Datadog .rollup(sum, 60) vs .rollup(avg, 60) aggregation meaning - aggregate

What does .rollup(sum, 60) and .rollup(avg, 60) mean?
This is my understanding
.rollup(sum, 60) - sum up values every second for 60 seconds.
.rollup(avg, 60) - sum up values every second for 60 seconds and then
divide by 60.
Taking the example in screenshot, a time range of 5 mins produces these data points
.rollup(sum, 60) : 863, 1570, 1470, 819, 988
.rollup(avg, 60) : 215.75, 391.25, 368.5, 204.75, 247
(note: all the avg values produced are a factor of 4, ex: 863/215.75 =
4)
However, as per my understanding, it should have produced these values (863/60s = 14.38)
.rollup(avg, 60) - 14.38, 26.16, 24.5, 13.65, 16.46
What am I missing?
The metric is a counter metric published to datadog
applicationMonitor.counter(metric).increment();

The .rollup() function applies "time aggregation" -- it groups up your values in time buckets and shows you the selected aggregation of those values.
So with a .rollup(sum, 60) for each 60 second period you will see the "sum" of all the data points it contains. With .rollup(avg, 60), for each 60 second period you will see the "avg" of all the data points that it contains.
Sounds like whatever it is that's incrementing the counts has a "flush interval" of 15 seconds, since you seem to consistently have 4 values reported every minute. If your goal is to see "the average count value per 15 seconds over time" then the .rollup(avg, N) may be for you. If your goal is to see the "total count per minute" then .rollup(sum, 60) should get you there (but note, if you expand to a time-range of larger than 300 minutes, you'll see the rollup's time-bucket size grow to larger than 1 minute).

