查询操作
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          时序时空数据库 TSDB

          查询操作

          获取度量(Metric)

          如下代码可以获取metric列表:

          # 获取metric列表
          result = tsdb_client.get_metrics()
          print result.metrics

          返回结果:

          [u'wind']

          获取域(Field)

          如下代码可以获取field对象:

          metric = 'wind' # 要获取field所属的metric
          result = tsdb_client.get_fields(metric) # 获取指定metric下的所有field
          print result.fields

          返回结果:

          {direction:{type:u'Number'},speed:{type:u'Number'}}

          获取标签(Tag)

          如下代码可以获取tag对象:

          metric = 'wind' # 要获取tag所属的metric
          result = tsdb_client.get_tags(metric)
          print result.tags

          返回结果:

          {city:[u'ShangHai']}

          查询数据点

          查询单域数据点

          如下代码可以查询单域数据点:

          #构建查询对象
          query_list = [{
              "metric": "wind",
              "field": "direction",
              "filters": {
                  "start": 1531985370000,
                  "end": 1531985400000,
                  "tags": {
                      "city": ["ShangHai"]
                  },
                  "value": ">= 0"
              },
              "groupBy": [{
                  "name": "Tag",
                  "tags": ["city"]
              }],
              "limit": 1000,
              "aggregators": [{
                  "name": "Sum",
                  "sampling": "10 minutes"
              }]
          }]
          result = tsdb_client.get_datapoints(query_list)
          print result.results

          返回结果:

          [{field:u'direction',metric:u'wind',groups:[{group_infos:[{name:u'Tag',tags:{city:u'ShangHai'}}],values:[[1531985379000, 2]]}],rawcount:2}]

          查询多域数据点

          如下代码可以查询多域数据点:

          #构建查询对象
          query_list = [{
              "metric": "wind",
              "fields": ["direction", "speed"],   #查询多个域
              "filters": {
                  "start": 1531985370000,
                  "end": 1531985400000,
                  "tags": {
                      "city": ["ShangHai"]
                  },
                  "value": ">= 0"
              },
              "groupBy": [{
                  "name": "Tag",
                  "tags": ["city"]
              }],
              "limit": 1000,
              "aggregators": [{
                  "name": "Sum",
                  "sampling": "10 minutes"
              }]
          }]
          result = tsdb_client.get_datapoints(query_list)
          print result.results

          返回结果:

          [{fields:[u'direction', u'speed'],metric:u'wind',groups:[{group_infos:[{name:u'Tag',tags:{city:u'ShangHai'}}],values:[[1531985380000, 1, 4.5]]}],rawcount:1}]

          使用插值方式查询数据

          使用如下代码进行插值查询:

          # 构建查询对象
          query_list = [{
              "metric": "wind",
              "fields": ["direction", "speed"],
              "filters": {
                  "start": 1531985370000,
                  "end": 1532985370000,
                  "tags": {
                      "city": ["ShangHai"]
                  },
                  "value": ">= 0"
              },
              "fill": {                             #配置插值参数
                  "type": "Linear",                 #插值类型
                  "interval": "1 day",              #插值间隔
                  "maxWriteInterval": "30 minutes"  #最大写入间隔
              },
              "groupBy": [{
                  "name": "Tag",
                  "tags": ["city"]
              }],
              "limit": 1000,
              "aggregators": [{
                  "name": "Sum",
                  "sampling": "10 minutes"
              }]
          }]
           
          result = tsdb_client.get_datapoints(query_list)
          print result.results

          返回结果:

          [{fields:[u'direction', u'speed'],metric:u'wind',groups:[{group_infos:[{name:u'Tag',tags:{city:u'ShangHai'}}],values:[[1531985380000, 1, 4.5], [1532071780000, 1, 4.5], [1532158180000, 1, 4.5], [1532244580000, 1, 4.5], [1532330980000, 1, 4.5], [1532417380000, 1, 4.5], [1532503780000, 1, 4.5], [1532590180000, 1, 4.5], [1532676580000, 1, 4.5], [1532762980000, 1, 4.5], [1532849380000, 1, 4.5], [1532935780000, 1, 4.5]]}],rawcount:12}]

          分页查询数据点

          如果查询到的原始数据点过大,会分多次返回,使用marker参数来进行分页查询。

          # 通过循环来多次查询数据
           
          query_list = [{
              "metric": "wind",
              "field": "direction",
              "filters": {
                  "start": 1531985300000,
                  "end": 1531985400000,
                  "tags": {
                      "city": ["ShangHai"]
                  }
              },
              "limit": 1
          }]
          count = 0
          while True:
              count += 1
              if len(query_list) >0:
                  result = tsdb_client.get_datapoints(query_list)
                  print count, result.results
              else:
                  print 'end query'
                  break
              next_query = []
              for i in range(len(query_list)):
                  if result.results[i].truncated:
                      query_list[i]['marker'] = result.results[i].next_marker
                      next_query.append(query_list[i])
              query_list = next_query

          返回结果:

          1 [{field:u'direction',next_marker:u'AAABZLFxwqBjaXR5PVNoYW5nSGFp',groups:[{group_infos:[],values:[[1531985379000, 1]]}],truncated:True,metric:u'wind',rawcount:1}]
          2 [{field:u'direction',metric:u'wind',groups:[{group_infos:[],values:[[1531985380000, 1]]}],rawcount:1}]
          end query

          使用SQL查询数据点

          TSDB支持使用SQL进行数据查询,示例代码如下:

          sql = "select * from wind"
          result = tsdb_client.get_rows_with_sql(sql)
          print result

          返回结果:

          {rows:[[1531985379000, 1.0, None, u'ShangHai'], [1531985380000, 1.0, 4.5, u'ShangHai']],columns:[{name:u'timestamp'}, {name:u'direction'}, {name:u'speed'}, {name:u'city'}],metadata:{content_length:'168',content_type:'application/json; charset=UTF-8',keep_alive:'timeout=10',server:'BWS',connection:'keep-alive',pragma:'no-cache',date:'Thu, 02 Aug 2018 00:35:28 GMT',bce_request_id:'46f5d19e-58de-4654-bc35-91bb67b58e76',cache_control:'no-cache'}}
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