influxdb group by from most recent time - influxdb

Using "group by" to bin 5m intervals. The way it works now it will group from 15min to just under 20min and this will be give a 15 timestamp.
Is it possible to group from 20 to just above 15 and give it a 20 min timestamp.
thank you

Unfortunately, no: the time would always be the beginning of each time group.
I mean, you can group like that with ORDER BY time DESC - for some functions (like DIFFERENCE()) that does matter - but the timestamp still be the beginning.
That seems to be a feature of InfluxDB (although I haven't found mention of that in InfluxDB docs).
PS In Kapacitor, you can use the time of the selected point instead of the time group interval beginning - but only for selector functions, not aggregates

Related

How to find the highest temperature recorded on each day

I am working on InfluxDb and have a time series data which records temperature of a location. Here i have to find the max temp and the time stamp for each day.
The task is to:
Find the mean temperature on 1 hour basis.
Then find the max temperature from the above mean for each day.
I have wrote a query but I'am not getting the output as required.
SELECT MAX(mean)
FROM (SELECT mean("value")
FROM "temperature"
WHERE ("location" = 'L1')
GROUP BY time(1h))
GROUP BY time(1d)
I'am getting the output as:
time max
---- ---
2020-01-17T00:00:00Z 573.44
2020-01-16T00:00:00Z 674.44
Here am getting the time stamp as 00:00:00z is there a way to get the exact time i.e if mean temp is 573.44 at 13:00 hour on 2020-01-17, The timestamp should be 2020-01-17T13:00:00Z
Currently, no.
GROUP BY effectively removes per-entry timestamps. It's like all the data for the grouping interval (e.g. for the day) gets dropped into a bucket without their timestamps. The bucket has a single timestamp - the start of the interval.
The result only ever has the timestamp of the start of the GROUP BY interval.
The only way to do this is in the app, not the backend. You might want to experiment; if you only need resolution down to the e.g. nearest 5 minutes, do the query using GROUP BY time(5m). This can be done to shrink the size of data retrieved by the client vs backend processing time.

InfluxDB Continuous Query running on entire time series data

If my interpretation is correct, according to the documentation provided here:InfluxDB Downsampling when we down-sample data using a Continuous Query running every 30 minutes, it runs only for the previous 30 minutes data.
Relevant part of the document:
Use the CREATE CONTINUOUS QUERY statement to generate a CQ:
CREATE CONTINUOUS QUERY "cq_30m" ON "food_data" BEGIN
SELECT mean("website") AS "mean_website",mean("phone") AS "mean_phone"
INTO "a_year"."downsampled_orders"
FROM "orders"
GROUP BY time(30m)
END
That query creates a CQ called cq_30m in the database food_data.
cq_30m tells InfluxDB to calculate the 30-minute average of the two
fields website and phone in the measurement orders and in the DEFAULT
RP two_hours. It also tells InfluxDB to write those results to the
measurement downsampled_orders in the retention policy a_year with the
field keys mean_website and mean_phone. InfluxDB will run this query
every 30 minutes for the previous 30 minutes.
When I create a Continuous Query it actually runs on the entire dataset, and not on the previous 30 minutes. My question is, does this happen only the first time after which it runs on the previous 30 minutes of data instead of the entire dataset?
I understand that the query itself uses GROUP BY time(30m) which means it'll return all data grouped together but does this also hold true for the Continuous Query? If so, should I then include a filter to only process the last 30 minutes of data in the Continuous Query?
What you have described is expected functionality.
Schedule and coverage
Continuous queries operate on real-time data. They use the local server’s timestamp, the GROUP BY time() interval, and InfluxDB database’s preset time boundaries to determine when to execute and what time range to cover in the query.
CQs execute at the same interval as the cq_query’s GROUP BY time() interval, and they run at the start of the InfluxDB database’s preset time boundaries. If the GROUP BY time() interval is one hour, the CQ executes at the start of every hour.
When the CQ executes, it runs a single query for the time range between now() and now() minus the GROUP BY time() interval. If the GROUP BY time() interval is one hour and the current time is 17:00, the query’s time range is between 16:00 and 16:59.999999999.
So it should only process the last 30 minutes.
Its a good point about the first run.
I did manage to find a snippet from an old document
Backfilling Data
In the event that the source time series already has data in it when you create a new downsampled continuous query, InfluxDB will go back in time and calculate the values for all intervals up to the present. The continuous query will then continue running in the background for all current and future intervals.
https://influxdbcom.readthedocs.io/en/latest/content/docs/v0.8/api/continuous_queries/#backfilling-data
Which would explain the behaviour you have found

