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K6
executor
getStagesUnscaledMaxTarget

How to use getStagesUnscaledMaxTarget method of executor Package

Best K6 code snippet using executor.getStagesUnscaledMaxTarget

ramping_arrival_rate.go

Source:ramping_arrival_rate.go

...79	maxVUsRange := fmt.Sprintf("maxVUs: %d", et.ScaleInt64(varc.PreAllocatedVUs.Int64))80	if varc.MaxVUs.Int64 > varc.PreAllocatedVUs.Int64 {81		maxVUsRange += fmt.Sprintf("-%d", et.ScaleInt64(varc.MaxVUs.Int64))82	}83	maxUnscaledRate := getStagesUnscaledMaxTarget(varc.StartRate.Int64, varc.Stages)84	maxArrRatePerSec, _ := getArrivalRatePerSec(85		getScaledArrivalRate(et.Segment, maxUnscaledRate, varc.TimeUnit.TimeDuration()),86	).Float64()87	return fmt.Sprintf("Up to %.2f iterations/s for %s over %d stages%s",88		maxArrRatePerSec, sumStagesDuration(varc.Stages),89		len(varc.Stages), varc.getBaseInfo(maxVUsRange))90}91// Validate makes sure all options are configured and valid92func (varc *RampingArrivalRateConfig) Validate() []error {93	errors := varc.BaseConfig.Validate()94	if varc.StartRate.Int64 < 0 {95		errors = append(errors, fmt.Errorf("the startRate value can't be negative"))96	}97	if varc.TimeUnit.TimeDuration() < 0 {98		errors = append(errors, fmt.Errorf("the timeUnit must be more than 0"))99	}100	errors = append(errors, validateStages(varc.Stages)...)101	if !varc.PreAllocatedVUs.Valid {102		errors = append(errors, fmt.Errorf("the number of preAllocatedVUs isn't specified"))103	} else if varc.PreAllocatedVUs.Int64 < 0 {104		errors = append(errors, fmt.Errorf("the number of preAllocatedVUs can't be negative"))105	}106	if !varc.MaxVUs.Valid {107		// TODO: don't change the config while validating108		varc.MaxVUs.Int64 = varc.PreAllocatedVUs.Int64109	} else if varc.MaxVUs.Int64 < varc.PreAllocatedVUs.Int64 {110		errors = append(errors, fmt.Errorf("maxVUs can't be less than preAllocatedVUs"))111	}112	return errors113}114// GetExecutionRequirements returns the number of required VUs to run the115// executor for its whole duration (disregarding any startTime), including the116// maximum waiting time for any iterations to gracefully stop. This is used by117// the execution scheduler in its VU reservation calculations, so it knows how118// many VUs to pre-initialize.119func (varc RampingArrivalRateConfig) GetExecutionRequirements(et *lib.ExecutionTuple) []lib.ExecutionStep {120	return []lib.ExecutionStep{121		{122			TimeOffset:      0,123			PlannedVUs:      uint64(et.ScaleInt64(varc.PreAllocatedVUs.Int64)),124			MaxUnplannedVUs: uint64(et.ScaleInt64(varc.MaxVUs.Int64 - varc.PreAllocatedVUs.Int64)),125		},126		{127			TimeOffset:      sumStagesDuration(varc.Stages) + varc.GracefulStop.TimeDuration(),128			PlannedVUs:      0,129			MaxUnplannedVUs: 0,130		},131	}132}133// NewExecutor creates a new RampingArrivalRate executor134func (varc RampingArrivalRateConfig) NewExecutor(135	es *lib.ExecutionState, logger *logrus.Entry,136) (lib.Executor, error) {137	return &RampingArrivalRate{138		BaseExecutor: NewBaseExecutor(&varc, es, logger),139		config:       varc,140	}, nil141}142// HasWork reports whether there is any work to be done for the given execution segment.143func (varc RampingArrivalRateConfig) HasWork(et *lib.ExecutionTuple) bool {144	return varc.GetMaxVUs(et) > 0145}146// RampingArrivalRate tries to execute a specific number of iterations for a147// specific period.148// TODO: combine with the ConstantArrivalRate?149type RampingArrivalRate struct {150	*BaseExecutor151	config RampingArrivalRateConfig152	et     *lib.ExecutionTuple153}154// Make sure we implement the lib.Executor interface.155var _ lib.Executor = &RampingArrivalRate{}156// Init values needed for the execution157func (varr *RampingArrivalRate) Init(ctx context.Context) error {158	// err should always be nil, because Init() won't be called for executors159	// with no work, as determined by their config's HasWork() method.160	et, err := varr.BaseExecutor.executionState.ExecutionTuple.GetNewExecutionTupleFromValue(varr.config.MaxVUs.Int64)161	varr.et = et162	varr.iterSegIndex = lib.NewSegmentedIndex(et)163	return err //nolint: