I have a huge script with seven Thread Group. I used Conccurency Thread Group and Throughput Shaping Timer. I have two questions:
Can I use value in Throughput Shaping Timer with dots like, START RPS : 0.01, END RPS: 0.3?
How to use Throughput Shaping Timer in CTG several times? For example:
I have 10 steps. First 5 steps should have RPS from 0.5 to 2 (stepped), steps 6 and 7 RPS from 0.3 to 0.8 and last steps, start RPS from 0.1 to 0.4.
I want to used proportion - my boss want to simulate our production enviroment....
Sorry for my English.
Yes, you can.
No, you cannot, if you need to apply different throughput values to different Samplers you need to put them into different thread groups as if you put everything into one Thread Group - JMeter will apply the lowest throughput to all the requests due to its threads model nature.
If you need to pass some data between different thread groups there are 2 options:
__setProperty() function in first Thread Group to write some value into JMeter Properties and __P() function in second Thread Group to read the value
Inter-Thread Communication Plugin
Related
I am having some trouble understanding the way windowing is implemented internally in Flink and could not find any article which explain this in depth. In my mind, there are two ways this can be done. Consider a simple window wordcount code as below
env.socketTextStream("localhost", 9999)
.flatMap(new Splitter())
.groupBy(0)
.window(Time.of(500, TimeUnit.SECONDS)).sum(1)
Method 1: Store all events for 500 seconds and at the end of the window, process all of them by applying the sum operation on the stored events.
Method 2: We use a counter to store a rolling sum for every window. As each event in a window comes, we do not store the individual events but keep adding 1 to previously stored counter and output the result at the end of the window.
Could someone kindly help to understand which of the above methods (or maybe a different approach) is used by Flink in reality. The reason is, there are pros and cons to both approach and is important to understand in order configure the resources for the cluster correctly.
eg: The Method 1 seems very close to batch processing and might potentially have issues related to spike in processing at every 500 sec interval while sitting idle otherwise etc while Method2 would need to maintain a common counter between all task managers.
sum is a reducing function as mentioned here(https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/operators/windows/#reducefunction). Internally, Flink will apply reduce function to each input element, and simply save the reduced result in ReduceState.
For other windows functions, like windows.apply(WindowFunction). There is no aggregation so all input elements will be saved in the ListState.
This document(https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/operators/windows/#window-functions) about windows stream mentions about how the internal elements are handled in Flink.
I have two operators, a source and a map. The incoming throughput of of the map is stuck at just above 6K messages/s whereas the message count reaches the size of the whole stream (~ 350K) in under 20s (see duration). 350000/20 means that I have a throughput of at least 17500 and not 6000 as flink suggests! What's going on here?
as shown in the picture:
start time = 13:10:29
all messages are already read by = 13:10:46 (less than 20s)
I checked the flink library code and it seems that the numRecordsOutPerSecond statistic (as well as the rest similar ones) operate on a window. This means that they display average throughput but of the last X seconds. It's not the average throughput of the whole execution
My simulation runs two concurrent scenarios, one ramps up to 1000 users and iterates the scenario flow throughout the whole test duration
while the second one injects 500 users every hour for 10 minutes. I am doing this to simulate a peak every hour.
The simulation looks more or less as the following:
setUp(steadyStateScenario.inject(rampConcurrentUsers(1) to (numberOfTestUsers) during (Config.rampUpDuration minutes))
.protocols(httpconf),
peakScenario.inject(nothingFor(60 minutes), rampUsers(numberOfPeakTestUsers) during (Config.PeakRampUpDuration minutes))
.protocols(httpconf))
.maxDuration(Config.scenarioDuration hours)
.assertions(global.successfulRequests.percent.is(100))
My issue with the current simulation is that I am unable to randomise peak duration and number of peak users.
To overcome this I would like to execute peakScenario.inject every hour and change values in numberOfPeakTestUsers and Config.PeakRampUpDuration each time it
is executed. This should be done while steadyStateScenario is running at the background without interruption. Is that possible?
Cheers.
I am trying to shape a JMeter test involving a Concurrency Thread Group and a Throughput Shaping Timer as documented here and here. the timer is configured to run ten ramps and stages with RPS from 1 to 333.
