樣本數據

這個樣本數據使用了一個時序性能測試工具benchmark（https://github.com/influxdata/influxdb-comparisons）中的樣本數據生成工具。在安裝并編譯這個工具后，你可以使用下面兩步生成數據并加載到TSDB引擎中。

生成數據

cd influxdb-comparisons/cmd
bulk_data_gen/bulk_data_gen --seed=123 --use-case=devops --scale-var=10 --format=opentsdb --timestamp-start="2019-03-01T00:00:00Z" --timestamp-end="2019-03-01T00:10:00Z" > tsdb_devops_sf10_10m_seed123.json

下面顯示了部分樣本數據：

{"metric":"redis.evicted_keys","timestamp":1551398990000,"tags":{"arch":"x86","datacenter":"us-east-1b","hostname":"host_9","os":"Ubuntu16.10","port":"1470","rack":"7","region":"us-east-1","server":"redis_29176","service":"14","service_environment":"production","service_version":"0","team":"LON"},"value":2951}
{"metric":"redis.keyspace_hits","timestamp":1551398990000,"tags":{"arch":"x86","datacenter":"us-east-1b","hostname":"host_9","os":"Ubuntu16.10","port":"1470","rack":"7","region":"us-east-1","server":"redis_29176","service":"14","service_environment":"production","service_version":"0","team":"LON"},"value":2945}
{"metric":"redis.keyspace_misses","timestamp":1551398990000,"tags":{"arch":"x86","datacenter":"us-east-1b","hostname":"host_9","os":"Ubuntu16.10","port":"1470","rack":"7","region":"us-east-1","server":"redis_29176","service":"14","service_environment":"production","service_version":"0","team":"LON"},"value":2944}
{"metric":"redis.instantaneous_ops_per_sec","timestamp":1551398990000,"tags":{"arch":"x86","datacenter":"us-east-1b","hostname":"host_9","os":"Ubuntu16.10","port":"1470","rack":"7","region":"us-east-1","server":"redis_29176","service":"14","service_environment":"production","service_version":"0","team":"LON"},"value":65}
{"metric":"redis.instantaneous_input_kbps","timestamp":1551398990000,"tags":{"arch":"x86","datacenter":"us-east-1b","hostname":"host_9","os":"Ubuntu16.10","port":"1470","rack":"7","region":"us-east-1","server":"redis_29176","service":"14","service_environment":"production","service_version":"0","team":"LON"},"value":58}

加載數據

cat tsdb_devops_sf10_10m_seed123.json | bulk_load_opentsdb/bulk_load_opentsdb --urls=http://your_tsdb_host:port_num -workers=5

查詢

• 時間范圍查詢：查看一個metric在一個時間段內的所有的列，包括值，時間戳，以及對應的tag key的值。

select *
from tsdb.`cpu.usage_system` 
where `timestamp` between '2019-03-01 00:00:00' and '2019-03-01 00:00:10'

• 時間范圍查詢：查詢一個metric在一個時間段內的指定的列，包括值，時間戳，以及具體某幾個tag key的值。

select `value`, `timestamp`, hostname,  datacenter
from tsdb.`cpu.usage_system` 
where `timestamp` between '2019-03-01 00:00:00' and '2019-03-01 00:00:10'

• 時間范圍+tagkey條件查詢：查詢一個metric在時間段內的值，時間戳， 并且hostname滿足IN-LIST的條件。

select `value`, `timestamp`, hostname,  datacenter
from tsdb.`cpu.usage_system` 
where `timestamp` between '2019-03-01 00:00:00' and '2019-03-01 00:00:10' and
      hostname in ('host_0', 'host_2', 'host_4')

• 查詢結果按時間戳排序：查看一個metric在一個時間段內的值，時間戳，以及對應的tag key的值。

select *
from tsdb.`cpu.usage_system` 
where `timestamp` between '2019-03-01 00:00:00' and '2019-03-01 00:00:10'
order by `timestamp`