Related

Decay chain simulation - with significantly different time scales

I would like to simulate a decay chain with Python. Normally, (in a loop over all nuclides) one calculates the number of decays per time step and updates the number of mother and daughter nuclei.
My problem is that the decay chain contains half-lives on very different time scales, i.e.
0.0001643 seconds for Po-214 and 307106512477175.9 seconds (= 1600 years) for Ra-226.
Using the same time step for all nuclides seems useless.
Is there a simulation method, preferably in Python, that can be used to handle this case?
Don't use time steps for this. Use event scheduling.
Half lives can be expressed as exponential decay, and the conversion between half life and rate of decay is straightforward. Start with the number of both types of nuclei, and schedule exponential inter-event times to figure out when the next decay of each type will occur. Whichever type has the lower time, decrement the corresponding number of nuclei and schedule the next decay for that type (and if need be, increment the count of whatever it decays into).
This can easily be generalized to multiple distinct event types by using a priority queue ordered by time of occurrence to determine which event will be the next one performed. This is the underlying principle behind discrete event simulation.
Update
This approach works with individual decay events, but we can leverage two important properties when we have exponential inter-event times.
The first is to note that exponentially distributed inter-event times means these are Poisson processes. The superposition property tells us that the union of two independent Poisson processes, each having rate λ, is a Poisson process with rate 2λ. Simple induction shows that if we have n independent Poisson properties with the same rate, their superposition is a Poisson process with rate nλ.
The second property is that the exponential distribution is memoryless. This means that when a Poisson event occurs, we can generate the time to the next event by generating a new exponentially distributed time at the current rate and adding it to the current time.
You haven't provided any information about what you want in the way of output, so I arbitrarily decided to print a report showing the time and the current numbers of nuclides whenever that number was halved. I also printed a report every 10 years, given the long half-life of Po-214.
I converted half-lifes to rates using the link provided at the top of the post, and then to means since that's what
Python numpy's exponential generator is parameterized to use. That's an easy conversion, since means and rates are inverses of each other.
Here's a Python implementation with comments:
from numpy.random import default_rng
from math import log
rng = default_rng()
# This creates a list of entries of quantities that will trigger a report.
# I've chosen to go with successive halvings of the original quantity.
def generate_report_qtys(n0):
report_qty = []
divisor = 2
while divisor < n0:
report_qty.append(n0 // divisor) # append next half-life qty to array
divisor *= 2
return report_qty
seconds_per_year = 365.25 * 24 * 60 * 60
po_214_half_life = 0.0001643 # seconds
ra_226_half_life = 1590 * seconds_per_year
log_2 = log(2)
po_mean = po_214_half_life / log_2 # per-nuclide decay rate for po_214
ra_mean = ra_226_half_life / log_2 # ditto for ra_226
po_n = po_n0 = 1_000_000_000
ra_n = ra_n0 = 1_000_000_000
time = 0.0
# Generate a report when the following sets of half-lifes are reached
po_report_qtys = generate_report_qtys(po_n0)
ra_report_qtys = generate_report_qtys(ra_n0)
# Initialize first event times for each type of event:
# - first entry is polonium next event time
# - second entry is radium next event time
# - third entry is next ten year report time
next_event_time = [
rng.exponential(po_mean / po_n),
rng.exponential(ra_mean / ra_n),
10 * seconds_per_year
]
# Print column labels and initial values
print("time,po_214,ra_226,time_in_years")
print(f"{time},{po_n},{ra_n},{time / seconds_per_year}")
while time < ra_226_half_life:
# Find the index of the next event time. Index tells us the event type.
min_index = next_event_time.index(min(next_event_time))
if min_index == 0:
po_n -= 1 # decrement polonium count
time = next_event_time[0] # update clock to the event time
if po_n > 0:
next_event_time[0] += rng.exponential(po_mean / po_n) # determine next event time for po
else:
next_event_time[0] = float('Inf')
# print report if this is a half-life occurrence
if len(po_report_qtys) > 0 and po_n == po_report_qtys[0]:
po_report_qtys.pop(0) # remove this occurrence from the list
print(f"{time},{po_n},{ra_n},{time / seconds_per_year}")
elif min_index == 1:
# same as above, but for radium
ra_n -= 1
time = next_event_time[1]
if ra_n > 0:
next_event_time[1] += rng.exponential(ra_mean / ra_n)
else:
next_event_time[1] = float('Inf')
if len(ra_report_qtys) > 0 and ra_n == ra_report_qtys[0]:
ra_report_qtys.pop(0)
print(f"{time},{po_n},{ra_n},{time / seconds_per_year}")
else:
# update clock, print ten year report
time = next_event_time[2]
