I am creating a dashboard using metrics in graphite. I have tried consolidatedBy to get all the metrics. The metric values looks correct and are of the range of 1000s.
The graphite query for the same is
consolidateBy(monitors.x.y.z.client_metrics.k.*.*.*.*.*.response_codes.*.count, 'sum')
I want to get the total number of requests, which would be sum of all this series.
So, I tried the sum function but it is giving the sum around 1-12, which is actually less than the actual values.
The query is
sum(consolidateBy(monitors.x.y.z.client_metrics.k.*.*.*.*.*.response_codes.*.count, 'sum'))
This query also gives the same result
sum(monitors.x.y.z.client_metrics.k.*.*.*.*.*.response_codes.*.count)
My questions are:
Why is the sum of series giving point's value less than the actual values ?
I just want to calculate the total number of requests. If there is an easier solution. Can you please specify it.
Related
I want to create a graph which shows the total capacity for each week relative to remaining availability across a series of specific dates. Just now when I attempt this in Power Bi it calculates this correctly for one of the values (remaining availability) but generates a value much higher than expected by manual calculation for the total capacity - instead showing the total for the entire column rather than for each specific date.
Why is Power Bi doing this and how can I solve it?
So far, I have tried generating the graph like this:
(https://i.stack.imgur.com/GV3vk.png)
and as you can see the capacity values are incredibly high they should be 25 days.
The total availability values are correct (ranging from 0 to 5.5 days).
When I create matrices to see the sum breakdown they are correct but it only appears to be that when combined together one of the values changes to the value for the whole column.
If anyone could help me with this issue that would be great! Thanks!
I have a metric that shows the state of a server. The values are integers and if the value is 0 (zero) then the server is stable, else it is unstable. And the graph we have is at a minute level. So, I want to show an aggregated value to know how many hours the server is unstable in the selected time range.
Lets say, if I select "Last 7 days" as the time duration...we have get X hours of instability of server.
And one more thing, I have a line graph (time series graph) that shows the state of server...but, the thing is when I select "Last 24 hours or 48 hours" I am getting the graph at a minute level...when I increase the duration to a quarter I am getting the graph for every 5 min or something like that....I understand it's aggregating the values....but does any body know how the grafana is doing the aggregation ??
I have tried "scaleToSeconds" function and "ConsolidateBy" functions and many more to first get the count of non zero value minutes, but no success.
Any help would be greatly appreciated.
Thanks in advance.
There are a few different ways to tackle this, there are 2 places that aggregation happens in this situation:
When you query for a time range longer than your raw retention interval and whisper returns aggregated data. The aggregation method used here is defined in your carbon aggregation configuration.
When Grafana sends a query to Graphite it passes maxDataPoints=<width of graph in pixels>, and Graphite will perform aggregation to return at most that many points (because you don't have enough pixels to render more points than that). The method used for this consolidation is controlled by the consolidateBy function.
It is possible for both of these to be used in the same query if you eg have a panel that queries 3 days worth of data and you store 2 days at 1-minute and 7 days at 5-minute intervals in whisper then you'd have 72 * 60 / 5 = 864 points from the 5-minute archive in whisper, but if your graph is only 500px wide then at runtime that would be consolidated down to 10-minute intervals and return 432 points.
So, if you want to always have access to the count then you can change your carbon configuration to use sum aggregation for those series (and remove the existing whisper files so new ones are created with the new aggregation config), and pass consolidateBy('sum') in your queries, and you'll always get the sum back for each interval.
That said, you can also address this at query time by multiplying the average back out to get a total (assuming that your whisper aggregation config is using average). The simplest way to do that will be to summarize the data with average into buckets that match the longest aggregation interval you'll be querying, then scale those values by that interval to calculate the total number of minutes. Finally, you'll want to use consolidateBy('sum') so that any runtime consolidation will work properly.
consolidateBy(scale(summarize(my.series, '10min', 'avg'), 60), 'sum')
With all of that said, you may want to consider reporting uptime in terms of percentages rather than raw minutes, in which case you can use the raw averages directly.
When you say the value is zero (0), the server is healthy - what other values are reported while the server is unhealthy/unstable? If you're only reporting zero (healthy) or one (unhealthy), for example, then you could use the sumSeries function to get a count across multiple servers.
Some more information is needed here about the types of values the server is reporting in order to give you a better answer.
Grafana does aggregate - or consolidate - data typically by using the average aggregation function. You can override this using the 'sum' aggregation in the consolidateBy function.
To get a running calculation over time, you would most likely have to use the summarize function (also with the sum aggregation) and define the time period, e.g. 1 hour, 1 day, 1 week, and so on. You could take this a step further by combining this with a time template variable so that as the period grows/shrinks, the summarize period will increase/decrease accordingly.
Because Prometheus topk returns more results than expected, and because https://github.com/prometheus/prometheus/issues/586 requires client-side processing that has not yet been made available via https://github.com/grafana/grafana/issues/7664, I'm trying to pursue a different near-term work-around to my similar problem.
