Using the quantile function, I was able to get 95 % percentile value in a stream.
Now, i want to filter records which lie below the 95% percentile.
hence, I loop over my recods and filter records which lie below the percentile.
However, at this topic I get error –
Please find code below –
percentile = totalTimeByDoc
|> filter(fn: (r) => r["documentType"] == "PurchaseOrder")
|> group(columns:["documentType"])
// |> yield()
|> quantile(column: "processTime", q: 0.95, method: "estimate_tdigest", compression: 9999.0)
|> limit(n: 1)
|> rename(columns: {processTime: "pt"})
Gives me data – >
0 PurchaseOrder 999
Now, I try to loop over my records and filter -
percentile_filered = totalTimeByDoc
|> filter(fn: (r) => r["documentType"] == "PurchaseOrder")
|> filter(fn: (r) => r.processTime < percentile[0]["pt"])
|> yield()
Where, totalTimeByDoc is like below –
|0|PurchaseOrder|testpass22PID230207222747-1|1200|
|1|PurchaseOrder|testpass22PID230207222747-2|807|
|2|PurchaseOrder|testpass22PID230207222934-1|671|
|3|PurchaseOrder|testpass22PID230207222934-2|670|
I get following error from above query –
error #116:41-116:51: expected [{A with pt: B}] (array) but found stream[{A with pt: B}]
You are only missing column extraction from percentile stream. Have a look at Extract scalar values. In this very case, you could do
percentile = totalTimeByDoc
|> ...
|> rename(columns: {processTime: "pt"})
|> findColumn(fn: (key) => true, column: "pt")
percentile_filtered = totalTimeByDoc
|> filter(fn: (r) => r["documentType"] == "PurchaseOrder")
|> filter(fn: (r) => r.processTime < percentile[0])
|> yield()
Related
Trying to write my fist TICKscript to work out when two sensor values cross: if the outside temperature has changed from lower to higher than the inside temperature then I need to close the windows (and conversely).
Using the query builder in InfluxDB I'm getting this for the meadian of the temperature values inside the house over the last 15 minutes:
from(bucket: "zigbee")
|> range(start: -15m, stop: now())
|> filter(fn: (r) => r["room"] == "Kitchen" or r["room"] == "DiningRoom" or r["room"] == "Bed3" or r["room"] == "Bed1")
|> filter(fn: (r) => r["_field"] == "temperature")
|> group(columns: ["_measurement"])
|> aggregateWindow(every: 15m, fn: mean, createEmpty: false)
|> yield(name:"inside")
The syntax |> appears to undocumented -- can you provide a reference?
Replacing |> with | breaks it.
It seems that group and aggregateWindow do not commute?
Presumably because aggregateWindow is forced to choose a single representative _time value for each window?
I think the plan is to
assign this to a stream,
copy and edit to creata a second stream shifted by 15 minutes,
create a second pair of streams for the outside temperature.
join all four streams and caluclate a value indicating whether the inside and outside temperatures have crossed over.
Unless you have a better idea?
(Right now it's looking easier to import the data into SQL.)
Check InfluxDB Flux language documentation for |>:
InfluxDB Pipe-forward operator
According to your flux syntax query:
from(bucket: "zigbee")
|> range(start: -15m, stop: now())
|> filter(fn: (r) => r["room"] == "Kitchen" or r["room"] == "DiningRoom" or r["room"] == "Bed3" or r["room"] == "Bed1")
|> filter(fn: (r) => r["_field"] == "temperature")
|> group(columns: ["_measurement"])
|> aggregateWindow(every: 15m, fn: mean, createEmpty: false)
|> yield(name:"inside")
You are taking data from bucket "zigbee"
Data from source are passed to range filter function with pipe-forward |> operator
Results from range filter data are passed to next filter function with another pipe-forward operator
Etc.
So all data flows as a result from one function to another.
You can group by but in your case columns are "room" key values if I understand your intentions correctly, so try:
|> group(columns: ["room"])
There is a difference between key values and measurement names - you should check InfluxDB documentation for understatnding data structure.
Flux data model documentation
I'ts not TICKscript, it's something do to with InfluxDB that might be called flux.
mean = from(bucket: "zigbee")
|> range(start: -5d, stop: now())
|> filter(fn: (r) => r["room"] == "Outside")
|> filter(fn: (r) => r["_measurement"] == "temperature")
|> aggregateWindow(every: 30m, fn: mean, createEmpty: false)
shift = mean
|> timeShift(duration: -3h)
j = join(tables: {mean: mean, shift: shift}, on: ["_time"])
|> map(fn: (r) => ({ r with diff: float(v: r._value_mean) - float( v: r._value_shift) }))
// yield contains 1 table with the required columns, but the UI doesn't understand it.