next_event_time[2] += 10 * seconds_per_year
print(f"{time},{po_n},{ra_n},{time / seconds_per_year}")
Run times are proportional to the number of nuclides. Running with a billion of each took 831.28s on my M1 MacBook Pro, versus 2.19s for a million of each. I also ported this to Crystal, a compiled Ruby-like language, which produced comparable results in 32 seconds for a billion of each nuclide. I would recommend using a compiled language if you intend to run larger sized problems, but I will also point out that if you use half-life reporting as I did the results are virtually identical for smaller population sizes but are obtained much more rapidly.
I would also suggest that if you want to use this approach for a more complex model, you should use a priority queue of tuples containing time and type of event to store the set of pending future events rather than a simple list.
Last but not least, here's some sample output:
time,po_214,ra_226,time_in_years
0.0,1000000000,1000000000,0.0
0.0001642985647308265,500000000,1000000000,5.20630734690935e-12
0.0003286071415481526,250000000,1000000000,1.0412931957694901e-11
0.0004929007624958987,125000000,1000000000,1.5619082645571865e-11
0.0006571750701843468,62500000,1000000000,2.082462133319222e-11
0.0008214861652253772,31250000,1000000000,2.6031325741671646e-11
0.0009858208114474198,15625000,1000000000,3.1238776442043114e-11
0.0011502417677631668,7812500,1000000000,3.6448962144243124e-11
0.0013145712145548718,3906250,1000000000,4.165624808460947e-11
0.0014788866075394896,1953125,1000000000,4.686308868670272e-11
0.0016432124609700412,976562,1000000000,5.2070260760325286e-11
0.001807832817519779,488281,1000000000,5.728676507465013e-11
0.001972981254301889,244140,1000000000,6.252000324175124e-11
0.0021372947080755688,122070,1000000000,6.772678239395799e-11
0.002301139510796509,61035,1000000000,7.29187108904514e-11
0.0024642826956509244,30517,1000000000,7.808840645837847e-11
0.0026302282280720344,15258,1000000000,8.33469030620844e-11
0.0027944471221414947,7629,1000000000,8.855068579808016e-11
0.002954014120737834,3814,1000000000,9.3607058861822e-11
0.0031188370035748177,1907,1000000000,9.882998084692174e-11
0.003282466175503322,953,1000000000,1.0401507641592902e-10
0.003457552492113242,476,1000000000,1.0956322699169905e-10
0.003601851131916978,238,1000000000,1.1413577496124477e-10
0.0037747824699194033,119,1000000000,1.1961563838566314e-10
0.0039512825256332275,59,1000000000,1.252085876503038e-10
0.004124330529803301,29,1000000000,1.3069214800248755e-10
0.004337121375518753,14,1000000000,1.3743508300754027e-10
0.004535068261934763,7,1000000000,1.437076413268044e-10
0.004890820999020369,3,1000000000,1.5498076529965425e-10
0.004909065046898487,1,1000000000,1.555588842908994e-10
315576000.0,0,995654793,10.0
631152000.0,0,991322602,20.0
946728000.0,0,987010839,30.0
1262304000.0,0,982711723,40.0
1577880000.0,0,978442651,50.0
1893456000.0,0,974185269,60.0
2209032000.0,0,969948418,70.0
2524608000.0,0,965726762,80.0
2840184000.0,0,961524848,90.0
3155760000.0,0,957342148,100.0
3471336000.0,0,953178898,110.0
3786912000.0,0,949029294,120.0
4102488000.0,0,944898063,130.0
4418064000.0,0,940790494,140.0
4733640000.0,0,936699123,150.0
5049216000.0,0,932622334,160.0
5364792000.0,0,928565676,170.0
5680368000.0,0,924523267,180.0
5995944000.0,0,920499586,190.0
6311520000.0,0,916497996,200.0
6627096000.0,0,912511030,210.0
6942672000.0,0,908543175,220.0
7258248000.0,0,904590364,230.0
7573824000.0,0,900656301,240.0
7889400000.0,0,896738632,250.0
8204976000.0,0,892838664,260.0
8520552000.0,0,888956681,270.0
8836128000.0,0,885084855,280.0
9151704000.0,0,881232862,290.0
9467280000.0,0,877401861,300.0
9782856000.0,0,873581425,310.0
10098432000.0,0,869785364,320.0
10414008000.0,0,866002042,330.0
10729584000.0,0,862234212,340.0
11045160000.0,0,858485627,350.0
11360736000.0,0,854749939,360.0
11676312000.0,0,851032010,370.0
11991888000.0,0,847329028,380.0
12307464000.0,0,843640016,390.0
12623040000.0,0,839968529,400.0
12938616000.0,0,836314000,410.0
13254192000.0,0,832673999,420.0
13569768000.0,0,829054753,430.0
13885344000.0,0,825450233,440.0
14200920000.0,0,821859757,450.0
14516496000.0,0,818284787,460.0
14832072000.0,0,814727148,470.0
15147648000.0,0,811184419,480.0
15463224000.0,0,807655470,490.0
15778800000.0,0,804139970,500.0
16094376000.0,0,800643280,510.0
16409952000.0,0,797159389,520.0
16725528000.0,0,793692735,530.0
17041104000.0,0,790239221,540.0
17356680000.0,0,786802135,550.0
17672256000.0,0,783380326,560.0
17987832000.0,0,779970864,570.0
18303408000.0,0,776576174,580.0
18618984000.0,0,773197955,590.0
18934560000.0,0,769836170,600.0
19250136000.0,0,766488931,610.0
19565712000.0,0,763154778,620.0
19881288000.0,0,759831742,630.0
20196864000.0,0,756528400,640.0
20512440000.0,0,753237814,650.0
20828016000.0,0,749961747,660.0
21143592000.0,0,746699940,670.0
21459168000.0,0,743450395,680.0
21774744000.0,0,740219531,690.0
22090320000.0,0,736999181,700.0
22405896000.0,0,733793266,710.0
22721472000.0,0,730602000,720.0
23037048000.0,0,727427544,730.0
23352624000.0,0,724260327,740.0
23668200000.0,0,721110260,750.0
23983776000.0,0,717973915,760.0
