I have a set of measurements that are stored in various tables in InfluxDB1.8, which I am trying to query with Flux in Grafana. My goal is to perform automatic analysis on a set of the measurements, which requires combining them into one table.
Below is an example of the code that does the join of the two tables. However, the names of the columns in the joint table are too long and also depend on the time range, which makes them totally unusable.
Here is an example of the first column name produced with the code below:
_value_I {_field_I=“Value”, _field_V=“Value”, _measurement_I=“alias/tuningDevices/accelerator/actualValue2”, _measurement_V=“alias/tuningDevices/accelerator/actualValue1”, _start_I=“2025-01-09 03:48:40.43 +0000 UTC”, _start_V=“2025-01-09 03:48:40.43 +0000 UTC”, _stop_I=“2025-01-09 09:48:40.43 +0000 UTC”, _stop_V=“2025-01-09 09:48:40.43 +0000 UTC”}
My question: Is it at all possible in the InfluxDB1.8 to combine tables to perform analysis on them with Flux or do I need to upgrade?
flux
V_acc = “alias/tuningDevices/accelerator/actualValue1”
I_acc = “alias/tuningDevices/accelerator/actualValue2”
I = from(bucket: “acsdb/autogen”)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r[“_measurement”] == I_acc)
|> filter(fn: (r) => r[“_field”] == “Value”)
|> aggregateWindow(every: 1m, fn: mean)
|> yield(name: “I”)
V = from(bucket: “acsdb/autogen”)
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r[“_measurement”] == V_acc)
|> filter(fn: (r) => r[“_field”] == “Value”)
|> aggregateWindow(every: 1m, fn: mean)
|> yield(name: “V”)
join(
tables: {V:V, I:I},
on: [“_time”],
)