// The UI requires 1 table for each series.
j |> map(fn: (r) => ({_time: r._time, _value: r._value_mean})) |> yield(name: "mean")
j |> map(fn: (r) => ({_time: r._time, _value: r._value_shift})) |> yield(name: "shift")
j |> map(fn: (r) => ({_time: r._time, _value: r.diff})) |> yield(name: "diff")
The |> in TickScript "Declares a chaining method call which creates an instance of a new node and chains it to the node above it." as said in the official documentation
I have a db of hydrological data.
I wrote a cell in the influxdata panel to find the first occurrence where the measurement is above 17°.
But now I don't want to display the value but the timestamp in the cell when the first occurence was:
from(bucket: "hydroAPI")
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r["_measurement"] == "hydro")
|> filter(fn: (r) => r["_field"] == "temperature")
|> filter(fn: (r) => r["loc"] == "XXXX")
|> aggregateWindow(every: v.windowPeriod, fn: mean, createEmpty: false)
|> filter(fn: (r) => r._value > 17 )
|> first()
|> yield(name: "mean")
This code is working, but it shows just the value. I want to see the time. Is this possible?
The Single Stat visualization will always display what's in the _value column, so you need to replace that with your time.
Try adding this just before your yield():
|> drop(columns: ["_value"])
|> rename(columns: {_time: "_value"})
|> toString()
The first line drops the _value column so that you can rename the _time column in the second line. Since you probably don't want the timestamp in nanoseconds, the toString() on the third line will convert it into a human-readable form.
I see many similar questions but couldn't find a good match.
If we define a query and the result aught to be single value, is there a flux way to store as such? Example:
total = from(bucket: "xxx")
|> range(start: 0)
|> filter(fn: (r) => ...)
|> keep(columns: ["_value"])
|> sum()
consumed = from(bucket: "xxx")
|> range(start: 0)
|> filter(fn: (r) => ...)
|> keep(columns: ["_value"])
|> last()
total - consumed
Results in
invalid: error #18:1-18:40: [A] is not Subtractable
I can think of other ways to solve similar issues, but this example made me question whether flux actually supports easy working with single values or 1x1 relations.
Thanks
Not answering my original question but I want to provide the workaround I went with to solve this. I would still be interested in a more direct solution.
I've introduced a second column, then joined the two tables on that column:
total = from(bucket: "xxx")
|> range(start: 0)
|> filter(fn: (r) => ...)
|> keep(columns: ["_value"])
|> sum()
// Added:
|> map(fn: (r) => ({ age: "latest", _value:r._value }))
consumed = from(bucket: "xxx")
|> range(start: 0)
|> filter(fn: (r) => ...)
|> keep(columns: ["_value"])
|> last()
// Added:
|> map(fn: (r) => ({ age: "latest", _value:r._value }))
join(tables: {total: total, consumed: consumed}, on: ["age"])
|> map(fn: (r) => ({_value: r._value_total - r._value_consumed}))
In the query, total and consumed are tables. For how to extract and use scalar values, please see Extract scalar values in Flux
I am new to flux query language (with Influx DB 2) and cant find a solution for the following problem:
I have data with changing true and false values:
I was able to calculate the time in seconds until the next change by using the events.duration function:
Now I want to calculate the total time and the time of all "false"-events and after that I want to calculate the percentage of all false events. I tryed the following
import "contrib/tomhollingworth/events"
total = from(bucket: "********")
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r["_measurement"] == "********")
|> filter(fn: (r) => r["Server"] == "********")
|> filter(fn: (r) => r["_field"] == "********")
|> filter(fn: (r) => r["DataNode"] == "********")
|> events.duration(
unit: 1s,
columnName: "duration",
timeColumn: "_time",
stopColumn: "_stop"
)
|> sum(column: "duration")
|> yield(name: "total")
downtime = from(bucket: "********")
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r["_measurement"] == "********")
|> filter(fn: (r) => r["Server"] == "********")
|> filter(fn: (r) => r["_field"] == "********")
|> filter(fn: (r) => r["DataNode"] == "********")
|> events.duration(
unit: 1s,
columnName: "duration",
timeColumn: "_time",
stopColumn: "_stop"
)
|> pivot(rowKey:["_time"], columnKey: ["_value"], valueColumn: "duration")
|> drop(columns: ["true"])
|> sum(column: "false")
|> yield(name: "downtime")
downtime_percentage = downtime.false / total.duration
With this I am getting the following error error #44:23-44:31: expected {A with false:B} but found [C]
I also tryed some variations but couldnet get it to work.
