I have a bunch of IoT sensors that upload second by second data to InfluxDB. Since their network is unreliable, sometimes they do not report data.
I'm trying to figure out how to determine time periods in InfluxDB for which there is no data, and am encountering some wacky behavior with subqueries.
What I've tried so far:
Count the number of points each second, for example:
select count(power)
from energy
where time < '2017-05-14T00:05:10Z'
and time >= '2017-05-14T00:04:30Z'
group by time(1s);
This looks promising, as it returns a result for each second in the interval and the count of data points:
...
1494720297000000000 1
1494720298000000000 1
1494720299000000000 0
1494720300000000000 0
...
Now I want only the time periods where there are 0 points, however when I try this, only time ranges with non-zero numbers of points are reported:
select "points"
from
(select count(power) as "points"
from energy
where time < '2017-05-14T00:05:10Z'
and time >= '2017-05-14T00:04:30Z'
group by time(1s));
Returns:
...
1494720297000000000 1
1494720298000000000 1
No data after 1494720298000000000 is returned, even though the subquery does return rows.
Any help would be appreciated in crafting a query or approach to identify only the areas of time where there is no data.
add fill(none) after your query
Example-select count(power)from energy where time < '2017-05-14T00:05:10Z' and time >= '2017-05-14T00:04:30Z' group by time(1s) fill(none)
Related
I'd like to query InfluxDB using InfluxQL and exclude any rows from 0 to 5 minutes after the hour.
Seems pretty easy to do using the time field (the number of nanoseconds since the epoch) and a little modulus math. But the problem is that any WHERE clause with even the simplest calculation on time returns zero records.
How can I get what I need if I can't perform calculations on time? How can I exclude any rows from 0 to 5 minutes after the hour?
# Returns 10 records
SELECT * FROM "telegraf"."autogen"."processes" WHERE time > 0 LIMIT 10
# Returns 0 records
SELECT * FROM "telegraf"."autogen"."processes" WHERE (time/1) > 0 LIMIT 10
Using InfluxDB: Is there any way to build a time-bucketed report of a field value representing a state that persists over time? Ideally in InfluxQL query language
More specifically as an example: Say a measurement contains points that report changes in the light bulb state (On / Off). They could be 0s and 1s as in the example below, or any other value. For example:
time light
---- -----
2022-03-18T00:00:00Z 1
2022-03-18T01:05:00Z 0
2022-03-18T01:55:00Z 0
2022-03-18T02:30:00Z 1
2022-03-18T04:06:00Z 0
The result should be a listing of intervals indicating if this light was on or off during each time interval (e.g. hours), or what percentage of that time it was on. For the given example, the result if grouping hourly should be:
Hour
Value
2022-03-18 00:00
1.00
2022-03-18 01:00
0.17
2022-03-18 02:00
0.50
2022-03-18 03:00
1.00
2022-03-18 04:00
0.10
Note that:
for 1am bucket, even if the light starts and ends in On state, it was On for only 10 over 60 minutes, so the value is low (10/60)
and more importantly the bucket from 3am to 4am has value "1" as the light was On since the last period, even if there was no change in this time period. This rules out usage of simple aggregation (e.g. MEAN) over a GROUP BY TIME(), as there would not be any way to know if an empty/missing bucket corresponds to an On or Off state as it only depends on the last reported value before that time bucket.
Is there a way to implement it in pure InfluxQL, without retrieving potentially big data sets (points) and iterating through them in a client?
I consider that raw data could be obtained by query:
SELECT "light" FROM "test3" WHERE $timeFilter
Where "test3" is your measurement name and $timeFilter is from... to... time period.
In this case we need to use a subquery which will fill our data, let's consider grouping (resolution) time as 1s:
SELECT last("light") as "filled_light" FROM "test3" WHERE $timeFilter GROUP BY time(1s) fill(previous)
This query gives us 1/0 value every 1s. We will use it as a subquery.
NOTE: You should be informed that this way does not consider if beginning of data period within $timeFilter has been started with light on or off. This way will not provide any data before hour with any value within $timeFilter.
In next step you should use integral() function on data you got from subquery, like this:
SELECT integral("filled_light",1h) from (SELECT last("light") as "filled_light" FROM "test3" WHERE $timeFilter GROUP BY time(1s) fill(previous)) group by time(1h)
This is how it looks on charts:
And how Result data looks in a table:
This is not a perfect way of getting it to work but I hope it resolves your problem.
Given a timeseries of (electricity) marketdata with datapoints every hour, I want to show a Bar Graph with all time / time frame averages for every hour of the data, so that an analyst can easily compare actual prices to all time averages (which hour of the day is most/least expensive).
We have cratedb as backend, which is used in grafana just like a postgres source.
