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Table Chart¶

Table charts include grouped table charts and time series table charts.

Grouped Table Chart Data Structure Description¶

// The columns of the following demo data table are ['host', 'host_ip', 'columnA', 'columnB']
{
    group_by: ['host', 'host_ip'],
    column_names: ['columnA', 'columnB'],
    series: [
      {
        tags: {
          host: 'host_1',
          host_ip: '111.11.123.103',
        },
        values: [[null, 1,2]],
        column_names: ['time', 'columnA', 'columnB'],
        columns: ['time', 'columnA', 'columnB'],
      },
      {
        tags: {
          host: 'host_2',
          host_ip: '111.11.123.101',
        },
        values: [[null, 3,4]],
        column_names: ['time', 'columnA', 'columnB'],
        columns: ['time', 'columnA', 'columnB'],
      },
      {
        tags: {
          host: 'host_3',
          host_ip: '111.11.123.102',
        },
        values: [[null, 5,6]],
        column_names: ['time', 'columnA', 'columnB'],
        columns: ['time', 'columnA', 'columnB'],
      },
      {
        tags: {
          host: 'host_4',
          host_ip: '111.11.123.106',
        },
        values: [[null, 7,8]],
        column_names: ['time', 'columnA', 'columnB'],
        columns: ['time', 'columnA', 'columnB'],
      },
    ],
  },

The column values of the grouped table chart are composed by merging group_by and column_names. group_by can be empty.

Field description:

Parameter	Type	Required	Description
group_by	list		Part of the table columns; the corresponding column values are obtained from the tags object in each item of `series data`
group_by[#]	str
column_names	list	Required	Part of the table columns; should correspond to non-`time` field values in `series data`'s column_names; corresponding column values are taken from `series[#].values`
column_names[#]	str
series	list	Required	Data groups; the length indicates how many rows of data there are in the table
series[#]	dict		A collection of data
series[#].tags	dict		Attribute values for the `group_by` table columns (mapping values corresponding to the `group_by` part of columns, also used as alias key mappings)
series[#].columns	list	Required	Same as `series[#].column_names`['time', ...]
series[#].columns[#]	str		Source data field key; the first column must be the `time` field
series[#].column_names	list	Required	Source data field keys; other than `time`, they are used as references for table columns
series[#].column_names[#]	str
series[#].values	list		Data groups; their lengths should match that of `series[#].columns` (in table charts, mapping values corresponding to columns referenced by `column_names`)
series[#].values[#]	list		Composed of `[null, data value, ...]`
series[#].values[#][#]	str

External Function Response Structure Example¶

@DFF.API('Function Name', category='dataPlatform.dataQueryFunc')
def whytest_topology_test():
    data1_1 = 100
    data1_2 = 101
    data2_1 = 200
    data2_2 = 201
    now1 = int(time.time()) * 1000
    now2 = int(time.time()) * 1000
    #
    return {
    "content": [
      {
        "group_by": ['attrA'],
        "columns": ["filedA","filedB"]
        "column_names": ["filedA","filedB"]
        "series": [
          {
            "tags": {"attrA":'value1'},
            "columns": ["time", "filedA","filedB"],
            "values": [
              [now1, data1_1,data1_2],
              [now2, data2_1,data2_2]
            ],
            "total_hits": -1
          }
        ]
      }
    ]
  }

Time Series Table Chart Data Structure Description¶

// The columns of the following demo data table are ['fieldA', 'fieldB', 'fieldC', 'fieldD']
{
  "query": {},
  "series": [
    {
      "values": [
        [1737365938763, 19],
        [1737365938585, 20],
        [1737365938874, 21],
        [1737365939137, 22]
      ],
      "columns": ["time", "fieldA"]
    },
    {
      "values": [
        [1737365938763, 30],
        [1737365938585, 30.5],
        [1737365938874, 31],
        [1737365939137, 31.5]
      ],
      "columns": ["time", "fieldB"]
    },
    {
      "values": [
        [1737365938763, 50],
        [1737365938585, 50.5],
        [1737365938874, 51],
        [1737365939137, 51.5]
      ],
      "columns": ["time", "fieldC"]
    },
    {
      "values": [
        [1737365938763, 60],
        [1737365938585, 60.5],
        [1737365938874, 61],
        [1737365939137, 61.5]
      ],
      "columns": ["time", "fieldD"]
    }
  ]
}

The column values of a time series table chart are composed by deduplicating and merging the second column data from series[#].columns.

Field description:

Parameter	Type	Required	Description
series	list	Required	Data groups; the length indicates how many sets of data there are in the table
series[#]	dict		A collection of data
series[#].columns	list	Required	Consists of `time` and `column name`, i.e., `['time', column name]`
series[#].columns[#]	str
series[#].values	list		Two-dimensional array; each entry represents the value of the column at different time points; the array length affects the number of rows in the table
series[#].values[#]	list		`[timestamp, data value]`
series[#].values[#][#]	str

External Function Response Structure Example¶

@DFF.API('Function Name', category='dataPlatform.dataQueryFunc')
def whytest_topology_test():
    data1_1 = 100
    data1_2 = 101
    data2_1 = 200
    data2_2 = 201
    data3_1 = 101
    data3_2 = 202
    now1 = int(time.time()) * 1000
    now2 = int(time.time()) * 1000
    now3 = int(time.time()) * 1000
    #
    return {
    "content": [
      {
        "series": [
          {
            "columns": ["time", "filedA"],
            "values": [
              [now1, data1_1],
              [now2, data2_1],
              [now3, data3_1],
            ],
          },
          {
            "columns": ["time", "filedB"],
            "values": [
              [now1, data1_2],
              [now2, data2_2],
              [now3, data3_2]
            ],
          }
        ]
      }
    ]
  }