Offsetting a graph by the first value of the shown time span

I have a graph of an energy meter in Grafana which shows the value of the consumed active energy over the selected time span.
This is a relatively new meter, a few months old, so the highest value it is currently showing is around 1570.3 kWh.
The interval shown in the image above is over the course of 24h, so it starts at 1568.1 kWh.
I want to offset the entire graph by 1568.1 kWh, so that the beginning of the graph is at 0 kWh and the end at 2200 Wh (~ 91 Wh per hour in average over 24 h).
It should always adjust when I change the selected time span, so that I can get a good overview of the daily, weekly or monthly consumption.
How do I archive this?
I read that using something like SELECT integral(derivative(max("in-value"))) ... would do the job, but I didn't get it to work. Also, I believe that just adding a SELECT max("in-value") - first_value_of_timespan("in-value") ... would be more precise and efficient, but such a method first_value_of_timespan does not exist.
The solution is to take the difference between the current interval and the next one (there are many small intervals in the shown time span), and then to do a cumulative_sum over all the differences of the time range.
In the specific case shown in the question the solution would be
SELECT cumulative_sum(difference(max("in-total"))) FROM "le.e6.haus.strom.zähler.hausstrom-solar" WHERE $timeFilter GROUP BY time($__interval) fill(previous)

InfluxDB integral gives high value after missing data

I am storing Amps, Volts and Watts in influxdb in a measurement/table called "Power". The frequency of update is approx every second. I can use the integral function to get power usage (in Amp Hour or Watt Hour) on an hourly basis. This is working very nicely, so I can get a graph of power used each hour over a 24 hour period. My SQL is below.
The issue is if there is a gap in the data then I get a huge spike in the result when it returns. eg if data was missing from 3pm to 5.45 pm, then the 5 pm result shows a huge spike. Reason I can see is there is close to 3 hours gap, so it just calculates the area under the graph and lumps it into the 5 PM value. Can I avoid that?
SELECT INTEGRAL(Watts) FROM Power WHERE time > now() - 24h GROUP BY time(1h)
I had a similar issue with Influx. It turns out that integral() doesn't support fill() as noted by Yuri Lachin in the comments.
Since you're grouping by hours anyway, then the average value of the power (watts) for the hour happens to be equal to the energy consumption for the hour (watt-hours), so you can use the mean() value here and you should get the correct result.
The query I'm using is:
SELECT mean("load_power") AS "load"
FROM "power_readings"
WHERE $timeFilter
GROUP BY time(1h) fill(0)
For daily numbers I can go back to using integral() because I rarely have gaps of data that span multiple days, so there's no filler needed.
Since you can use the fill() function in this query, you can decide what makes the most sense of the various options (see https://docs.influxdata.com/influxdb/v1.7/query_language/data_exploration/#group-by-time-intervals-and-fill)
You need to use fill() in group by section of query (see docs for Influx fill() usage).
In your case fill(none) or fill(0) should do the job.

