With InfluxQL, the following query shows you the overall series cardinality of the database.
SHOW SERIES CARDINALITY
How can I see the series cardinality of a single measurement? Or even better, all the measurements in a database with their respective cardinalities, as a list?
I am using InfluxDB 1.7.2.
I wasn't expecting it to be that simple but it turns out the query below works.
SHOW SERIES CARDINALITY FROM <some_measurement_name>
I have a SQL query that acts as a data source in my tableau desktop:
SELECT
row_number() over (order by sales) as rn,
article_number,
country,
SUM(sold_items) as si,
SUM(sales) as sales
FROM data.sales
WHERE sales.order_date between '2021-01-01' and '2021-12-31'
GROUP BY 2, 3
On tableau I dragged rn to column and sales to row to generate a bar chart. The following is the output:
I want to convert this into a 0-100% distribution chart so that I can get the following result:
How can I achieve this? Also, I want the user to filter by country level so even if the # of records increase or decrease, the distribution should always be consistent with the filtered data.
You can do this with nested table calcs.
For example, the following uses the Superstore sample data set, and then first computes a running total of SUM(Sales) per day, then converts that to a percent of total. Notice the edit table calc dialog box - applying two back to back calculations in this case.
The x-axis in this example is Order-Date, and in your question, the the x-axis is a percentage somehow - so its not exactly what you requested but still shows that table calcs are an easy way to do these types of operations.
Also, realize you can just connect to the sales table directly, the custom sql isn’t adding any value, and in fact can defeat query optimizations that Tableau normally makes.
The tableau help docs explains table calculations. Pay attention to the discussion on partitioning and addressing.
I have raw data in Tableau that looks like:
Month,Total
2021-08,17
2021-09,34
2021-10,41
2021-11,26
2021-12,6
And by using the following calculation
RUNNING_SUM(
COUNTD(IF [Inserted At]>=[Parameters].[Start Date]
AND [Inserted At]<=[End Date]
THEN [Id] ELSE NULL END
))
/
LOOKUP(RUNNING_SUM(
COUNTD(IF [Inserted At]>=[Parameters].[Start Date]
AND [Inserted At]<=[End Date]
THEN [Id] ELSE NULL END
)),-1)*100-100
I get
Month,My_Calc
2021-08,NULL
2021-09,200
2021-10,80.4
2021-11,28.3
2021-12,5.1
And all I really want is 5.1 (last monthly value) as one big metric (% Month-Over-Month Growth).
How can I accomplish this?
I'm relatively new to Tableau and don't know how to use calculated fields in conjunction with the date groupings aspect to express I want to calculate month-over-month growth. I've tried the native year-over-year growth running total table calculation but that didn't end with the same result since I think my calculation method is different.
First a brief table calc intro, and then the answer at the end.
Most calculations in Tableau are actually performed by the data source (e.g. database server), and the results are then returned to Tableau (i.e. the client) for presentation. This separation of responsibilities allows high performance, even when facing very large data sets.
By contrast, table calculations operate on the table of query results that were returned from the server. They are executed late in the order of operations pipeline. That is why table calcs operate on aggregated data -- i.e. you have to ask for WINDOW_SUM(SUM([Sales)) and not WINDOW_SUM([Sales])
Table calcs give you an opportunity to make final passes of calculations over the query results returned from the data source before presentation to the user. You can for instance calculate a running total or make the visualization layout dynamically depend in part on the contents of the query results. This flexibility comes at a cost, the calculation is only one part of defining a table calc. You also have to specify how to apply the calculation to the table of summary results, known as partitioning and addressing. The Tableau on-line help has a useful definition of partitioning and addressing.
Essentially, table calcs are applied to blocks of summary data at a time, aka vectors or windows. Partitioning is how you tell Tableau how you wish to break up the summary query results into windows for purposes of applying your table calc. Addressing is how you specify the order in which you wish to traverse those partitions. Addressing is important for some table calcs, such as RUNNING_SUM, and unimportant for others, such as WINDOW_SUM.
Besides understanding partitioning and addressing very well, it is also helpful to learn about the functions INDEX(), SIZE(), FIRST(), LAST(), WINDOW_SUM(), LOOKUP() and (eventually) PREVIOUS_VALUE() to really understand table calcs. If you really understand them, you'll be able to implement all of these functions using just two of them as the fundamental ones.