wrapcheck164}165// cal calculates the  transtitions between stages and gives the next full value produced by the166// stages. In this explanation we are talking about events and in practice those events are starting167// of an iteration, but could really be anything that needs to occur at a constant or linear rate.168//169// The basic idea is that we make a graph with the X axis being time and the Y axis being170// events/s we know that the area of the figure between the graph and the X axis is equal to the171// amount of events done - we multiply time by events per time so we get events ...172// Mathematics :).173//174// Lets look at a simple example - lets say we start with 2 events and the first stage is 5175// seconds to 2 events/s and then we have a second stage for 5 second that goes up to 3 events176// (using small numbers because ... well it is easier :D). This will look something like:177//  ^178// 7|179// 6|180// 5|181// 4|182// 3|       ,-+183// 2|----+-'  |184// 1|    |    |185//  +----+----+---------------------------------->186//  0s   5s   10s187// TODO: bigger and more stages188//189// Now the question is when(where on the graph) does the first event happen? Well in this simple190// case it is easy it will be at 0.5 seconds as we are doing 2 events/s. If we want to know when191// event n will happen we need to calculate n = 2 * x, where x is the time it will happen, so we192// need to calculate x = n/2as we are interested in the time, x.193// So if we just had a constant function for each event n we can calculate n/2 and find out when194// it needs to start.195// As we can see though the graph changes as stages change. But we can calculate how many events196// each stage will have, again it is the area from the start of the stage to it's end and between197// the graph and the X axis. So in this case we know that the first stage will have 10 full events198// in it and no more or less. So we are trying to find out when the 12 event will happen the answer199// will be after the 5th second.200//201// The graph doesn't show this well but we are ramping up linearly (we could possibly add202// other ramping up/down functions later). So at 7.5 seconds for example we should be doing 2.5203// events/s. You could start slicing the graph constantly and in this way to represent the ramping204// up/down as a multiple constant functions, and you will get mostly okayish results. But here is205// where calculus comes into play. Calculus gives us a way of exactly calculate the area for any206// given function and linear ramp up/downs just happen to be pretty easy(actual math prove in207// https://github.com/k6io/k6/issues/1299#issuecomment-575661084).208//209// One tricky last point is what happens if stage only completes 9.8 events? Let's say that the210// first stage above was 4.9 seconds long 2 * 4.9 is 9.8, we have 9 events and .8 of an event, what211// do with do with that? Well the 10th even will happen in the next stage (if any) and will happen212// when the are from the start till time x is 0.2 (instead of 1) as 0.2 + 0.8 is 10. So the 12th for213// example will be when the area is 2.2 as 9.8+2.2. So we just carry this around.214//215// So in the end what calis doing is to get formulas which will tell it when216// a given event n in order will happen. It helps itself by knowing that in a given217// stage will do some given amount (the area of the stage) events and if we past that one we218// know we are not in that stage.219//220// The specific implementation here can only go forward and does incorporate221// the striping algorithm from the lib.ExecutionTuple for additional speed up but this could222// possibly be refactored if need for this arises.223func (varc RampingArrivalRateConfig) cal(et *lib.ExecutionTuple, ch chan<- time.Duration) {224	start, offsets, _ := et.GetStripedOffsets()225	li := -1226	// TODO: move this to a utility function, or directly what GetStripedOffsets uses once we see everywhere we will use it227	next := func() int64 {228		li++229		return offsets[li%len(offsets)]230	}231	defer close(ch) // TODO: maybe this is not a good design - closing a channel we get232	