Druid.io 查询分时段指标

查询语句示例

{
      "aggregations": [
            {
              "fieldName": "unique_user",
              "type": "thetaSketch",
              "name": "new_user",
              "isInputThetaSketch": false,
              "size": 16384
            }
          ],
          "filter": {
            "fields": [
              {
                "type": "selector",
                "dimension": "event",
                "value": "register"
              },
              {
                "type": "selector",
                "dimension": "register_days",
                "value": 0
              },
              {
                "type": "selector",
                "dimension": "appkey",
                "value": "apprenrenwang"
              },
              {
                "type": "selector",
                "dimension": "platform",
                "value": "android"
              }
            ],
            "type": "and"
          },
          "intervals": "2020-05-18T16:00:00.000Z/2020-05-19T16:00:00.000Z",
          "dataSource": "appyouyou-app_statistic",
         "granularity":{"type": "period", "period": "PT1H", "timeZone": "Asia/Shanghai", "origin": "2020-04-30T16:00:00"},
          "threshold": 10000,
          "queryType": "groupBy",
          "metric": "new_user"
}

上面的这个查询语句实现了查询北京时间2020年5月19日 0点到2020年5月20日 0点，24小时时段的新用户指标数据

返回结果

[
  {
    "version": "v1",
    "timestamp": "2020-05-19T00:00:00.000+08:00",
    "event": {
      "new_user": 55.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T01:00:00.000+08:00",
    "event": {
      "new_user": 51.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T02:00:00.000+08:00",
    "event": {
      "new_user": 36.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T03:00:00.000+08:00",
    "event": {
      "new_user": 11.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T04:00:00.000+08:00",
    "event": {
      "new_user": 9.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T05:00:00.000+08:00",
    "event": {
      "new_user": 30.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T06:00:00.000+08:00",
    "event": {
      "new_user": 38.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T07:00:00.000+08:00",
    "event": {
      "new_user": 54.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T08:00:00.000+08:00",
    "event": {
      "new_user": 53.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T09:00:00.000+08:00",
    "event": {
      "new_user": 56.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T10:00:00.000+08:00",
    "event": {
      "new_user": 65.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T11:00:00.000+08:00",
    "event": {
      "new_user": 71.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T12:00:00.000+08:00",
    "event": {
      "new_user": 64.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T13:00:00.000+08:00",
    "event": {
      "new_user": 72.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T14:00:00.000+08:00",
    "event": {
      "new_user": 57.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T15:00:00.000+08:00",
    "event": {
      "new_user": 83.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T16:00:00.000+08:00",
    "event": {
      "new_user": 89.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T17:00:00.000+08:00",
    "event": {
      "new_user": 63.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T18:00:00.000+08:00",
    "event": {
      "new_user": 59.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T19:00:00.000+08:00",
    "event": {
      "new_user": 69.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T20:00:00.000+08:00",
    "event": {
      "new_user": 63.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T21:00:00.000+08:00",
    "event": {
      "new_user": 77.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T22:00:00.000+08:00",
    "event": {
      "new_user": 91.0
    }
  },
  {
    "version": "v1",
    "timestamp": "2020-05-19T23:00:00.000+08:00",
    "event": {
      "new_user": 79.0
    }
  }
]

时段语句说明

 "granularity":{"type": "period", "period": "PT1H", "timeZone": "Asia/Shanghai", "origin": "2020-04-30T16:00:00"},

"type": "period" ：说明当前查询语句为 period 指标类型，

"period": "PT1H" ：当前查询的period时间粒度为1小时，（period值设置标准见附录1）

"timeZone": "Asia/Shanghai"： 查询返回结果需要的时区，这里我希望查询结果返回的时间是上海时区

"origin": "2020-04-30T16:00:00"：period指标的起始时间，这个时间是druid数据时间(默认是UTC时间，属于0时区)

附录

1 period值设置标准表

period时间粒度	值设置规范示例
年	P1Y,  P2Y,  P5Y......
月	P1M, P2M......
周	P2W, P5W, P7W,.....
日	P1D, P3D.....
小时	PT1H, PT2H, PT4H,....
分钟	PT1M,  PT3M,  PT5M,....
秒	PT30S, PT10S, ....