Datasets:

wangdx25
/

live_cpu_utilization

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.

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):

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
License: mit (as declared by the original dataset).