24299352000.0,0,714851218,770.0
24614928000.0,0,711740161,780.0
24930504000.0,0,708645945,790.0
25246080000.0,0,705559170,800.0
25561656000.0,0,702490991,810.0
25877232000.0,0,699436919,820.0
26192808000.0,0,696394898,830.0
26508384000.0,0,693364883,840.0
26823960000.0,0,690348242,850.0
27139536000.0,0,687345934,860.0
27455112000.0,0,684354989,870.0
27770688000.0,0,681379178,880.0
28086264000.0,0,678414567,890.0
28401840000.0,0,675461363,900.0
28717416000.0,0,672522494,910.0
29032992000.0,0,669598412,920.0
29348568000.0,0,666687807,930.0
29664144000.0,0,663787671,940.0
29979720000.0,0,660901676,950.0
30295296000.0,0,658027332,960.0
30610872000.0,0,655164886,970.0
30926448000.0,0,652315268,980.0
31242024000.0,0,649481821,990.0
31557600000.0,0,646656096,1000.0
31873176000.0,0,643841377,1010.0
32188752000.0,0,641041609,1020.0
32504328000.0,0,638253759,1030.0
32819904000.0,0,635479981,1040.0
33135480000.0,0,632713706,1050.0
33451056000.0,0,629962868,1060.0
33766632000.0,0,627223350,1070.0
34082208000.0,0,624494821,1080.0
34397784000.0,0,621778045,1090.0
34713360000.0,0,619076414,1100.0
35028936000.0,0,616384399,1110.0
35344512000.0,0,613702920,1120.0
35660088000.0,0,611035112,1130.0
35975664000.0,0,608376650,1140.0
36291240000.0,0,605729994,1150.0
36606816000.0,0,603093946,1160.0
36922392000.0,0,600469403,1170.0
37237968000.0,0,597854872,1180.0
37553544000.0,0,595254881,1190.0
37869120000.0,0,592663681,1200.0
38184696000.0,0,590085028,1210.0
38500272000.0,0,587517782,1220.0
38815848000.0,0,584961743,1230.0
39131424000.0,0,582420312,1240.0
39447000000.0,0,579886455,1250.0
39762576000.0,0,577362514,1260.0
40078152000.0,0,574849251,1270.0
40393728000.0,0,572346625,1280.0
40709304000.0,0,569856166,1290.0
41024880000.0,0,567377753,1300.0
41340456000.0,0,564908008,1310.0
41656032000.0,0,562450828,1320.0
41971608000.0,0,560005832,1330.0
42287184000.0,0,557570018,1340.0
42602760000.0,0,555143734,1350.0
42918336000.0,0,552729893,1360.0
43233912000.0,0,550326162,1370.0
43549488000.0,0,547932312,1380.0
43865064000.0,0,545550017,1390.0
44180640000.0,0,543178924,1400.0
44496216000.0,0,540814950,1410.0
44811792000.0,0,538462704,1420.0
45127368000.0,0,536123339,1430.0
45442944000.0,0,533792776,1440.0
45758520000.0,0,531469163,1450.0
46074096000.0,0,529157093,1460.0
46389672000.0,0,526854383,1470.0
46705248000.0,0,524564196,1480.0
47020824000.0,0,522282564,1490.0
47336400000.0,0,520011985,1500.0
47651976000.0,0,517751635,1510.0
47967552000.0,0,515499791,1520.0
48283128000.0,0,513257373,1530.0
48598704000.0,0,511022885,1540.0
48914280000.0,0,508798440,1550.0
49229856000.0,0,506582663,1560.0
49545432000.0,0,504379227,1570.0
49861008000.0,0,502186693,1580.0
50176584000.0,0,500000869,1590.0
Expanded for More than 2 Nuclides
I mentioned that for more than a couple of nuclides you'd want to use a priority queue to track which decays occur next. I reorganized the code around functions, but that allowed greater flexibility in expanding the scope of the problem. Here you go:
#!/usr/bin/env python3
from numpy.random import default_rng
from math import log
import heapq
SECONDS_PER_YEAR = 365.25 * 24 * 60 * 60
LOG_2 = log(2)
rng = default_rng()
def generate_report_qtys(n0):
report_qty = []
divisor = 2
while divisor < n0:
report_qty.append(n0 // divisor) # append next half-life qty to array
divisor *= 2
return report_qty
po_n0 = 10_000_000
ra_n0 = 10_000_000
mu_n0 = 10_000_000
# mean is half-life / LOG_2
properties = dict(
po_214 = dict(
mean = 0.0001643 / LOG_2,
qty = po_n0,
report_qtys = generate_report_qtys(po_n0)
),
ra_226 = dict(
mean = 1590 * SECONDS_PER_YEAR / LOG_2,
qty = ra_n0,
report_qtys = generate_report_qtys(ra_n0)
),
made_up = dict(
mean = 75 * SECONDS_PER_YEAR / LOG_2,
qty = mu_n0,
report_qtys = generate_report_qtys(mu_n0)
)
)
nuclide_names = [name for name in properties.keys()]
def population_mean(nuclide):
return properties[nuclide]['mean'] / properties[nuclide]['qty']
def report(): # isolate as single point of maintenance even though it's a one-liner
nuc_qtys = [str(properties[nuclide]['qty']) for nuclide in nuclide_names]
print(f"{time},{time / SECONDS_PER_YEAR}," + ','.join(nuc_qtys))
def decay_event(nuclide):
properties[nuclide]['qty'] -= 1
current_qty = properties[nuclide]['qty']
if current_qty > 0:
heapq.heappush(event_q, (time + rng.exponential(population_mean(nuclide)), nuclide))
rep_qty = properties[nuclide]['report_qtys']
if len(rep_qty) > 0 and current_qty == rep_qty[0]:
rep_qty.pop(0) # remove this occurrence from the list
report()
def report_event():
heapq.heappush(event_q, (time + 10 * SECONDS_PER_YEAR, 'report_event'))
report()
event_q = [(rng.exponential(population_mean(nuclide)), nuclide) for nuclide in nuclide_names]
event_q.append((0.0, "report_event"))
heapq.heapify(event_q)
time = 0.0 # simulated time
print("time(seconds),time(years)," + ','.join(nuclide_names)) # column labels
while time < 1600 * SECONDS_PER_YEAR:
time, event_id = heapq.heappop(event_q)
if event_id == 'report_event':
report_event()
else:
decay_event(event_id)
To add more nuclides, add more entries to the properties dictionary, following the template of the current entries.

