How do you check the uptime of a Phoenix/Elixir/Erlang application? If you do :observer.start() and look at the System tab, you can see the uptime in the Statistics area. But I want to be able to pull that information programmatically and include it in a report. I've figured out where to get most of that data from, but I don't see where it pulls uptime from.
You can use either statistics(runtime):
Returns information about runtime, in milliseconds.
This is the sum of the runtime for all threads in the Erlang runtime
system and can therefore be greater than the wall clock time.
Or statistics(wall_clock):
Returns information about wall clock. wall_clock can be used in the
same manner as runtime, except that real time is measured as opposed
to runtime or CPU time.
In both cases, you need to call them at the beginning of your program in order to reset their timers. When you want to print the time passed just do:
{_, Time1} = statistics(runtime).
Or
{_, Time2} = statistics(wall_clock).
Accordingly, and then you will have the time in Time1 or Time2. For more information take a look at erlang:statistics/1
Note: If you want the total time elapsed since the Erlang VM started you can take the first element from the tuple: {Total_Time, Time_Since_Last_Call} = statistics(wall_clock).
Related
i read about flink`s window assigners over here: https://ci.apache.org/projects/flink/flink-docs-stable/dev/stream/operators/windows.html#window-assigners , but i cant find any solution for my problem.
as part of my project i need a windowing that the timer will start given the first element of the key and will be closed and set ready for processing after X minutes. for example:
first keyA comes at (hh:mm:ss) 00:00:02, i want all keyA will be windowing until 00:01:02, and then the timer of 1 minutes will start again only when keyA will be given as input.
Is it possible to do something like that in flink? is there a workaround?
hope i made it clear enough.
Implementing keyed windows that are aligned with the first event, rather than with the epoch, is quite difficult, in general, which I believe is why this isn't supported by Flink's window API. The problem is that with an out-of-order stream using event time processing, as earlier events arrive you may need to revise your notion of when the window began, and when it should end. For example, if the first keyA arrives at 00:00:02, but then some time later an event with keyA arrives with a timestamp of 00:00:01, now suddenly the window should end at 00:01:01, rather than 00:01:02. And if the out-of-orderness is large compared to the window length, handling this becomes quite complex -- imagine, for example, that the event from 00:00:01 arrives 2 minutes after the event from 00:00:02.
Rather than trying to implement this with the window API, I would use a KeyedProcessFunction. If you only need to support processing time windows, then these concerns about out-of-orderness do not apply, and the solution can be fairly simple. It suffices to keep one object in keyed state, which might be a list holding all of the events in the window, or a counter or other aggregator, depending on what you're trying to accomplish.
When an event arrives, if the state (for this key) is null, then there is no open window for this key. Initialize the state (i.e., create a new, empty list, or set the counter to zero), and create a Timer to fire at the appropriate time. Then regardless of whether the state had been null, add the incoming event to the state (i.e., append it to the list, or increment the counter).
When the timer fires, emit the window's result and reset the state to null.
If, on the other hand, you want to do this with event time windows, first sort the stream and then use the same approach. Note that you won't be able to handle late events, so plan your watermarking accordingly (reducing the likelihood of late events to a manageable level), or go for a more complex implementation.
I have been using Reactor pretty extensively for a while now.
The biggest caveat I have had coming up multiple times is default request sizes / prefetch.
Take this simple code for example:
Mono.fromCallable(System::currentTimeMillis)
.repeat()
.delayElements(Duration.ofSeconds(1))
.take(5)
.doOnNext(n -> log.info(n.toString()))
.blockLast();
To the eye of someone who might have worked with other reactive libraries before, this piece of code
should log the current timestamp every second for five times.
What really happens is that the same timestamp is returned five times, because delayElements doesn't send one request upstream for every elapsed duration, it sends 32 requests upstream by default, replenishing the number of requested elements as they are consumed.
This wouldn't be a problem if the environment variable for overriding the default prefetch wasn't capped to minimum 8.
This means that if I want to write real reactive code like above, I have to set the prefetch to one in every transformation. That sucks.
Is there a better way?
Been rolling around the web and forums, cannot find a resource on this.
What I am to achieve is create an alert for when there is no change in data for a period of time.
We are monitoring openfiles for our webserver/s so this number fluctuates rather often. Noticed that when the number is stagnant it points to an issue on the server. So what we want is if openfile remains X for 2minutes alert us.
