Tablestore:Write time series data

After you run the use --ts -t <TIMESERIES_TABLE> command to specify a time series table, you can use the Tablestore CLI to run the following commands to write time series data to the time series table.

• Run the putts command to insert a row of data. For more information, see the Write time series data section of the Operations on time series data topic.

  The following sample code provides an example on how to insert a row of time series data into a time series table:

  putts --k '["cpu","localhost",["region=hangzhou","os=ubuntu"]]' --field '[{"c":"fieldname","v":"fieldvalue"},{"c":"bool_field","v":true},{"c":"double_field","v":1.1},{"c":"int_value","v":10,"isint":true}]' --time 1635162859000000

• Run the import_timeseries command to import time series data from a local file to the time series table. For more information, see the Import time series data section of the Operations on time series data topic.

  Assume that the file is named import_timeseries.txt and stored in the /temp/ directory. Each row in the following example represents a row of time series data in the measurement_name,tags fields timestamp format.

  The following sample code shows the content of a configuration file:

  cpu,hostname=host_0,region=cn-hangzhou usage_user=58i,usage_system=2i,usage_idle=24i 1609459200000000000
  cpu,hostname=host_1,region=cn-hangzhou usage_user=58i,usage_system=2i,usage_idle=24i 1609459200000000000

  The following sample code shows how to import time series data from the import_timeseries.txt file to a time series table:

  import_timeseries --input /temp/import_timeseries.txt

You can use Tablestore SDK for Java and Tablestore SDK for Go to write time series data. In this example, Tablestore SDK for Java is used.

The following sample code provides an example on how to write multiple rows of time series data to a time series table named test_timeseries_table:

private static void putTimeseriesData(TimeseriesClient client) {
    List<TimeseriesRow> rows = new ArrayList<TimeseriesRow>();
    for (int i = 0; i < 10; i++) {
        Map<String, String> tags = new HashMap<String, String>();
        tags.put("region", "hangzhou");
        tags.put("os", "Ubuntu16.04");
        // Specify the measurement name, data source, and tags of a time series to construct the identifier of the time series. 
        TimeseriesKey timeseriesKey = new TimeseriesKey("cpu", "host_" + i, tags);
        // Specify the timeseriesKey and timeInUs parameters to create a row of time series data. 
        TimeseriesRow row = new TimeseriesRow(timeseriesKey, System.currentTimeMillis() * 1000 + i);
        // Add data values. 
        row.addField("cpu_usage", ColumnValue.fromDouble(10.0));
        row.addField("cpu_sys", ColumnValue.fromDouble(5.0));
        rows.add(row);
    }
    String tableName = "test_timeseries_table";
    PutTimeseriesDataRequest putTimeseriesDataRequest = new PutTimeseriesDataRequest(tableName);
    putTimeseriesDataRequest.setRows(rows);
    // Write multiple rows of time series data at a time. 
    PutTimeseriesDataResponse putTimeseriesDataResponse = client.putTimeseriesData(putTimeseriesDataRequest);
    // Check whether all data is written to the time series table. 
    if (!putTimeseriesDataResponse.isAllSuccess()) {
        for (PutTimeseriesDataResponse.FailedRowResult failedRowResult : putTimeseriesDataResponse.getFailedRows()) {
            System.out.println(failedRowResult.getIndex());
            System.out.println(failedRowResult.getError());
        }
    }
}