Coldfusion calculate seconds in days, hours, min

I want to convert seconds to days, hours and minutes
Currently, it works just for hours and minutes but not for days. Can you please support me tell me what I did wrong:
<cfscript>
seconds = '87400';
midnight = CreateTime(0,0,0);
time = DateAdd("s", seconds, variables.midnight);
date= xxxxxxxxxxxxxxxxxxxxx???
</cfscript>
<cfoutput>
#DateFormat(variables.date, 'd')# not working
#TimeFormat(variables.time, 'HH:mm')#
</cfoutput>
For the value 87400 the expected result is
1 Days, 0 hours, 16 minutes
If I take 94152 seconds it will be:
1 days, 3 hours, 22 minutes
The only issue i have is to get the correct days ... hours and minutes are diplayed but not the correct days
thank you for all the support
A simple way to calculate the intervals is by taking advantage of the modulus operator:
totalSeconds = 94152;
days = int(totalSeconds / 86400);
hours = totalSeconds / 3600 % 24;
minutes = totalSeconds / 60 % 60;
seconds = totalSeconds % 60;
For 94152 seconds, the results would be:
Interval
Value
DAYS
1
HOURS
2
MINUTES
9
SECONDS
12
TOTALSECONDS
94152
demo trycf.com
I understand from your question that you don't need to get a certain date and time along a timeline, but convert a total amount of seconds in days, hours and minutes. To do that you don't necessary need to use cfml time and date functions like CreateTime() or DateAdd(). You just may need these in order to get a reference point of time or date along a timeline, which doesn't seem to be the case, otherwise you would know the value of your starting date variable. Thus, you can solve this with plain rule of three. There may be simpler methods, so I'm posting an alternative only.
We know that:
60 seconds is equivalent to 1 minute
60 minutes is equivalent to 1 hour
24 hours is equivalent to 1 day
Thus, your calcualtion within cfml could be like so:
<cfscript>
//Constants for calculation
secondsPerDay= 60*60*24;
secondsPerHour= 60*60;
secondsPerMinute= 60;
//Seconds to convert
secondsTotal=87400;
// temp variable
secondsRemain= secondsTotal;
days= int( secondsRemain / secondsPerDay);
secondsRemain= secondsRemain - days * secondsPerDay;
hours= int( secondsRemain / secondsPerHour);
secondsRemain= secondsRemain - hours * secondsPerHour;
minutes= int( secondsRemain / secondsPerMinute);
secondsRemain= secondsRemain - minutes * secondsPerMinute;
writeoutput( "#secondsTotal# seconds are: #days# days, #hours# hours, #minutes# minutes and #secondsRemain# seconds." );
</cfscript>
That outputs:
87400 seconds are: 1 days, 0 hours, 16 minutes and 40 seconds.

