlib import Path |
| from tsfile import TsFileReader |
| |
| path = Path("app_flow.tsfile") |
| with TsFileReader(str(path)) as reader: |
| schemas = reader.get_all_table_schemas() |
| print("tables:", list(schemas)) |
| table_name = next(iter(schemas)) |
| table = schemas[table_name] |
| columns = [column.get_column_name() for column in table.get_columns()] |
| print("columns:", columns) |
| field_names = [ |
| column.get_column_name() |
| for column in table.get_columns() |
| if column.get_column_name() not in {"Time", "time"} |
| ] |
| if field_names: |
| with reader.query_table(table_name, field_names[:3], batch_size=1024) as result: |
| batch = result.read_arrow_batch() |
| if batch is not None: |
| print(batch.to_pandas().head()) |
| ``` |
|
|
|
|