var (233		stageStart                   time.Duration234		timeUnit                     = float64(varc.TimeUnit.Duration)235		doneSoFar, endCount, to, dur float64236		from                         = float64(varc.StartRate.ValueOrZero()) / timeUnit237		// start .. starts at 0 but the algorithm works with area so we need to start from 1 not 0238		i = float64(start + 1)239	)240	for _, stage := range varc.Stages {241		to = float64(stage.Target.ValueOrZero()) / timeUnit242		dur = float64(stage.Duration.Duration)243		if from != to { // ramp up/down244			endCount += dur * ((to-from)/2 + from)245			for ; i <= endCount; i += float64(next()) {246				// TODO: try to twist this in a way to be able to get i (the only changing part)247				// somewhere where it is less in the middle of the equation248				x := (from*dur - noNegativeSqrt(dur*(from*from*dur+2*(i-doneSoFar)*(to-from)))) / (from - to)249				ch <- time.Duration(x) + stageStart250			}251		} else {252			endCount += dur * to253			for ; i <= endCount; i += float64(next()) {254				ch <- time.Duration((i-doneSoFar)/to) + stageStart255			}256		}257		doneSoFar = endCount258		from = to259		stageStart += stage.Duration.TimeDuration()260	}261}262// This is needed because, on some platforms (arm64), sometimes, even though we263// in *reality* don't get negative results due to the nature of how float64 is264// implemented, we get negative values (very close to the 0). This would get an265// sqrt which is *even* smaller and likely will have negligible effects on the266// final result.267//268// TODO: this is probably going to be less necessary if we do some kind of of269// optimization above and the operations with the float64 are more "accurate"270// even on arm platforms.271func noNegativeSqrt(f float64) float64 {272	if !math.Signbit(f) {273		return math.Sqrt(f)274	}275	return 0276}277// Run executes a variable number of iterations per second.278//279// TODO: Split this up and make an independent component that can be reused280// between the constant and ramping arrival rate executors - that way we can281// keep the complexity in one well-architected part (with short methods and few282// lambdas :D), while having both config frontends still be present for maximum283// UX benefits. Basically, keep the progress bars and scheduling (i.e. at what284// time should iteration X begin) different, but keep everyhing else the same.285// This will allow us to implement https://github.com/k6io/k6/issues/1386286// and things like all of the TODOs below in one place only.287//nolint:funlen,cyclop288func (varr RampingArrivalRate) Run(parentCtx context.Context, out chan<- metrics.SampleContainer) (err error) {289	segment := varr.executionState.ExecutionTuple.Segment290	gracefulStop := varr.config.GetGracefulStop()291	duration := sumStagesDuration(varr.config.Stages)292	preAllocatedVUs := varr.config.GetPreAllocatedVUs(varr.executionState.ExecutionTuple)293	maxVUs := varr.config.GetMaxVUs(varr.executionState.ExecutionTuple)294	// TODO: refactor and simplify295	timeUnit := varr.config.TimeUnit.TimeDuration()296	startArrivalRate := getScaledArrivalRate(segment, varr.config.StartRate.Int64, timeUnit)297	maxUnscaledRate := getStagesUnscaledMaxTarget(varr.config.StartRate.Int64, varr.config.Stages)298	maxArrivalRatePerSec, _ := getArrivalRatePerSec(getScaledArrivalRate(segment, maxUnscaledRate, timeUnit)).Float64()299	startTickerPeriod := getTickerPeriod(startArrivalRate)300	// Make sure the log and the progress bar have accurate information301	varr.logger.WithFields(logrus.Fields{302		"maxVUs": maxVUs, "preAllocatedVUs": preAllocatedVUs, "duration": duration, "numStages": len(varr.config.Stages),303		"startTickerPeriod": startTickerPeriod.Duration, "type": varr.config.GetType(),304	}).Debug("Starting executor run...")305	activeVUsWg := &sync.WaitGroup{}306	returnedVUs := make(chan struct{})307	startTime, maxDurationCtx, regDurationCtx, cancel := getDurationContexts(parentCtx, duration, gracefulStop)308	vusPool := newActiveVUPool()309	defer func() {310		// Make sure all VUs aren't executing iterations anymore, for the cancel()311		// below to deactivate them....

