I have a measurement that has the following fields in InfluxDB. The measurement name is data.
Measurement name: data
Time Device Interface Metric Value
2016-10-11T19:00:00Z device1_name int_name In_bits 10
2016-10-11T19:00:00Z device2_name int_name Out_bits 5
.
.
.
I have created a continuous query as follows:
CREATE CONTINUOUS QUERY "test_query" ON "db_name" BEGIN SELECT sum("value") as Sumin INTO "data.copy" FROM "data" where metric = 'In_bits' GROUP BY time(15m), device END
After creating this, how do I see the results saved in new measurement data.copy? After creating this query, I am not able to find the new measurement that has been created. If I'm doing something wrong, I would like to get more input on this matter. Thanks!
Related
I created a continuous query to downsample readings from temperature sensors in my influxdb to store hourly means for a longer time. There are readings of multiple sensors in one table. Upon executing the query, the sensors ip is missing.
Basic data looks like this:
> SELECT ip,tC FROM ht LIMIT 5
name: ht
time ip tC
---- -- --
1671057540000000000 192.168.0.83 21
1671057570000000000 192.168.0.83 21
1671057750000000000 192.168.0.17 21.38
The continuous query (simplified without CREATE ... END):
SELECT last(ip), mean("tC") AS "mean_temp" INTO "downsampled"."ht_downsampled" FROM "ht" GROUP BY time(1h),ip
The issue is, the value of 'ip' is only a tag, not the value in the table and subsequently is missing in the table the query inserts into:
name: ht
tags: ip=192.168.0.17
time ip mean_temp mean_hum
---- -- --------- --------
1671055200000000000 21.47 42.75
1671058800000000000 21.39428571428571 48.785714285714285
1671062400000000000 21.314999999999998 51.625
Why is last(ip) not producing any value?
Can I get the value from the 'tags' into the table?
Is there a different approach to group data with a constant value?
Could you just try query the ip instead of the last(ip) since you are grouping by the ip in the statement already?
Sample code:
SELECT ip, mean("tC") AS "mean_temp" INTO "downsampled"."ht_downsampled" FROM "ht" GROUP BY time(1h), ip
I'd like to calculate the delta values for a series of measurements stored in an InfluxDB. The values are readings from an electricity meter taken every 5 minutes. The values increase over time. Here is subset of the data to give you an idea (commands shown below are executed in the InfluxDB CLI):
> SELECT "Haushaltstromzaehler - cnt" FROM "myhome_measurements" WHERE time >= '2018-02-02T10:00:00Z' AND time < '2018-02-02T11:00:00Z'
name: myhome_measurements
time Haushaltstromzaehler - cnt
---- --------------------------
2018-02-02T10:00:12.610811904Z 11725.638
2018-02-02T10:05:11.242021888Z 11725.673
2018-02-02T10:10:10.689827072Z 11725.707
2018-02-02T10:15:12.143326976Z 11725.736
2018-02-02T10:20:10.753357056Z 11725.768
2018-02-02T10:25:11.18448512Z 11725.803
2018-02-02T10:30:12.922032896Z 11725.837
2018-02-02T10:35:10.618788096Z 11725.867
2018-02-02T10:40:11.820355072Z 11725.9
2018-02-02T10:45:11.634203904Z 11725.928
2018-02-02T10:50:11.10436096Z 11725.95
2018-02-02T10:55:10.753853952Z 11725.973
Calculating the differences in the InfluxDB CLI is pretty straightforward with the difference() function. This gives me the electricity consumed within the 5 minutes intervals:
> SELECT difference("Haushaltstromzaehler - cnt") FROM "myhome_measurements" WHERE time >= '2018-02-02T10:00:00Z' AND time < '2018-02-02T11:00:00Z'
name: myhome_measurements
time difference
---- ----------
2018-02-02T10:05:11.242021888Z 0.03499999999985448
2018-02-02T10:10:10.689827072Z 0.033999999999650754
2018-02-02T10:15:12.143326976Z 0.02900000000045111
2018-02-02T10:20:10.753357056Z 0.0319999999992433
2018-02-02T10:25:11.18448512Z 0.03499999999985448
2018-02-02T10:30:12.922032896Z 0.033999999999650754
2018-02-02T10:35:10.618788096Z 0.030000000000654836
2018-02-02T10:40:11.820355072Z 0.03299999999944703
2018-02-02T10:45:11.634203904Z 0.028000000000247383
2018-02-02T10:50:11.10436096Z 0.02200000000084401
2018-02-02T10:55:10.753853952Z 0.02299999999922875
Where I struggle is getting this to work in a continuous query. Here is the command I used to setup the continuous query:
CREATE CONTINUOUS QUERY cq_Haushaltstromzaehler_cnt ON myhomedb
BEGIN
SELECT difference(sum("Haushaltstromzaehler - cnt")) AS "delta" INTO "Haushaltstromzaehler_delta" FROM "myhome_measurements" GROUP BY time(1h)
END
Looking in the InfluxDB log file I see that no data is written in the new 'delta' measurement from the continuous query execution:
...finished continuous query cq_Haushaltstromzaehler_cnt, 0 points(s) written...
