BTW, for model 1 there will be 6 millions data points for database 
"devices" (600K data points per database/measurement). 
For model 3, there will be 600K data points per database (each database has 
its own metric).




On Monday, September 26, 2016 at 8:52:19 PM UTC-4, Carlo Vargas wrote:
>
>
> Currently I am evaluating different Time Series data bases and I do have 
> some questions regarding data modelling and query performance in InfluxDB.
>
> Context: We have 200 000 devices and 10 metrics per device (for instance: 
> SYSTEM_MEMORY).
>
> Devices were processed 3 times, so we ended up with 600K data points.
>
> Here are the 3 models that were used:
>
> Model 1: One database named "devices", 10 measurements (one for each 
> metric), and the tag deviceId.
>
>                 Issues:
>                 - Queries by value are not responding. Example of this 
> query: select * from SYSTEM_MEMORY where value > 80
>                 - It uses a lot of RAM, the server crashes when the above 
> query is executed or when the following Continuous Query is also executed:
>                   ... BEGIN SELECT mean(value) as mean_value INTO 
> devices."<current_policy>".:MEASUREMENT FROM devices."<new_policy>"./.*/ 
> GROUP 
> BY time(5m), deviceId
>
>
> Model 2: One database named "devices", one measurement named "data", 
> deviceId tag and each metric as a field.                
>
>                 Issues:
>                 - Queries by value are slow (more than a minute). 
> Example: select * from "data" where SYSTEM_MEMORY > 80 
>                 - Continuous Query takes so much time: 
>                   ... BEGIN SELECT mean(SYSTEM_MEMORY) as SYSTEM_MEMORY_mean 
> INTO .... FROM  data GROUP BY time(5m), deviceId
>
>
> Model 3:  One database per metric, one measurement "data", and deviceId 
> tag.
>
>                 Issues:
>                 - Queries by value takes around 25 seconds. Example: 
> select * from "data" where value > 80 (this query is done in SYSTEM_MEMORY 
> database)
>                 - Continuous Query needs to be created for each database 
> and they are slow.
>                 - Adding data points is slower than previous two models.
>
>
> Any advice/suggestion would be really appreciated.
>
> Thanks in advance.
>

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