helpers.go

Source:helpers.go

...35		result += time.Duration(s.Duration.Duration)36	}37	return38}39func getStagesUnscaledMaxTarget(unscaledStartValue int64, stages []Stage) int64 {40	max := unscaledStartValue41	for _, s := range stages {42		if s.Target.Int64 > max {43			max = s.Target.Int6444		}45	}46	return max47}48// A helper function to avoid code duplication49func validateStages(stages []Stage) []error {50	var errors []error51	if len(stages) == 0 {52		errors = append(errors, fmt.Errorf("at least one stage has to be specified"))53		return errors...

getStagesUnscaledMaxTarget

Using AI Code Generation

1import (2func main() {3	config, err := clientcmd.BuildConfigFromFlags("", filepath.Join(os.Getenv("HOME"), ".kube", "config"))4	if err != nil {5		panic(err.Error())6	}7	clientset, err := kubernetes.NewForConfig(config)8	if err != nil {9		panic(err.Error())10	}11	sparkClientset, err := versioned.NewForConfig(config)12	if err != nil {13		panic(err.Error())14	}15	sparkApp, err := sparkClientset.SparkoperatorV1beta1().SparkApplications("default").Get("spark-pi", v1.GetOptions{})16	if err != nil {17		panic(err.Error())18	}19	pod, err := clientset.CoreV1().Pods("default").Get(driverPodName, v1.GetOptions{})20	if err != nil {21		panic(err.Error())22	}

getStagesUnscaledMaxTarget

Using AI Code Generation

1import (2func main() {3	fmt.Println(executor.GetStagesUnscaledMaxTarget(1))4}5import (6func main() {7	fmt.Println(executor.GetStagesUnscaledMaxTarget(1))8}9import (10func main() {11	fmt.Println(executor.GetStagesUnscaledMaxTarget(1))12}13import (14func main() {15	fmt.Println(executor.GetStagesUnscaledMaxTarget(1))16}17import (18func main() {19	fmt.Println(executor.GetStagesUnscaledMaxTarget(1))20}21import (22func main() {23	fmt.Println(executor.GetStagesUnscaledMaxTarget(1))24}25import (

getStagesUnscaledMaxTarget

Using AI Code Generation

1import (2func main() {3	c := cron.New()4	c.AddFunc("@every 1s", func() {5		fmt.Println("Every second")6	})7	c.AddFunc("@every 1m", func() {8		fmt.Println("Every minute")9	})10	c.AddFunc("@every 1h", func() {11		fmt.Println("Every hour")12	})13	c.AddFunc("@every 1d", func() {14		fmt.Println("Every day")15	})16	c.AddFunc("@every 1w", func() {17		fmt.Println("Every week")18	})19	c.AddFunc("@every 1y", func() {20		fmt.Println("Every year")21	})22	c.AddFunc("@every 1h30m", func() {23		fmt.Println("Every 1h30m")24	})25	c.AddFunc("@every 1h30m10s", func() {26		fmt.Println("Every 1h30m10s")27	})28	c.AddFunc("@every 1h10s", func() {29		fmt.Println("Every 1h10s")30	})31	c.AddFunc("@every 1m10s", func() {32		fmt.Println("Every 1m10s")33	})34	c.AddFunc("@every 1m30s", func() {35		fmt.Println("Every 1m30s")36	})37	c.Start()38	time.Sleep(10 * time.Second)39	c.Stop()40}41import (42func main() {43	t := time.Now()44	fmt.Println(t.Format(time.RFC3339))45	fmt.Println(t.Format(time.RFC1123))46	fmt.Println(t.Format(time.RFC1123Z))47	fmt.Println(t.Format(time.RFC3339Nano))48	fmt.Println(t.Format(time.RFC822))49	fmt.Println(t.Format(time.RFC822Z))50	fmt.Println(t.Format(time.RFC850))51	fmt.Println(t.Format(time.ANSIC))52	fmt.Println(t.Format(time

getStagesUnscaledMaxTarget

Using AI Code Generation

1func main() {2    executor := executor{}3    stages := []stage{4        {1, 2, 3},5        {2, 3, 4},6    }7    fmt.Println(executor.getStagesUnscaledMaxTarget(stages))8}9func main() {10    executor := executor{}11    stages := []stage{12        {1, 2, 3},13        {2, 3, 4},14    }15    fmt.Println(executor.getStagesUnscaledMaxTarget(stages))16}17func main() {18    executor := executor{}19    stages := []stage{20        {1, 2, 3},21        {2, 3, 4},22    }23    fmt.Println(executor.getStagesUnscaledMaxTarget(stages))24}25func main() {26    executor := executor{}27    stages := []stage{28        {1, 2, 3},29        {2, 3, 4},30    }31    fmt.Println(executor.getStagesUnscaledMaxTarget(stages))32}33func main() {34    executor := executor{}35    stages := []stage{36        {1, 2, 3},37        {2, 3, 4},38    }39    fmt.Println(executor.getStagesUnscaledMaxTarget(stages))40}41func main() {42    executor := executor{}43    stages := []stage{44        {1, 2, 3},45        {2, 3, 4},46    }47    fmt.Println(executor.getStagesUnscaledMaxTarget(stages))48}49func main() {50    executor := executor{}51    stages := []stage{52        {1, 2, 3},53        {2, 3, 4},54    }55    fmt.Println(executor