InfluxDB and Grafana graph using midnight as 0 on Y-axis derivative

I am graphing with Grafana (2.6.0) and I have an InfluxDB (0.10.2) database with the following data in it:
> select * from "WattmeterMainskwh" where time > now() - 5m
name: WattmeterMainskwh
-----------------------
time value
1457579891000000000 15529.322
1457579956000000000 15529.411
1457580011000000000 15529.425
1457580072000000000 15529.460
1457580135000000000 15529.476
...etc...
This data collects my household kilowatt usage as measured by a kWH gauge that steadily increments the usage value across months or years. I cannot easily reset the counter, nor do I wish to do so.
My goal is to create a graph that shows my daily kWH use over 24 hour periods starting at midnight, or at a minimum showing relative kWH over the interval displayed. This type of graph would be useful in many other circumstances as well where I could imagine "errors across the day" or "visitors since opening time" or "BGP resets per calendar week" were useful but the collection counter was not reset to zero upon the reset or turn-over of the time interval. This kind of counting is actually quite common in my experience.
This graph works, but doesn't show me what I'm looking for:
SELECT derivative(mean("value")) FROM "WattmeterMainskwh" WHERE $timeFilter GROUP BY time($interval) fill(null)
That graph just shows the difference between one sample and the previous sample. What I want is a steadily increasing line starting from the left side of the graph and increasing towards the right side of the graph, with zero as the bottom of the Y axis, and the graph starting at zero at the farthest left X value.
This graph works too and shows me the correct curve, but it's off by fifteen thousand or so. So far, it's the closest to what I want but since this is an ever-increasing counter that can't be reset I need to subtract some from the Y axis. Ideally, I'd like to subtract whatever the value was at the previous midnight from each sample to get a relative number based on a day instead of an absolute based on all time.
SELECT sum("value") FROM "WattmeterMainskwh" WHERE $timeFilter GROUP BY time($interval) fill(null)
And here's the graph from that previous statement:
Graph that is off by 15k
This attempt didn't work - I apparently can't take a sum of a derivative group:
SELECT sum(derivative(mean("value"))) FROM "WattmeterMainskwh" WHERE $timeFilter GROUP BY time($interval) fill(null)
This doesn't work, either - I can't perform functions within "derivative":
SELECT derivative(sum("value")-first("value")) FROM "WattmeterMainskwh" WHERE $timeFilter GROUP BY time($interval) fill(null)
Of course, I could just create a new value that had calculations applied to it before I wrote it into InfluxDB, but that seems to me to be a data-redundant and sloppy way to solve this problem, as well as being quite inflexible if I want to look at other intervals on a whim. I'm hoping that there is some way to do this more elegantly within the combination of InfluxDB & Grafana, but I'm just not able to find it with the search terms I've used or the thinking I've put towards interpreting the documentation.
Is this type of graph even possible with InfluxDB/Grafana? As far as I can tell a continuous query is not a solution, and the lack of nested SELECTs makes even the hackish ways of doing this not obvious to me.
BONUS: It would be really great to have the graph show midnight every night as a "zero" location, instead of "zero" being the first point in the displayed interval, so looking at five days of normal data would show five distinct "waves" of increasing daily aggregate energy usage, with the wave Y value going back down to zero at 12:00:01 on each day. But I'll take whatever I can get.
Nested functions have only partial support. However, you can effectively nest functions by chaining Continuous Queries.
Use a CQ to calculate the derivative(mean(value)) and store that in a new measurement foo. Then for your graph you can query select sum(value) from foo.
(I know this answer is quite late, but it might help others. Oh, and please excuse me for all the Dutch in my graphs; I had to keep it in dutch for the highest possible WAF)
You could do what I do for my kWh calculations:
Which results in a simple query like this:
SELECT distinct("kwh_combined") FROM "smartmeter" WHERE $timeFilter GROUP BY time($__interval) fill(linear)
In order to get your total count.. or if you want it in a nice graph like this which shows the number of kWh's used per hour in the bars and the yellow line (I normally run in dark mode, excuse the yellow) which is my current WATT power draw:
This data (or at least your hourly usage in bars) can be retrieved by a query like this:
Which is this exact query (for B):
SELECT spread("kwh_combined") FROM "smartmeter" WHERE $timeFilter GROUP BY time(1h) fill(null)
... where the 'kwh_combined' is (still) my counter just counting up and up.
All this results in me being able to 'query' the InfluxDB for a certain time period, like "last 24 hours" to come up with a nice panel like this: (ignore the encircled prices, that was for a question I posted I just made 10 minutes ago, check my PS)
I hope this helps you or anyone else; it took me some figuring out, but I'm happy to give something back to the community :)
PS: Don't be as stupid as I was and hardcode your electrical and gas prices into your dashboard but store them with your measurements as they could change over time.
I had the same problem (same application even) and solved it here. In your case, the query should be roughly:
SELECT value-value_fill FROM
(SELECT first(value) as value_fill FROM WattmeterMainskwh WHERE time>now()-7d GROUP BY time(1d)),
(SELECT first(value) as value FROM WattmeterMainskwh WHERE time>now()-7d GROUP BY time(1h))
fill(previous)

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