Firt, I specify user account 1500, spawrate 1, which would take 25 minutes, then I'd like to decrease 1 per second within 900 seconds. how can I simulate this situation?
Sounds like you may want to use the Load Shape feature of Locust. It lets you define in code what you want the user count and spawn rate to be at any given time.
https://docs.locust.io/en/stable/generating-custom-load-shape.html
Related
Utilizing strictly SwiftUI, I am creating a fitness based application which provides the user their Calories, then breaks down said calories into macronutrients. What I am trying to accomplish is restricting the ability for the user to use the slider to increase the number of a macronutrient unless they reduce one of the other sliders.
For instance, if the user has 2000 Calories, we will say their macronutrient break down is as follows...
300 Carbohydrates
175 Protein
85 Fats
If the user reduces the number of carbohydrates, I want to allow them to increase a slider or sliders of their choice the same amount in which they reduce one of the sliders.
For instance, if the user reduces their Carbohydrates by 30, I want them to be able increase their Protein or Fats by that amount or a combination of both but by no ore the 30.
I have a server that processes packets from different devices. Devices can report in different intervals.
I would like to make a chart showing the distribution of intervals by the count of devices (how many devices are reporting within 5 sec/10 sec/60 sec ...)
Intervals for each device can vary.
Now I'm sending metric with Set using deviceId with tags that represent interval (5 sec, 10 sec, 30 sec, and more) but I'm not sure that it is correct.
What is the best way to realize it?
Set is almost never the right custom metric type to use. It will send a count of the number of unique items per a given tag. The underlying items details will be stripped from the metric, meaning that from one time slice to the next, you will have no idea that actual true number of items over time.
For example
3:00:07-3:00:32 | 5 second bucket:[device1,device4,device7] -> 3 values
3:00:32-3:00:47 | 5 second bucket:[device1,device3] -> 2 values
Your time series to datadog will report 3, and then 2. But because the underlying device info is stripped you have no idea how to combine that 2 and 3 if you to zoom out in time and roll up the numbers to show 1 data point per minute. It could be any number from 3 to 5, but the Datadog backend has no idea. (even though we know that across those 30 seconds there were 4 unique values total)
Plus even if it was accurate somehow, you can't create an alert of it or notify anyone, because you won't know which device is having issues if you see a spike of devices in the 60 second bucket.
So let's go through other metric options.
The only metric types that are ever worth using are usually distributions or gauges, or [counts].
A gauge metric is just a measurement of the latency at a point in time, it's usually good for things like CPU or Memory of a computer, or temperature in a room. Numbers that are impossible to actually collect all dat a points for so you just take measurements every 10 seconds, or every minute, or however often you never to get an idea of the behavior.
A count metric is more exact, it's the number of things that happened. Usually good for number of requests to a server, or number of files processed. Even something like the amount of bytes flowing through something, although that usually is treated like a gauge by most people.
Distributions are good for when you want to create a gauge metric, but you need detailed measurements for every single event that happens. For example a web server is handling hundreds of requests per second and we need to know the latency metrics of that server. It's not possible to send a latency metric for every request as a gauge. Gauges have a built in limit of 1 data point per second (in Datadog). Anything more sent in a 1 second interval gets dropped. But we need stats for every request, so a distribution will summarize the data, it keep a running count, min, max, average, and optionally several percentiles (p50, p75, p99).
I haven't seen many good use cases for metric types outside of those 3. For your scenario, it seems like you would want to be sending a distribution metric for that device interval. So device 1 sends a value of 10.14 and device 3 sends a value of 2.3 and so on.
Then you can use a distribution widget in a dashboard to show the number of devices for each interval bucket.
Of course make sure you tag each metric by the device that is generating the metric.
I am working on a VRPTW and want to minimize the total time (travel time + waiting time) cumulated for all vehicles. So if we have 2 vehicles one that starts at time 0 and returns at time 50 and one that starts at time 25 and returns at time 100, then the objective value would be 50+75=125.
Currently I have implemented the following code:
for i in range(data['num_vehicles']):
routing.AddVariableMinimizedByFinalizer(
time_dimension.CumulVar(routing.End(i)))
However, this seems like it is only minimizing the time we arrive back at the depot.
Also it results in very high waiting times.
How do I implement it correctly in Google OR tools?
This is called the span.
See the SetSpanCostCoefficientForVehicle method for one vehicle.
You can also set it for all vehicles.
I have a question. I send counter to graphite. It increases every time somebody uses endpoint. So, it increases slowly during the day. I want to display on dashboard amount of connections during time (histogram - one bar graph per 5 minutes). For example, i now have smth like this
.
And I want grafana to display changes in time (5 min). It started in 13:31. so i want one bar graph(from 13:31 too 13:36) that will have value 12, next bar grapgh with value 0 and e.t.c (For example, if counter increases by 3, next bar graph will have value 3). I have no ideas, how to do it and will be glad if you help.
For rate of change over time, Have a look at the perSecond function of Graphite.
For actual change (i.e the derivative) for your usecase id lookat the nonNegativeDerivative Function
https://graphite.readthedocs.io/en/latest/functions.html
I used this (as per the example) to calculate Network traffic
I want to be able to rank users based on how quick they have completed each level. I want this to be an overall leaderboard I.e. shortest overall time for all levels.
The problem here is that for each level completed the totally completion time goes up. But I want to ensure that the leaderboard takes that into account so that a user having completed 10 levels will rank more highly than someone with only 1 completed level.
How can I create some kind of score based on this?
Before submitting the time to leader board.
You could perform a modulation on the total time by the number of levels completed, then for each level completed reduce it by a set amount so people who complete all levels with the same average time will score better then people with the same average time but with fewer levels.
My Preferred Method:
Or you could express it with a score value.
level complete = 1,000.
Each level has a set time limit bonus, the longer you take the less bonus u get.
eg
I Complete the level in 102 secs Goal time is 120 secs
I get 1,000 points for completion and 1,500 points for each second
that i beat the Goal time for.
This way i will get 1,000 + (18* 1,500) = 28,000 points
Next guy completes in 100 secs
He Gets 1,000 + (20*1,500) = 31,000 points
I suggest adding a default amount of time to the total for each incomplete level. So, say, if a player beats a new level in 3 minutes, that replaces a 10 minute placeholder time, and they 'save' 7 minutes from the total.
Without that kind of trick, the iPhone has no provision for multi-factor rankings.
Leaderboard scores in GameKit have to be expressed as a single number (see this section of the GameKit Programming Guide), so that won't be possible.
Your best bet would be to just have a completion time leaderboard for people who have completed all the levels, and maybe another leaderboard (or a few) for people who have completed a smaller number of levels.