I guess I am getting some basic things wrong but I couldnt figure it out yet. Let me know, if you need more information.
I have found a way to solve my problem. Although I am sure that there is a more elegant solution, I document my way here, maybe it helps someone and we can improve it together.
import "contrib/tomhollingworth/events"
//Set time window in seconds (based on selected time)
time_window = int(v: v.timeRangeStart)/-1000000000
//Filter (IoT-)Data
data= from(bucket: "*******")
|> range(start: v.timeRangeStart, stop: v.timeRangeStop)
|> filter(fn: (r) => r["_measurement"] == "*******")
|> filter(fn: (r) => r["Server"] == "*******")
|> filter(fn: (r) => r["Equipment"] == "*******")
|> filter(fn: (r) => r["DataNode"] == "******")
//Use events.duration to calculate the duration in seconds of each true/false event.
|> events.duration(
unit: 1s,
columnName: "duration",
timeColumn: "_time",
stopColumn: "_stop"
)
//Sum up the event times via "sum()" and save them as an array variable via "findColumn()". This is the only way to access the value later (As far as I know. please let me know if you know other ways!).
total_array = data
|> sum(column: "duration")
|> findColumn(
fn: (key) => key._field == "*******",
column: "duration",
)
//Calculate "missing time" in seconds in the time window, because the first event in the time window is missing.
missing_time = time_window - total_array[0]
//Create an array with the first event to determine if it is true or false
first_value_in_window = data
|> first()
|> findColumn(
fn: (key) => key._field == "*******",
column: "_value",
)
//Calculate the downtime by creating columns with the true and false values via pivot. Then sum up the column with the false values
downtime = data
|> map(fn: (r) => ({ r with duration_percentage: float(v: r.duration)/float(v: time_window) }))
|> pivot(rowKey:["_time"], columnKey: ["_value"], valueColumn: "duration_percentage")
|> map( fn: (r) => ({r with
downtime: if exists r.false then
r.false
else
0.0
}))
|> sum(column: "downtime")
//Create an array with the downtime so that this value can be accessed later on
downtime_array = downtime
|> findColumn(
fn: (key) => key._field == "PLS_Antrieb_laeuft",
column: "downtime",
)
//If the first value in the considered time window is true, then the remaining time in the time window (missing_time) was downtime. Write this value in the column "false_percentage_before_window".
//The total downtime is calculated from the previously calculated sum(downtime_array) and, if applicable, the downtime of the remaining time in the time window if the first value is true (first_value_in_window[0])
data
|> map( fn: (r) => ({r with
false_percentage_before_window: if first_value_in_window[0] then
float(v: missing_time)/float(v: time_window)
else
0.0
}))
|> map(fn: (r) => ({ r with _value: (downtime_array[0] + r.false_percentage_before_window) * 100.00 }))
|> first()
|> keep(columns: ["_value"])
|> yield(name: "Total Downtime")
This solution assumes that the true/false events only occur alternately.
I'm struggling with an Influx 2 query in Flux on how to join and map data from two differents sets (tables) into a specific desired output.
My current Flux query is this:
data = from(bucket: "foo")
|> range(start:-1d)
|> filter(fn: (r) => r._measurement == "io")
|> filter(fn: (r) => r["device_id"] == "12345")
|> filter(fn: (r) => r._field == "status_id" )
# count the total points
totals = data
|> count(column: "_value")
|> toFloat()
|> set(key: "_field", value: "total_count")
# calculate the amount of onlines points (e.g. status = '1')
onlines = data
|> filter(fn: (r) => r._value == 1)
|> count(column: "_value")
|> toFloat()
|> set(key: "_field", value: "online_count")
union(tables: [totals, onlines])
This returns as output:
[{'online_count': 58.0}, {'total_count': 60.0}]
I would like to have appended to this output a percentage calculated from this. Something like:
[{'online_count': 58.0}, {'total_count': 60.0}, {'availability': 0.96666667}]
I've tried combining this using .map(), but to no avail:
# It feels like the map() is what I need, but can't find the right
# combination with .join/union(), .map(), .set()., .keep() etc.
union(tables: [totals, onlines])
|> map(fn: (r) => ({ r with percentage_online: r.onlines.online_count / r.totals.total_count * 100 }))
How can I append the (calculated) percentage as new field 'availability' in this Flux query?
Or, alternatively, is there a different Flux query approach to achieve this outcome?
N.B. I am aware of the Calculate percentages with Flux article from the docs, which I can't get working into this specific scenario. But it's close.