I have a grafana dashboard that charts a bunch of data on 2 query's, this is one below:
SELECT
"time" AS "time",
metric AS metric,
value
FROM
slipstream_volttron
WHERE
$__timeFilter("time") AND
metric ~ 'slipstream_internal/slipstream_hq/.*/SA-F$'
ORDER BY 1,2
And this is the other query:
SELECT
"time" AS "time",
metric AS metric,
value
FROM
slipstream_volttron
WHERE
$__timeFilter("time") AND
metric ~ 'slipstream_internal/slipstream_hq/.*/Discharge Air Flow$'
ORDER BY 1,2
Would anyone know how I could modify this into one SQL expression for a totalization? Instead of 50 different lines on my chart, just one line of all variables added together. The data is air flow readings and I am trying to figure out how to just plot a totalized air flow reading of all data, hopefully that makes sense for anything */Discharge Air Flow and .*/SA-F
I'm guessing your database isn't MSSQL as I don't recognise ~ as a valid comparison operator so my answer is a bit of a guess based on what would work for MSSQL. I think this should give you the results you are looking for:
SELECT
"time" AS "time",
SUM(value)
FROM
slipstream_volttron
WHERE
$__timeFilter("time") AND
(metric ~ 'slipstream_internal/slipstream_hq/.*/Discharge Air Flow$'
OR metric ~ 'slipstream_internal/slipstream_hq/.*/SA-F$')
GROUP BY time
ORDER BY 1
Related
I have timeseries data in a table using Timescaledb.
Data is as follows:
time locationid parameterid unitid value
2022-04-18T10:00:00.000Z "1" "1" "2" 2.2
2022-04-18T10:00:00.000Z "2" "1" "2" 3.0
2022-04-18T09:00:00.000Z "1" "1" "2" 1.2
2022-04-18T09:00:00.000Z "2" "1" "2" 4.0
2022-04-18T08:00:00.000Z "1" "1" "2" 2.6
2022-04-18T08:00:00.000Z "2" "1" "2" 3.1
2022-04-18T07:00:00.000Z "1" "1" "2" 2.1
2022-04-18T07:00:00.000Z "2" "1" "2" 2.7
I have 1000s of rows with time series IOT data that I am putting into graphs using HighCharts.
My question is, is there a way to limit the number of items returned in my results, but not a classic limit. I'd like to return a 256 data groups at all times. So if I had 2,560 rows my query would group by/date trunc / time_bucket every 100 rows, but if I had 512 rows my query would only group every 2 rows so that I am always returning 256 no matter what.
My current query:
SELECT time_bucket('4 hours', time) as "t"
,locationid, avg(timestamp) as "x", avg(value) as "y"
FROM probe_data
WHERE locationid = '${q.locationid}'and parameterid = '${q.parameterid}'
and time > '${q.startDate}' and time < `${q.endDate}`
GROUP BY "t", locationid
ORDER BY "t" DESC;
It seems like I should be able to use my min date and max date to count the number of possible returns and then divide by 256? Is this the best way to do it?
There are a few different ways you can do something like this:
You can just change the time bucket you're using dynamically in your query with time_bucket. You can do arithmetic on intervals and get another interval back ie SELECT (now()- '2022-04-21')/256; will return an interval, this can be the first input into time_bucket. So something like
SELECT time_bucket((enddate - startdate) / 256, time) as "t"
...
GROUP BY time_bucket((enddate - startdate) / 256, time)
Should do what you're looking for to a large extent...
However, it does mean that you're going to be getting averages of arbitrarily larger groups of data as you zoom out and doesn't horribly allow you to cache things or the like and probably isn't actually a great representation of the underlying process.
Another option would be:
You can do an average at a set time_bucket that is relevant to your data analysis and then downsample that using an algorithm like largest triangle three buckets which maintains the visual accuracy of a graph in a useful way while downsampling the data. It's one of the experimental hyperfunctions that we have in TimescaleDB.
This would allow you to also use something like continuous aggregates to downsample the data with a set time_bucket and then get the number of points you need for your graph more quickly using the LTTB algorithm.
So it sort of depends what you're looking for...in some cases using LTTB on its own without doing the average or even using something like ASAP smoothing (another experimental hyperfunction) might be a better way to do what you're looking for and are built-in for this type of work! I think the docs pages have more info on the algorithms and what they're useful for, but both LTTB and ASAP are designed specifically for graphing applications so I thought I'd point them out!
No - SQL doesn't support dynamic grouping. To do what you ask, you'd have to fetch the resultset & check the number of records returned to then run again with your logic.
Effectively, you have a presentation level issue - not a SQL issue.