SELECT
extract(HOUR from start_timestamp) as "time",
avg(marketprice) as value
FROM doc.el_marketprices
GROUP BY 1
ORDER BY 1
So my data basically looks like this
time value
23.00 23.19
22.00 25.38
21.00 29.93
20.00 31.45
19.00 34.19
18.00 41.59
17.00 39.38
16.00 35.07
15.00 30.61
14.00 26.14
13.00 25.20
12.00 24.91
11.00 26.98
10.00 28.02
9.00 28.73
8.00 29.57
7.00 31.46
6.00 30.50
5.00 27.75
4.00 20.88
3.00 19.07
2.00 18.07
1.00 19.43
0 21.91
After hours of fiddling around with Bar Graphs, Histogramm Mode, Heatmap Panel und much more, I am just not able to draw a simple Hours-of-the day histogramm with this in Grafana. I would very much appreciate any advice on how to use any panel to get this accomplished.
your query doesn't return correct time series data for the Grafana - time field is not valid timestamp, so don't extract only
hour, but provide full start_timestamp (I hope it is timestamp
data type and value is in UTC)
add WHERE time condition - use Grafana's macro __timeFilter
use Grafana's macro $__timeGroupAlias for hourly groupping
SELECT
$__timeGroupAlias(start_timestamp,1h,0),
avg(marketprice) as value
FROM doc.el_marketprices
WHERE $__timeFilter(start_timestamp)
GROUP BY 1
ORDER BY 1
This will give you data for historic graph with hourly avg values.
Required histogram may be a tricky, but you can try to create metric, which will have extracted hour, e.g.
SELECT
$__timeGroupAlias(start_timestamp,1h,0),
extract(HOUR from start_timestamp) as "metric",
avg(marketprice) as value
FROM doc.el_marketprices
WHERE $__timeFilter(start_timestamp)
GROUP BY 1
ORDER BY 1
And then visualize it as histogram. Remember that Grafana is designated for time series data, so you need proper timestamp (not only extracted hours, eventually you can fake it) otherwise you will have hard time to visualize non time series data in Grafana. This 2nd query may not work properly, but it gives you at least idea.
I am not sure how to best ask this question.. I am looking to select data but with a minimum time interval between the results. For example:
This measurement:
time field
2015-08-18T00:00:00Z 12
2015-08-18T00:00:00Z 1
2015-08-18T00:06:00Z 11
2015-08-18T00:06:00Z 3
2015-08-18T05:54:00Z 2
2015-08-18T06:00:00Z 1
2015-08-18T06:06:00Z 8
2015-08-18T06:12:00Z 7
This Query:
select sum(*) from measurement where field > 0 would return the sum of all of the rows. I would like to be able to specify a minimum interval between results and only match on the first row in a set of closely timed rows. Ex. 8 minute minimum interval would only match these rows (and result in a sum of 22):
time field
2015-08-18T00:00:00Z 12
2015-08-18T05:54:00Z 2
2015-08-18T06:06:00Z 8
Is there a way to get my expected output from influxdb?
The only alternative I can think of is to just return all of the rows without the sum() aggregate function then loop through the results and do lots of time comparisons or date math in my application.
Probably not with InfluxQL.
InfluxQL has a function elapsed which returns the time elapsed between consecutive datapoints https://docs.influxdata.com/influxdb/v1.7/query_language/functions/#elapsed
That's possibly the only function that has something to do with time but I can't think of a way to apply it for what you need.
You may have better luck with the window function of Flux https://v2.docs.influxdata.com/v2.0/query-data/guides/window-aggregate/
I'm not familiar enough to say how, if at all possible.
Doing it in your application may be the way to go.
Using Influx DB v0.9, say I have this simple query:
select count(distinct("id")) FROM "main" WHERE time > now() - 30m and time < now() GROUP BY time(1m)
Which gives results like:
08:00 5
08:01 10
08:02 5
08:03 10
08:04 5
Now I want a query that produces points with an average of those values over 5 minutes. So the points are now 5 minutes apart, instead of 1 minute, but are an average of the 1 minute values. So the above 5 points would be 1 point with a value of the result of (5+10+5+10+5)/5.
This does not produce the results I am after, for clarity, since this is just a count, and I'm after the average.
select count(distinct("id")) FROM "main" WHERE time > now() - 30m and time < now() GROUP BY time(5m)
This doesn't work (gives errors):
select mean(distinct("id")) FROM "main" WHERE time > now() - 30m and time < now() GROUP BY time(5m)
Also doesn't work (gives error):
select mean(count(distinct("id"))) FROM "main" WHERE time > now() - 30m and time < now() GROUP BY time(5m)
In my actual usage "id" is a string (content, not a tag, because count distinct not supported for tags in my version of InfluxDB).
To clarify a few points for readers, in InfluxQL, functions like COUNT() and DISTINCT() can only accept fields, not tags. In addition, while COUNT() supports the nesting of the DISTINCT() function, most nested or sub-functions are not yet supported. In addition, nested queries, subqueries, or stored procedures are not supported.
However, there is a way to address your need using continuous queries, which are a way to automate the processing of data and writing those results back to the database.
First take your original query and make it a continuous query (CQ).
CREATE CONTINUOUS QUERY count_foo ON my_database_name BEGIN
SELECT COUNT(DISTINCT("id")) AS "1m_count" INTO main_1m_count FROM "main" GROUP BY time(1m)
END
There are other options for the CQ, but that basic one will wake up every minute, calculate the COUNT(DISTINCT("id")) for the prior minute, and then store that result in a new measurement, main_1m_count.
Now, you can easily calculate your 5 minute mean COUNT from the pre-calculated 1 minute COUNT results in main_1m_count:
SELECT MEAN("1m_count") FROM main_1m_count WHERE time > now() - 30m GROUP BY time(5m)
(Note that by default, InfluxDB uses epoch 0 and now() as the lower and upper time range boundaries, so it is redundant to include and time < now() in the WHERE clause.)