getStagesUnscaledMaxTarget

Using AI Code Generation

1import (2func main() {3	fmt.Println(executor.getStagesUnscaledMaxTarget())4}5import (6func main() {7	fmt.Println(executor.getStagesUnscaledMaxTarget())8}9import (10func main() {11	fmt.Println(executor.getStagesUnscaledMaxTarget())12}13import (14func main() {15	fmt.Println(executor.getStagesUnscaledMaxTarget())16}17import (18func main() {19	fmt.Println(executor.getStagesUnscaledMaxTarget())20}21import (22func main() {23	fmt.Println(executor.getStagesUnscaledMaxTarget())24}25import (

getStagesUnscaledMaxTarget

Using AI Code Generation

1func main() {2    var executor = new(Executor)3    var stages = []Stage{4        Stage{Target: 2, Weight: 1},5        Stage{Target: 2, Weight: 1},6        Stage{Target: 2, Weight: 1},7    }8    fmt.Println(executor.getStagesUnscaledMaxTarget(stages))9}10func main() {11    var executor = new(Executor)12    var stages = []Stage{13        Stage{Target: 1, Weight: 1},14        Stage{Target: 1, Weight: 1},15        Stage{Target: 1, Weight: 1},16    }17    fmt.Println(executor.getStagesUnscaledMaxTarget(stages))18}19func main() {20    var executor = new(Executor)21    var stages = []Stage{22        Stage{Target: 1, Weight: 1},23        Stage{Target: 1, Weight: 1},24        Stage{Target: 1, Weight: 1},25    }26    fmt.Println(executor.getStagesUnscaledMaxTarget(stages))27}28func main() {29    var executor = new(Executor)30    var stages = []Stage{31        Stage{Target: 2, Weight: 1},32        Stage{Target: 2, Weight: 1},33        Stage{Target: 2, Weight: 1},34    }35    fmt.Println(executor.getStagesUnscaledMaxTarget(stages))36}37func main() {38    var executor = new(Executor)39    var stages = []Stage{40        Stage{Target: 1, Weight: 1},41        Stage{Target: 1, Weight: 1},42        Stage{Target: 1, Weight: 1},43    }44    fmt.Println(executor.getStagesUnscaledMaxTarget(stages))45}46func main()

getStagesUnscaledMaxTarget

Using AI Code Generation

1import(2func main(){3	executor.GetStagesUnscaledMaxTarget()4}5import(6type Executor struct{7}8func (e *Executor) GetStagesUnscaledMaxTarget() {9}10import(11type common struct{12}13func (c *common) GetStagesUnscaledMaxTarget() {14}15The correct import path will be:16import(172. The second issue is with the import path of spark-common. The import path is not correct. It should be:18import(193. The third issue is with the import path of spark-common. The import path is not correct. It should be:20import(214. The fourth issue is with the import path of spark-common. The import path is not correct. It should be:22import(235. The fifth issue is with the import path of spark-common. The import path is not correct. It should be:24import(256. The sixth issue is with the import path of spark-common. The import path is not correct. It should be:26import(277. The seventh issue is with the import path of spark-common. The import path is not correct

getStagesUnscaledMaxTarget

Using AI Code Generation

1stagesUnscaledMaxTarget := executor.getStagesUnscaledMaxTarget()2stagesUnscaledMinTarget := executor.getStagesUnscaledMinTarget()3stagesScaledMaxTarget := executor.getStagesScaledMaxTarget()4stagesScaledMinTarget := executor.getStagesScaledMinTarget()5stagesScaledTarget := executor.getStagesScaledTarget()6stagesTarget := executor.getStagesTarget()7stagesTotal := executor.getStagesTotal()8stagesToKill := executor.getStagesToKill()9stagesToScaleDown := executor.getStagesToScaleDown()10stagesToScaleUp := executor.getStagesToScaleUp()

getStagesUnscaledMaxTarget

Using AI Code Generation

1import (2type Executor struct {3}4func main() {5	executor := new(Executor)6	stages := []float64{2.0, 4.0, 1.0, 5.0, 3.0}7	fmt.Println(executor.getStagesUnscaledMaxTarget())8}9func (executor *Executor) getStagesUnscaledMaxTarget() float64 {10	length := len(executor.Stages)11	sort.Float64s(executor.Stages)12	newStages := make([]float64, length)13	for i := length - 1; i >= 0; i-- {14		for j := length - 1; j >= 0; j-- {15		}16	}17	max := float64(0)18	for i := range newStages {19		max = math.Max(max, newStages[i])20	}21}

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