After much troubleshooting and experimenting I now understand why no data is generated. Setting up a continuous query requires to use the GROUP BY time() statement. This in turn requires to use an aggregate function within the differences() function. The problem now is that the aggregate function returns only one value for the time period specified by GROUP BY time(). Obviously, the differences() function cannot calculate a difference from just one value. Essentially, continuous query executes a command like this:
> SELECT difference(sum("Haushaltstromzaehler - cnt")) FROM "myhome_measurements" WHERE time >= '2018-02-02T10:00:00Z' AND time < '2018-02-02T11:00:00Z' GROUP BY time(1h)
>
I'm now somewhat clueless as to how to make this work and appreciate any advice you might have.
Does it help using the last aggregate function? Not tested this as a cq yet.
Select difference(last(T1_Consumed)) AS T1_Delta, difference(last(T2_Consumed)) AS T2_Delta
from P1Data
where time >= 1551648871000000000 group by time(1h)
DIFFERENCE() would calculate delta from the "aggregated" value taken from previous group, not within current group.
So fill free to use selector function there - since your counters seemed to be cumulative, LAST() should be working well.
I have a quite simple problem to modelize and I don't have experience in Esper, so I may be headed the wrong way so I'd like some insight.
Here's the scenario: I have one stream of events "ParkingEvent", with two types of events "SpotTaken" and "SpotFree". So I have an Esper context both partitioned by id and bordered by a starting event of type "SpotTaken" and an end event of type "SpotFree". The idea is to monitor a parking spot with a sensor and then aggregate data to count the number of times the spot has been taken and also the time occupation.
That's it, no time window or whatsoever, so it seems quite simple but I struggle aggregating data. Here's the code I got so far:
create context ParkingSpotOccupation
context PartionBySource
partition by source from SmartParkingEvent,
context ContextBorders
initiated by SmartParkingEvent(
type = "SpotTaken") as startEvent
terminated by SmartParkingEvent(
type = "SpotFree") as endEvent;
#Name("measurement_occupation")
context ParkingSpotOccupation
insert into CreateMeasurement
select
e.source as source,
"ParkingSpotOccupation" as type,
{
"startDate", min(e.time),
"endDate", max(e.time),
"duration", dateDifferenceInSec(max(e.time), min(e.time))
} as fragments
from
SmartParkingEvent e
output
snapshot when terminated;
I got the same data for min and max so I'm guessing I'm doing somthing wrong.
When I'm using context.ContextBorders.startEvent.time and context.ContextBorders.endEvent.time instead of min and max, the measurement_occupation statement is not triggered.
Given that measurements have already been computed by the EPL that you provided, this counts the number of times the spot has been taken (and freed) and totals up the duration:
select source, count(*), sum(duration) from CreateMeasurement group by source
I have about 200,000 rows of 24 hour data as follows:
I can use the query to create a room node with time, roomtemp, and set temp as properties. Moreover, I can also, define the relationship of each room with its corresponding temperatures.
Now, I need to find:
all rows that show an update/increase/decrease from initial temperature till set temperature for all rooms. e.g. based on above data, I need:
Here I have discarded 5th row data as 16 was repetitive and showed no update(increase or decrease) in temp value. The temperature values continued till it reached set temperature '18'.
I can manually create the temperature states by giving its values one by one, but I am unsure how to MERGE the above requirement into the graph using Cypher.
Can I utilize any other programming language to obtain same results using Neo4j in conjunction?
Do I have to utilize in-graph time-tree for this scenario? Can I retrieve my results without creating a time tree?
Filter temparature by room and date (which can also be a date-node)
Sort by time
Collect into a list
Filter by differences in two subsequent temperatues
Turn list into rows
Here is a query that does this:
MATCH (r:Room)<-[:TEMP]-(t:Temparature)
WHERE t.time STARTS WITH "2016-01-01"
AND t.temp < room.temp ADN t.temp > {initial}
WITH t ORDER by t.time ASC
WITH collect(t) temps
WITH [idx in range(0,size(temps)-2) WHERE temps[idx].temp <> temps[idx+1].temp | temps[idx] ] as filtered
UNWIND filtered as t
RETURN t;
I have a basic Esper query as follows:
#Name("MyTestQuery")
#Description("My First Test Query")
select sum(qty), venue
from MyTestWindow
group by venue
The query seems to duplicate the results of my sum i.e. if I send in a qty of 10 my query will fire multiple times and output:
10, 20, 30, 40
However, if I remove the group by function then it just outputs 10.
Is anyone able to advise why this might happen?
Typically you need to qualify the Stream name (MyTestWindow) with a window, so it is
"from MyTestWindow.win:time(1 sec) ". You need to select an appropriate window type from many Epser offers, depending on your application.
This example:
select sum(qty), venue
from MyTestWindow.win:time_batch(1 sec)
group by venue
having sum(qty) is not null
You can run a simple test of this at http://esper-epl-tryout.appspot.com/epltryout/mainform.html
the best way of doing a group by is to trigger an artificial "event" after sending in all events. this way you can fully control what you want you want to output and not let Esper's engine run in real time.
You might have to use the "distinct" feature in select to avoid duplicates. Esper can sometimes create duplicate events when you aren't using trigger variables, so distinct will allow you to get rid of unwanted events.
You can use win:time_batch to specified time interval in one update and coalesce function to handle the null value
select venue, sum(coalesce(ty, 0))
from MyTestWindow.win:time_batch(1 sec)
group by venue