In my particular case most of the metric values that I want to graph will be zero most of the time. Only when they are above zero are they interesting.
I can find ways to write prometheus queries to filter data points based on the value of a label, but I haven't yet been able to find a way to tell prometheus to return time series data points only if the value of the metric meets a certain condition. In my case, I want to filter for a value greater than zero.
Can I add a condition to a prometheus query that filters data points based on the metric value? If so, where can I find an example of the syntax to do that?
If you're confused by brian's answer: The result of filtering with a comparison operator is not a boolean, but the filtered series. E.g.
min(flink_rocksdb_actual_delayed_write_rate > 0)
Will show the minimum value above 0.
In case you actually want a boolean (or rather 0 or 1), use something like
sum (flink_rocksdb_actual_delayed_write_rate >bool 0)
which will give you the greater-than-zero count.
Filtering is done with the comparison operators, for example x > 0.
This can be solved with subqueries:
count_over_time((metric > 0)[5m:10s])
The query above would return the number of metric data points greater than 0 over the last 5 minutes.
This query may return inaccurate results depending on the relation between the second arg in square brackets (aka step for the inner query) and the real interval between raw samples (aka scrape_interval):
If the step exceeds scrape_interval, them some samples may be missing during the calculations. In this case the query will return lower than expected result.
If the step is smaller than the scrape_interval, then some samples may be counted multiple times. In this case the query will return bigger than expected result.
So it is recommended setting the step to scrape_interval in order to get accurate results.
P.S. The issues mentioned above are solved in VictoriaMetrics - Prometheus-like monitoring system I work on. It provides count_gt_over_time() function, which ideally fits this case. For example, the following MetricsQL query returns the exact number of raw samples with values greater than 0 over the last 5 minutes:
count_gt_over_time(metric[5m], 0)
I was trying to create a Prometheus graph on Grafana, but i can't find the function which calculate the average value.
For example , to create a graph for read_latency, the result contain many tags. If there are 3 machine, there will be 3 tag seperately, for machine1, machine2, machine3. Here is a graph(click to show)
Prometheus
I want to combine these three together, so there will be only one tag : machines, and the value is the average of those three.
It seems that Prometheus query function doesn't have something like average(), so I am not sure how to do this.
I used to work on InfluxDB, and the graph can show like (click to show):
influxDB
I think you are searching for the avg() operation. see documentation
Use built-in $__interval variable, where node, name are custom labels (depending on you metrics):
sum(avg_over_time(some_metric[$__interval])) by (node, name)
or fixed value like 1m,1h etc:
sum(avg_over_time(some_metric[1m])) by (node, name)
You can filter using Grafana variables:
sum(avg_over_time(some_metric{cluster=~"$cluster"}[1m])) by (node, name)
Short answer: use avg() function to return the average value across multiple time series. For example, avg(metric) returns the average value for time series with metric name.
Long answer: Prometheus provides two functions for calculating the average:
avg_over_time calculates the average over raw sample stored in the database on the lookbehind window specified in square brackets. The average is calculated independently per each matching time series. For example, avg_over_time(metric[1h]) calculates average values for raw samples over the last hour per each time series with metric name.
avg calculates the average over multiple time series. The average is calculated independently per each point on the graph.
If you need to calculate the average over raw samples across all the time series, which match the given selector, per each time bucket, e.g.:
SELECT
time_bucket('5 minutes', timestamp) AS t,
avg(value)
FROM table
GROUP BY t
Then the following PromQL query must be used:
sum(sum_over_time(metric[$__interval])) / sum(count_over_time(metric[$__interval]))
Do not use avg(avg_over_time(metric[$__interval])), since it returns average of averages, which isn't equal to real average. See this explanation for details.
In my app I have histograms setup for websocket ping times to every country, one histogram per country. In Grafana I have a graph of the average ping time for several countries I'm most interested in via the following query
rate(country_ping_sum{country=~"AU|NZ|CA|GB|US",instance="$instance"}[15m]) / rate(country_ping_count{country=~"AU|NZ|CA|GB|US",instance="$instance"}[15m])
This works perfectly well. I get a graph for each country. Now I want to add to the same graph an average of all the other countries combined into one.
avg(rate(country_ping_sum{country!~"AU|NZ|CA|GB|US",instance="$instance"}[15m]) / rate(country_ping_count{country!~"AU|NZ|CA|GB|US",instance="$instance"}[15m]))
This fails. When I try the query in the Prometheus query in the Prometheus console I get a value of NaN. If I take the same query and remove the avg() function then I get a list of every matching country, some have values and some have NaN. Many of the countries have a rate of 0 for both the sum and the count. Clearly those divisions by 0 are amounting to NaN for those particular countries.
So my question, how can I filter out NaN values before passing to avg()?
You're effectively taking an average of an average, which is generally not correct.
Instead do a sum of each rate, and then divide to get the overall average.