I have captured .silk file.
Is it possible to count min, median, and max number of bytes per flow using SiLK utils?
If yes, could you please point me in the right direction.
Have you tried this? rwstats --overall-stats filename.silk
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!
Sometimes when I run locust for some scenarios 95 percentile value is more than max. As far as I understood 95 percentile means the 95% of requests took lesser time than this.So how can Max value be lesser than 95 percentile? I am I doing something wrong here.
I also found that this only happens when there very less number of requests like 15 or less.
Percentiles are approximated in Locust.
This is done for performance reasons, as calculating an exact percentile would need to consider every sample (and doing this continously for large runs would just not work)
Min, max and average (mean) are accurate though.
And in longer runs (more than those 15 requests) the 95th percentile should not exceed your max.
How to calculate the number of rows, for example, I use the NullFieldSplitter plugin to divide the data into two parts, and I want to calculate the number of rows for each part. How to calculate it? Someone can take a look and help me, thanks.
You can count number of records using the Group By plugin under the Analytics section. After you split the record, you can pass them to the Group By to calculate the number of records for each of the ports using the Count aggregation method.
I have a counter that I am plotting on Grafana.
rate(processed_work_items_total{job="MainWorker"}[1m])
I am not getting the expected numbers in grafana.
What I want is the # of Work Items Processed per minute.
Is my query wrong? or my Unit of Measure in my Y Axis. I currently have it as ops/min and its giving me a super small number.
According to the documentation, rate(processed_work_items_total{job="MainWorker"}[1m]) will calculate the number of work items processed per second, measured over the last one minute (that's the [1m] from your query).
If you want the number of items per minute, simply multiply the above metric with 60.
If you need to calculate per-minute increase rate for a counter metric, then use increase(...[1m]). For example, the following query returns the increase of processed_work_items_total{job="MainWorker"} time series over the last minute:
increase(processed_work_items_total{job="MainWorker"}[1m])
Note that the increase() function in Prometheus may return unexpected results due to the following issues:
It may return fractional results over integer metric because of extrapolation. See this issue for details.
It may miss counter increase between the last raw sample just before the lookbehind window specified in square brackets and the first raw sample inside the lookbehind window.
It may miss the initial counter increase at the beginning of the time series.
These issues are going to be addressed in Prometheus according to this design doc. In the mean time it is possible to use MetricsQL, which is free from these issues.
I have a metric in Prometheus called unifi_devices_wireless_received_bytes_total, it represents the cumulative total amount of bytes a wireless device has received. I'd like to convert this to the download speed in Mbps (or even MBps to start).
I've tried:
rate(unifi_devices_wireless_received_bytes_total[5m])
Which I think is saying: "please give me the rate of bytes received per second", over the last 5 minutes, based on the documentation of rate, here.
But I don't understand what "over the last 5 minutes" means in this context.
In short, how can I determine the Mbps based on this cumulative amount of bytes metric? This is ultimately to display in a Grafana graph.
You want rate(unifi_devices_wireless_received_bytes_total[5m]) / 1000 / 1000
But I don't understand what "over the last 5 minutes" means in this context.
It's the average over the last 5 minutes.
The rate() function returns the average per-second increase rate for the counter passed to it. The average rate is calculated over the lookbehind window passed in square brackets to rate().
For example, rate(unifi_devices_wireless_received_bytes_total[5m]) calculates the average per-second increase rate over the last 5 minutes. It returns lower than expected rate when 100MB of data in transferred in 10 seconds, because it divides those 100MB by 5 minutes and returns the average data transfer speed as 100MB/5minutes = 333KB/s instead of 10MB/s.
Unfortinately, using 10s as a lookbehind window doesn't work as expected - it is likely the rate(unifi_devices_wireless_received_bytes_total[10s]) would return nothing. This is because rate() in Prometheus expects at least two raw samples on the lookbehind window. This means that new samples must be written at least every 5 seconds or more frequently into Prometheus for [10s] lookbehind window. The solution is to use irate() function instead of rate():
irate(unifi_devices_wireless_received_bytes_total[5m])
It is likely this query would return data transfer rate, which is closer to the expected 10MBs if the interval between raw samples (aka scrape_interval) is lower than 10 seconds.
Unfortunately, it isn't recommended to use irate() function in general case, since it tends to return jumpy results when refreshing graphs on big time ranges. Read this article for details.
So the ultimate solution is to use rollup_rate function from VictoriaMetrics - the project I work on. It reliably detects spikes in counter rates by returning the minimum, maximum and average per-second increase rate across all the raw samples on the selected time range.