I want to set up the Concurrency Thread Group to use the schedule feedback function and added the formula in the Target concurrency field (I have updated the example from tst-name to the actual timer name). ramp-up time and steps I have set to 1 as I assume the properties are not that important if the throughput is managed by the timer; the Hold Target Rate time is 8000, which is longer than the steps added in the timer (6200).
When I run the test, it ends without any exceptions within 3 seconds or so. The log file shows a few rows about starting and ending threads but nothing alarming.
The only thing I find suspicious is the Log entry "VirtualUserController: Test limit reached, thread is done plus thread name.
I am not getting enough clues from the documentation linked here to figure this out myself, do you have any hints?
According to the documentation rampup time and steps should be blank:
When using this approach, leave Concurrency Thread Group Ramp Up Time and Ramp-Up Steps Count fields blank"
So your assumption that setting them to 1 is OK, seems false...
I have a setup with a Beaglebone Black which communicates over I²C with his slaves every second and reads data from them. Sometimes the I²C readout fails though, and I want to get statistics about these fails.
I would like to implement an algorithm which displays the percentage of successful communications of the last 5 minutes (up to 24 hours) and updates that value constantly. If I would implement that 'normally' with an array where I store success/no success of every second, that would mean a lot of wasted RAM/CPU load for a minor feature (especially if I would like to see the statistics of the last 24 hours).
Does someone know a good way to do that, or can anyone point me in the right direction?
Why don't you just implement a low-pass filter? For every successfull transfer, you push in a 1, for every failed one a 0; the result is a number between 0 and 1. Assuming that your transfers happen periodically, this works well -- and you just have to adjust the cutoff frequency of that filter to your desired "averaging duration".
However, I can't follow your RAM argument: assuming you store one byte representing success or failure per transfer, which you say happens every second, you end up with 86400B per day -- 85KB/day is really negligible.
EDIT Cutoff frequency is something from signal theory and describes the highest or lowest frequency that passes a low or high pass filter.
Implementing a low-pass filter is trivial; something like (pseudocode):
new_val = 1 //init with no failed transfers
alpha = 0.001
while(true):
old_val=new_val
success=do_transfer_and_return_1_on_success_or_0_on_failure()
new_val = alpha * success + (1-alpha) * old_val
That's a single-tap IIR (infinite impulse response) filter; single tap because there's only one alpha and thus, only one number that is stored as state.
EDIT2: the value of alpha defines the behaviour of this filter.
EDIT3: you can use a filter design tool to give you the right alpha; just set your low pass filter's cutoff frequency to something like 0.5/integrationLengthInSamples, select an order of 0 for the IIR and use an elliptic design method (most tools default to butterworth, but 0 order butterworths don't do a thing).
I'd use scipy and convert the resulting (b,a) tuple (a will be 1, here) to the correct form for this feedback form.
UPDATE In light of the comment by the OP 'determine a trend of which devices are failing' I would recommend the geometric average that Marcus Müller ꕺꕺ put forward.
ACCURATE METHOD
The method below is aimed at obtaining 'well defined' statistics for performance over time that are also useful for 'after the fact' analysis.
Notice that geometric average has a 'look back' over recent messages rather than fixed time period.
Maintain a rolling array of 24*60/5 = 288 'prior success rates' (SR[i] with i=-1, -2,...,-288) each representing a 5 minute interval in the preceding 24 hours.
That will consume about 2.5K if the elements are 64-bit doubles.
To 'effect' constant updating use an Estimated 'Current' Success Rate as follows:
ECSR = (t*S/M+(300-t)*SR[-1])/300
Where S and M are the count of errors and messages in the current (partially complete period. SR[-1] is the previous (now complete) bucket.
t is the number of seconds expired of the current bucket.
NB: When you start up you need to use 300*S/M/t.
In essence the approximation assumes the error rate was steady over the preceding 5 - 10 minutes.
To 'effect' a 24 hour look back you can either 'shuffle' the data down (by copy or memcpy()) at the end of each 5 minute interval or implement a 'circular array by keeping track of the current bucket index'.
NB: For many management/diagnostic purposes intervals of 15 minutes are often entirely adequate. You might want to make the 'grain' configurable.