Probably something with NTILE, not sure if this would work but I'd imagine doing it something like this:
SELECT avg(sub.timestamp), avg(sub.value) FROM (
SELECT
timestamp,
value,
NTILE (256) OVER (
ORDER BY time
) bucket_no
FROM
probe_data
) sub GROUP BY sub.bucket_no;
Where the inner query would have all of your data broken into 256 groups, each group would then have a column bucket_no, and your outer query would group by the bucket_no
EDIT: just realized the mysql tag on this question is probably inaccurate, but I'll leave the answer as it might point you in the right direction for timescaledb.
I am trying to replicate the Google Analyitcs data in Big Query but couldnt do that.
Basically I am using Custom Dimension 40 (user subscription status)
but I am getting wrong numbers in BQ.
Can someone help me on this?
I am using this query but couldn't find it out the exact one.
SELECT
(SELECT value FROM hits.customDimensions where index=40) AS UserStatus,
COUNT(hits.transaction.transactionId) AS Unique_Purchases
FROM
`xxxxxxxxxxxxx.ga_sessions_2020*` AS GA, --new rollup
UNNEST(GA.hits) AS hits
WHERE
(SELECT value FROM hits.customDimensions where index=40) IN ("xx001","xxx002")
GROUP BY 1
I am getting this from big query which is wrong.
I have check out the dates also but dont know why its wrong.
Your question is rather unclear. But because you want something to be unique and numbers are mysteriously not what you want, I would suggest using COUNT(DISTINCT):
COUNT(DISTINCT hits.transaction.transactionId) AS Unique_Purchases
As far as I understand, you imported Google Analytics data into Bigquery and you are trying to group the custom dimension with index 40 and values ("xx001","xxx002") in order to know how many hit transactions were performed in function of these dimension values.
Replicating your scenario and trying to execute the query you posted, I got the following error.
However, I created a query that could help with your use-case. At first, it selects the transactionId and dimension values with the transactionId different from null and with index value equal to 40, then the grouping is done by the dimension value, filtered with values equals to "xx001"&"xxx002".
WITH tx AS (
SELECT
HIT.transaction.transactionId,
CD.value
FROM
`xxxxxxxxxxxxx.ga_sessions_2020*` AS GA,
UNNEST(GA.hits) AS HIT,
UNNEST(HIT.customDimensions) AS CD
WHERE
HIT.transaction.transactionId IS NOT NULL
AND
CD.index = 40
)
SELECT tx.value AS UserStatus, count(tx.transactionId) AS Unique_Purchases
FROM tx
WHERE tx.value IN ("xx001","xx002")
GROUP BY tx.value
For further details about the format and schema of the data that is imported into BigQuery, I found this document.
I have the following MYSQL table:
measuredata:
- ID (bigint)
- timestamp
- entityid
- value (double)
The table contains >1 billion entries. I want to be able to visualize any time-window. The time window can be size of "one day" to "many years". There are measurement values round about every minute in DB.
So the number of entries for a time-window can be quite different. Say from few hundrets to several thousands or millions.
Those values are ment to be visualiuzed in a graphical chart-diagram on a webpage.
If the chart is - lets say - 800px wide, it does not make sense to get thousands of rows from database if time-window is quite big. I cannot show more than 800 values on this chart anyhow.
So, is there a way to reduce the resultset directly on DB-side?
I know "average" and "sum" etc. as aggregate function. But how can I i.e. aggregate 100k rows from a big time-window to lets say 800 final rows?
Just getting those 100k rows and let the chart do the magic is not the preferred option. Transfer-size is one reason why this is not an option.
Isn't there something on DB side I can use?
Something like avg() to shrink X rows to Y averaged rows?
Or a simple magic to just skip every #th row to shrink X to Y?
update:
Although I'm using MySQL right now, I'm not tied to this. If PostgreSQL f.i. provides a feature that could solve the issue, I'm willing to switch DB.
update2:
I maybe found a possible solution: https://mike.depalatis.net/blog/postgres-time-series-database.html
See section "Data aggregation".
The key is not to use a unixtimestamp but a date and "trunc" it, avergage the values and group by the trunc'ed date. Could work for me, but would require a rework of my table structure. Hmm... maybe there's more ... still researching ...
update3:
Inspired by update 2, I came up with this query:
SELECT (`timestamp` - (`timestamp` % 86400)) as aggtimestamp, `entity`, `value` FROM `measuredata` WHERE `entity` = 38 AND timestamp > UNIX_TIMESTAMP('2019-01-25') group by aggtimestamp
Works, but my DB/index/structue seems not really optimized for this: Query for last year took ~75sec (slow test machine) but finally got only a one value per day. This can be combined with avg(value), but this further increases query time... (~82sec). I will see if it's possible to further optimize this. But I now have an idea how "downsampling" data works, especially with aggregation in combination with "group by".