I made such an alert through a small succession of things:
I have an exclusive 'alerting dummy board', for all the alerts, since I can only have one alert per graph (grafana version 6.6.0)
I use the following query: avg_over_time(delta(Sensor_Data[1m])[20s:]) - this calculates the 20s average of 'first_value-last_value of 1min interval'
My data gathering program feeds into prometheus and this in turn into grafana -- if this program freezes, it might continue sending the last value to prometheus, and the above query will drop to strictly zero.
so I have an alert which goes off if the above query is within a range (-0.01, 0.01) for a minute (a typical value of the above query with system running is abs(query) > 0.18)
Thus, Grafana sends an alert if the Sensor_Data value does not change within about 2-3 minutes.
If you do use Prometheus and Alert manager, There is a nice function that worked for me.
changes
So using something like this in Alert manager will trigger if no changes for the time interval
changes(metric_name[5m]) = 0
This has worked for me. Make sure you're using a rate or increase function (no change means it will drop to zero) and filter the query like the following:
increase(metric_name) > 0
Then, in Alert Config, set "If no data or all values are null" to "Alerting". That way, when there's no data, the alert will be triggered.
I am trying to figure out the behavior of Prometheus' increase() querying function with process restarts.
When there is a process restart within a 2m interval and I query:
sum(increase(my_metric_total[2m]))
I get a value less than expected.
For example, in a simple experiment I mock:
3 lcm_restarts
1 process restart
2 lcm_restarts
All within a 2 minute interval.
Upon querying:
sum(increase(lcm_restarts[2m]))
I receive a value of ~4.5 when I am expecting 5.
lcm_restarts graph
sum(increase(lcm_restarts[2m])) result
Could someone please explain?
Pretty concise and well-prepared first question here. Please keep this spirit!
When working with counters, functions as rate(), irate() and also increase() are adjusting on resets due to restarts. Other than the name suggests, the increase() function does not calculate the absolute increase in the given time frame but is a different way to write rate(metric[interval]) * number_of_seconds_in_interval. The rate() function takes the first and the last measurement in a series and calculates the per-second increase in the given time. This is the reason why you may observe non-integer increases even if you always increase in full numbers as the measurements are almost never exactly at the start and end of the interval.
For more details about this, please have a look at the prometheus docs for the increase() function. There are also some good hints on what and what not to do when working with counters in the robust perception blog.
Having a look at your label dimensions, I also think that counter resets don't apply to your constructed example. There is one label called reason that changed between the restarts and so created a second time series (not continuing the existing one). Here you are also basically summing up the rates of two different time series increases that (for themselves) both have their extrapolation happening.
So basically there isn't really anything wrong what you are doing, you just shouldn't rely on getting highly precise numbers out of prometheus for your use case.
Prometheus may return unexpected results from increase() function due to the following reasons:
Prometheus may return fractional results from increase() over integer counter because of extrapolation. See this issue for details.
Prometheus may return lower than expected results from increase(m[d]) because it doesn't take into account possible counter increase between the last raw sample just before the specified lookbehind window [d] and the first raw sample inside the lookbehind window [d]. See this article and this comment for details.
Prometheus skips the increase for the first sample in a time series. For example, increase() over the following series of samples would return 1 instead of 11: 10 11 11. See these docs for details.
These issues are going to be fixed according to this design doc. In the mean time it is possible to use other Prometheus-like systems such as VictoriaMetrics, which are free from these issues.
Let's say I have a function which does some work by spawning multiple processes. I want to compare CPU time vs real time taken by this function.
def test do
prev_real = System.monotonic_time(:millisecond)
# Code to complete some task
# Spawn different processes & give each process some task
# Receive result
# Finish task
current_real = System.monotonic_time(:millisecond)
diff_real = current_real - prev_real
IO.puts "Real time " <> to_string(diff_real)
IO.puts "CPU time ?????"
end
How to calculate CPU time required by the given function? I am interested in calculating CPU time/Real time ratio.
If you are just trying to profile your code rather than implement your own profiling framework I would recommend using already existing tools like:
fprof which will give you information about time spent in functions (real and own)
percept which will provide you information about which processes in your system ware working at any given time and on what
xprof which is design to help you find which calls to your function will cause it to take more time (trigger inefficient branch of code).
They take advantage of both erlang:trace to figure out which function is being executed and for how long and erlang:system_profile with runnable_procs to determine which processes are currently running. You might start a function, hit a receive or be preemptive rescheduled and wait without doing any actual work. Combining those two might be complicated, and I would recommend using already existing tools before trying glue together your own.
You could also look into tools like erlgrind and eflame if you are looking for more visual representations of your calls.