Finally, to partially address your question:
You can use the boolean formula LAST() = 0 to tell if you are at the last value of your partition. If you use that formula as a filter, you can hide all the other values. You'll have to get partitioning and addressing specified correctly. You would essentially be fetching a batch of data from your server, using it in calculations on the client side, but only displaying part of it. This can be a bit brittle depending on which fields are on which shelves, but it can work.
Normally, it is more efficient to use a calculation that can be performed server-side, such as LOD calc, if that allows you to avoid fetching data only for client side calculations. But if the data is already fetched for another purpose, or if the calculation requires table calc features, such as the ability to depend on the order of the values, then table calcs are a good tool.
However you do it, the % month-to-month change from 2021.11 (a value of 26) to the value for 2021.12 (a value of 6) is not 5.1%.
It's (( 6 - 26 ) / 26) * 100 = -76.9 %
OK, starting from scratch, this works for me: ( I don't know how to get exactly the table format I want without using ShowMe and Flip, but it works. Anyone else? )
drag Date to rows, change it to combined Month(Date)
drag sales to column shelf
in showme select TEXT-TABLES
flip rows for columns using tool bar
that gets a table like the one you show above
Drag Sales to color (This is a trick to simply hold it for a minute ),
click the down-arrow on the new SALES pill in the mark card,
select "Add a table calculation",
select Running Total, of SUM, compute using Table(down), but don't close this popup window yet.
click Add Secondary Calculation checkbox at the bottom
select Percent Different From
compute using table down
relative to Previous
Accept your work by closing the popup (x).
NOW, change the new pill in the mark card from color to text
you can see the 5.1% at the bottom. Almost done.
Reformat again by clicking table in ShowMe
and flipping axes.
click the sales column header and hide it
create a new calculated field
label 'rows-from-bottom'
formula = last()
close the popup
drag the new pill rows-from-bottom to the filters shelf
select range 0 to 0
close the popup.
Done.
For the next two weeks you can see the finished workbook here
https://public.tableau.com/app/profile/wade.schuette/viz/month-to-month/hiderows?publish=yes
We are measuring throughput using Grafana and Influx. Of course, we would like to measure throughput in terms how many requests, approximately, happens every single second (rps).
The typical request is:
SELECT sum("count") / 10 FROM "http_requests" GROUP BY time(10s)
But we are loosing possibility to use astonishing dynamic $__interval that very useful when graph scope is large, like a day of week. When we are changing interval we should change divider into SELECT expression.
SELECT sum("count") / $__interval FROM "http_requests" GROUP BY time($__interval)
But this approach does not work, because of empty result returns.
How to create request using dynamic $__interval for throughput measuring?
The reason you get no results is that $__interval is not a number but a string such as 10s, 1m, etc. that is understood by influxdb as a time range. So it is not possible to use it the way you are trying.
However, what you want to calculate is the mean which is available as a function in InfluxQL. The way to get the behavior that you want is with something like this.
SELECT mean("count") FROM "http_requests" GROUP BY time($__interval)
EDIT: On a second thought that is not quite what you want.
You'd probably need to use derivative. I'll come back to you on that one later.
Edit2: Do you think this answers the question that you have Calculating request per second using InfluxDB on Grafana
Edit3: Third edit's a charm.
We use your starting query and wrap it in another one as such:
SELECT sum("rps") from (SELECT sum("count") / 10 as rps FROM "http_requests" GROUP BY time(10s)) GROUP BY time($__interval)
In InfluxDB v1.3, I have a measurement with one field and a tag that can take two values.
I would like to compute (x where mytag=y) - (x where mytag=z), using the last value of each series when needed (something like an http://code.kx.com/wiki/Reference/aj). I would like to do this in one query, if possible.
If the above is not possible, is there a different schema (e.g. using separate measurements) where what I would like to do is feasible? If so, can you please elaborate on the structure and the query?
SELECT difference(mean(x))
FROM <measurement>
WHERE time > now() - 1h and (mytag='y' OR mytag='x')
GROUP BY time(60s), mytag
Functions like difference require an aggregate query (group by time()) as well as an aggregation function for the values within the grouped window (mean above).
Difference then shows the differences between sequential aggregated values for the time period specified, additionally grouped by the two tag values specified.
These can be adjusted depending on your data.