| --- |
| license: mit |
| tags: |
| - tsfile |
| - timeseries |
| - time-series |
| - format:tsfile |
| pretty_name: live_cpu_utilization (TsFile) |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: live_cpu_utilization_g0.tsfile |
| - split: train |
| path: live_cpu_utilization_g1.tsfile |
| --- |
| |
| # live_cpu_utilization (TsFile) |
|
|
| Apache TsFile version of [`ICOS-AI/live_cpu_utilization`](https://huggingface.co/datasets/ICOS-AI/live_cpu_utilization). |
|
|
| ## Overview |
|
|
| - **Tables:** 2 TsFile table(s), 2,488 rows in total. |
| - `live_cpu_utilization_g0`: 499 rows, 2023-06-28 16:26:24 鈫?2023-06-29 01:05:58 (UTC) |
| - `live_cpu_utilization_g1`: 1,989 rows, 2024-09-02 13:55:00 鈫?2024-09-09 11:35:00 (UTC) |
|
|
| ## Schema (TsFile structure) |
|
|
| ### `live_cpu_utilization_g0.tsfile` |
| |
| - **Time** (INT64, milliseconds) 鈥?the source `Date` column. |
| - **TAG**: none 鈥?the table is stored as a single device. |
| - **FIELD** (10): `CPU_Core_0_Usage` (string), `CPU_Core_1_Usage` (double), `CPU_Core_2_Usage` (double), `CPU_Core_3_Usage` (double), `CPU_Core_4_Usage` (double), `CPU_Core_5_Usage` (double), `CPU_Core_6_Usage` (double), `CPU_Core_7_Usage` (double), `CPU_Usage_All_Cores` (double), `Memory_Usage` (double) |
| |
| ### `live_cpu_utilization_g1.tsfile` |
|
|
| - **Time** (INT64, milliseconds) 鈥?the source `timestamp` column. |
| - **TAG**: none 鈥?the table is stored as a single device. |
| - **FIELD** (2): `CPU` (double), `MEM` (double) |
|
|
| ## Conversion notes |
|
|
| - One TsFile per source table (2 tables). Tables whose source carries several entities per file are stored as one device per entity. |
| - Time = the table's own clock column as INT64 milliseconds. The clock columns used here are `Date`, `timestamp`. |
| - No column is dropped. Rows that share a timestamp inside one device are ordered by a 1 ms step in stable source order, so calendar columns keep their exact day. |
|
|
| ## Usage |
|
|
| > **Read this dataset with the Java SDK.** It is written with the `zstd` compressor. The Apache TsFile Java SDK reads every compressor, but the Python SDK (`pip install tsfile`) implements only `UNCOMPRESSED`, `SNAPPY`, `GZIP` and `LZ4`; opening this file with it yields no rows. |
|
|
| Read a converted file with the Apache TsFile Java SDK (`org.apache.tsfile:tsfile`): |
|
|
| ```java |
| import org.apache.tsfile.file.metadata.IDeviceID; |
| import org.apache.tsfile.read.TsFileReader; |
| import org.apache.tsfile.read.TsFileSequenceReader; |
| import org.apache.tsfile.read.common.Path; |
| import org.apache.tsfile.read.expression.QueryExpression; |
| import org.apache.tsfile.read.query.dataset.QueryDataSet; |
| import org.apache.tsfile.write.schema.MeasurementSchema; |
| |
| import java.util.ArrayList; |
| import java.util.List; |
| |
| String path = "live_cpu_utilization_g0.tsfile"; |
| try (TsFileSequenceReader sequenceReader = new TsFileSequenceReader(path); |
| TsFileReader reader = new TsFileReader(sequenceReader)) { |
| // Devices are enumerated as IDeviceID objects. Tables with TAG columns |
| // have multi-segment device ids that cannot be rebuilt from their string |
| // form, so always use getAllDevices() rather than getAllDeviceIds(). |
| for (IDeviceID device : sequenceReader.getAllDevices()) { |
| List<Path> paths = new ArrayList<>(); |
| for (MeasurementSchema schema : reader.getMeasurement(device)) { |
| String name = schema.getMeasurementName(); |
| if (name != null && !name.isEmpty() && !name.equalsIgnoreCase("Time")) { |
| paths.add(new Path(device, name, true)); |
| } |
| } |
| QueryDataSet dataSet = reader.query(QueryExpression.create(paths, null)); |
| while (dataSet.hasNext()) { |
| System.out.println(dataSet.next()); |
| } |
| } |
| } |
| ``` |
|
|
| ## Source & license |
|
|
| - Original dataset: [`ICOS-AI/live_cpu_utilization`](https://huggingface.co/datasets/ICOS-AI/live_cpu_utilization) |
| - License: mit (as declared by the original dataset). |
|
|
