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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