how to reduce wait time in perl

I have script that queries a database a variable number of times per second.
For example, to achieve 36,000 queries per hour we input 600 queries per minute into our script. 600 x 60 = 36,000
This is the output we get you can see the delay between each query
{1} [2019-11-06 21:38:01.313]
{1} [2019-11-06 21:38:01.413]
{1} [2019-11-06 21:38:01.513]
{1} [2019-11-06 21:38:01.613]
{1} [2019-11-06 21:38:01.713]
{1} [2019-11-06 21:38:01.813]
My problem is we are missing out on that 0.0100 because we have a wait time inplace.
rates per minute = varies we can change this to a max of 960 queries per min but we would want fourmla that is flexible for 0-960.
my $wait_time = (1 / $rpm) * 60 * 1(connection); (max of 4 connections) wait time increases based on number of connections
Does anyone know how to reduce the wait time in between queries ?
thanks
This is the code line
my $wait_time = (1 / $rpm) * 60 * 1;
So when i enter in 600 queries per min
This code line calcuates the wait time based on number of connection we have
my $wait_time = (1 / 600) * 60 * 1;
1/600 * 60 * 1 = WAIT: 0.1
Well, your processing of the query needs time. A fragile solution, if i interpret your problem right, is to measure the time the current processing takes and substract that from the next sleep time. Of course that would break if the processing time equals or exceeds the sleep time.
A clean solution would be to have a dedicated main loop that does nothing but sleeping and firing off queries in separate threads.
I'm not sure if this will help because I have a very hard time understanding your question. I think you are concerned that you aren't making queries at the rate you desire.
It could be because you think of the wait time as being static. It's not the wait time that's static —that's dependent on how long the previous query took— it's the interval that's static.
use Time::HiRes qw( time sleep ); # Add support for fractional times.
my $interval = (1 / $qpm) * 60 * 1; # In (fractional) seconds.
my $next_run = time;
while (1) {
my $wait = $next_run - time;
sleep($wait) if $wait > 0;
$next_run += $interval;
... do work ...
}

Tableau - How to calculate date/time difference and result in full date/time?

I'm trying to calculate a difference between connection time and disconnected time. See image below. But DATEPART formula that I'm using only allows me to use one parameter (hour, minute, second,...)
However, as in the image, I have an ID where disconnection at 3/1/17 2:35:22PM and connection back at 3/2/17 1:59:38 PM
Ideal Response: 23 hours, 24 minutes and 16 seconds
but using the formula:
ZN(LOOKUP(ATTR(DATEPART('minute', [Disconnected At])),-1)-(ATTR(DATEPART('minute', [Connected At]))))
it isn't doing the trick.
Could someone help me to achieve my ideal response? Or similar result that would give me the completeness of date and time?
Thank You
Tableau ScreenShot
Use DATEDIFF by seconds between your two dates. Then create a calc field as follows:
//replace [Seconds] with whatever field has the number of seconds in it
//and use a custom number format of 00:00:00:00 (drop the first 0 to get rid of leading 0's for days)
IIF([Seconds] % 60 == 60,0,[Seconds] % 60)// seconds
+ IIF(INT([Seconds]/60) %60 == 60, 0, INT([Seconds]/60) %60) * 100 //minutes
+ IIF(INT([Seconds]/3600) % 24 == 0, 0, INT([Seconds]/3600) % 24) * 10000 //hours
+ INT([Seconds]/86400) * 1000000 // days
for more information, check out this blog post where I got this from. http://drawingwithnumbers.artisart.org/formatting-time-durations/

VBSCRIPT - Have Current Time and a Duration and want to calculate start time based on these values

Example:
Time = 09:41:46
Duration = 0:00:17 (IE 17 seconds)
Start Time = Time - Duration
Clearly I can't just break this up into hours minutes and seconds and do a basic minus operation given the 60 minute hour and 60 second minute etc.
Can't seem to get my head around how to calculate this and hoping someone has come across this before :).
You can use the DateAdd function.
For example, this will subtract 17 seconds from the specified date/time.
DateAdd("s", -17, "1/1/2013 09:41:46")
Try this
Time = 09:41:46
Duration = 0:00:17
Start_Time = FormatDateTime(Time - Duration, 3)
wscript.echo hour(Start_Time)
wscript.echo minute(Start_Time)
wscript.echo second(Start_Time)
References:
https://www.w3schools.com/asp/asp_ref_vbscript_functions.asp#date