• 含數學計算表達式的值過濾條件查詢：查看一個metric在一個時間段內的值，時間戳，以及對應的tag key的值， 值滿足其平方根>1.5。

select *
from tsdb.`cpu.usage_system` 
where `timestamp` between '2019-03-01 00:00:00' and '2019-03-01 00:00:10' and
    sqrt(`value`) > 1.5

• 分組聚合查詢：按照hostname, datacenter來分組，計算每個分組最大值，最小值，平均值。

select 
    hostname, 
    datacenter, 
    max(`value`) as maxV, 
    min(`value`) as minV, 
    avg(`value`) as avgV
from tsdb.`cpu.usage_system` 
where `timestamp` between '2019-03-01 00:00:00' and '2019-03-01 00:00:10'
group by hostname, datacenter

• 分組分時間段聚合查詢，按照hostname, datacenter來分組，并且進一步按照2分鐘的間隔分組，計算最大值，最小值，平均值。

select 
   hostname, 
   datacenter, 
   tumble(`timestamp`, interval '2' minute) as ts, 
   max(`value`) as maxV, 
   min(`value`) as minV, 
   avg(`value`) as avgV
from tsdb.`cpu.usage_system` 
where `timestamp` between '2019-03-01 00:00:00' and '2019-03-01 00:10:00'
group by hostname, datacenter, ts

• 分組分時間段聚合查詢, 計算聚合后的表達式的值max(value) - min(value) + 0.5* avg(value)。

select 
    hostname, 
    datacenter, 
    tumble(`timestamp`, interval '2' minute) as ts, 
    max(`value`) - min(`value`) + 0.5* avg(`value`) as compV
from tsdb.`cpu.usage_system` 
where `timestamp` between '2019-03-01 00:00:00' and '2019-03-01 00:10:00'
group by hostname, datacenter, ts

• 計算每臺機器上，相鄰兩個時間戳上的記錄值之間的差值。

下面的例子使用window function lag(), lag()函數使用一個基于hostname做分組，時間戳排序的窗口frame， 返回在同一個窗口內當前記錄的前一條記錄的值，通過計算兩者之差，獲取每臺主機上相鄰時間戳的記錄值上的差值。

select hostname, `timestamp`, `value`, 
       `value` - lag(`value`) over(partition by hostname order by `timestamp`) as diff
from tsdb.`cpu.usage_system` 
  where `timestamp` between '2019-03-01' and '2019-03-01 00:10:00'

• 計算每臺機器上，相鄰兩個時間戳上的記錄值之間的差值， 如果差值超過異常值，則重設成0。

下面的例子，把上面的查詢放在一個子查詢中，在子查詢之外用了一個case表達式，來表達如果查詢中的差值超過50.0（認為是異常值）， 則重設成0.0。

select hostname, `timestamp`, `value`, 
        case when diff > 50.0 then 0.0
                else diff
        end
from (
    select hostname, `timestamp`, `value`, 
       `value` - lag(`value`) over(partition by hostname order by `timestamp`) as diff
    from tsdb.`cpu.usage_system` 
      where `timestamp` between '2019-03-01' and '2019-03-01 00:10:00'
);

• 計算每臺機器上一分鐘內的記錄的最大值，以及相鄰分鐘之間最大值的差值。

下面的查詢區別于之前差值計算，在于要獲取每分鐘的最大值的差值。我們可以在子查詢中計算每分鐘的最大值，在子查詢外用窗口函數lag(）計算獲得相鄰分鐘之間最大值的差值。

select hostname, ts, maxValue, 
             maxValue - lag(maxValue) over(partition by hostname order by ts) as diff
from (
  select hostname, 
              tumble(`timestamp`, interval '1' minute) ts, max(`value`) maxValue
  from tsdb.`cpu.usage_system` 
  where `timestamp` between '2019-03-01' and '2019-03-01 00:10:00'
  group by hostname, ts)