There is probably no efficient way to do this. But, if you want, you can break the rows into equal sized groups and then fetch, say, the first row from each group. Here is one method:
select md.*
from (select md.*,
row_number() over (partition by tile order by timestamp) as seqnum
from (select md.*, ntile(800) over (order by timestamp) as tile
from measuredata md
where . . . -- your filtering conditions here
) md
) md
where seqnum = 1;
How to write this expression in PowerBI
select distinct([date]),Temperature from Device47A8F where Temperature>25
Totally new to PowerBI. Is there any tool that can change the query from sql to PowerBI expression?
I have tried so many type of different type of expressions but getting error, Most of the time I am getting this:
The expression refers to multiple columns. Multiple columns cannot be converted to a scalar value.
Need help, Thanks.
After I posted my answer, wondered if your expected result is get only one date by temperature, In other words, without repeated dates in your result set.
A side note: select distinct([date]),Temperature from Device47A8F where Temperature>25 returns repeated dates since DISTINCT keyword evaluate distinct columns values specified in the SELECT statement, it doesn't return distinct values in a specific column even if you surround it with parenthesis.
Now what brings us here. What I can see in your error is that you are trying to use a table-valued (produces a table with multiple columns) expression in a measure which only accepts scalar-valued (calculate only one value).
Supposing you have a table like this:
Running your SQL query you will get the highlighted in yellow rows:
You can see 01/09/2016 date is repeated. If you want to create a measure you have to define what calculation you want to show for temperature. i.e, average, max or min etc.
In the below expression is being calculated the maximum temperature greater than 25 per date:
MaxTempGreaterThan25 =
CALCULATE ( MAX ( Device47A8F[Temperature] ), Device47A8F[Temperature] > 25 )
In this case the measure MaxTempGreaterThan25 is calculated per date.
If you don't want to produce a measure but a table. In the Power BI Tool bar select Modeling tab and click New Table icon.
Use this expression:
MyTemperatureTable =
FILTER ( Device47A8F, Device47A8F[Temperature] > 25 )
It should produce a new table named MyTemperatureTable like this:
I recommend you learn some basics about DAX, it is pretty different from SQL / T-SQL and there are things you can't do depending on your model and data.
Let me know if this helps.
You probably don't need to write any code if your objective is to show the result in a Power BI visual e.g. a table. Power BI naturally aggregates data if the datatype is numeric (e.g. Temperature).
I would just add a Table visual on a Report page and add the Date and Temperature columns to it. Then in Visualizations / Fields / Values I would click the little down-arrow on the Temperature field and set the Aggregation e.g. Maximum. Then in Visualizations / Fields / Filters I would click the little down-arrow on the Temperature field and set the Filter e.g. is greater than: 25
Hard-coded solutions are unlikely to survive the next question from your users e.g. "but what if I want to see Temperature > 24? Or 20? Or 30?"
Based on the bigquery query reference, currently Quantiles do not allow any kind of grouping by another column. I am mainly interested in getting medians grouped by a certain column. The only work around I see right now is to generate a quantile query per distinct group member where the group member is a condition in the where clause.
For example I use the below query for every distinct row in column-y if I want to get the desired result.
SELECT QUANTILE( <column-x>, 1001)
FROM <table>
WHERE
<column-y> == <each distinct row in column-y>
Does the big query team plan on having some functionality to allow grouping on quantiles in the future?
Is there a better way to get what I am trying to get here?
Thanks
With the recently announced percentile_cont() window function you can get medians.
Look at the example in the announcement blog post:
http://googlecloudplatform.blogspot.com/2013/06/google-bigquery-bigger-faster-smarter-analytics-functions.html
SELECT MAX(median) AS median, room FROM (
SELECT percentile_cont(0.5) OVER (PARTITION BY room ORDER BY data) AS median, room
FROM [io_sensor_data.moscone_io13]
WHERE sensortype='temperature'
)
GROUP BY room
While there are efficient algorithms to compute quantiles they are somewhat memory intensive - trying to do multiple quantile calculations in a single query gets expensive.
There are plans to improve QUANTILES, but I don't know what the timeline is.
Do you need median? Can you filter outliers and do an average of the remainder?
If your per-group size is fixed, you may be able to hack it using combination of order, nest and nth. For instance, if there are 9 distinct values of f2 per value of f1, for median:
select f1,nth(5,f2) within record from (
select f1,nest(f2) f2 from (
select f1, f2 from table
group by f1,f2
order by f2
) group by f1
);
Not sure if the sorted order in subquery is guaranteed to survive the second group, but it worked in a simple test I tried.