load 10s
  metric{job="1"} 0+1x1000
  metric{job="2"} 0+2x1000

load 1ms
  metric_ms 0+1x10000

# Instant vector selectors.
eval instant at 10s metric @ 100
  metric{job="1"} 10
  metric{job="2"} 20

eval instant at 10s metric @ 100 offset 50s
  metric{job="1"} 5
  metric{job="2"} 10

eval instant at 10s metric offset 50s @ 100
  metric{job="1"} 5
  metric{job="2"} 10

eval instant at 10s metric @ 0 offset -50s
  metric{job="1"} 5
  metric{job="2"} 10

eval instant at 10s metric offset -50s @ 0
  metric{job="1"} 5
  metric{job="2"} 10

eval instant at 10s -metric @ 100
  {job="1"} -10
  {job="2"} -20

eval instant at 10s ---metric @ 100
  {job="1"} -10
  {job="2"} -20

# Millisecond precision.
eval instant at 100s metric_ms @ 1.234
  metric_ms 1234

# Range vector selectors.
eval instant at 25s sum_over_time(metric{job="1"}[100s] @ 100)
  {job="1"} 55

eval instant at 25s sum_over_time(metric{job="1"}[100s] @ 100 offset 50s)
  {job="1"} 15

eval instant at 25s sum_over_time(metric{job="1"}[100s] offset 50s @ 100)
  {job="1"} 15

# Different timestamps.
eval instant at 25s metric{job="1"} @ 50 + metric{job="1"} @ 100
  {job="1"} 15

eval instant at 25s rate(metric{job="1"}[100s] @ 100) + label_replace(rate(metric{job="2"}[123s] @ 200), "job", "1", "", "")
  {job="1"} 0.3

eval instant at 25s sum_over_time(metric{job="1"}[100s] @ 100) + label_replace(sum_over_time(metric{job="2"}[100s] @ 100), "job", "1", "", "")
  {job="1"} 165

# Subqueries.

# 10*(1+2+...+9) + 10.
eval instant at 25s sum_over_time(metric{job="1"}[100s:1s] @ 100)
  {job="1"} 460

# 10*(1+2+...+7) + 8.
eval instant at 25s sum_over_time(metric{job="1"}[100s:1s] @ 100 offset 20s)
  {job="1"} 288

# 10*(1+2+...+7) + 8.
eval instant at 25s sum_over_time(metric{job="1"}[100s:1s] offset 20s @ 100)
  {job="1"} 288

# Subquery with different timestamps.

# Since vector selector has timestamp, the result value does not depend on the timestamp of subqueries.
# Inner most sum=1+2+...+10=55.
# With [100s:25s] subquery, it's 55*4.
eval instant at 100s sum_over_time(sum_over_time(metric{job="1"}[100s] @ 100)[100s:25s] @ 50)
  {job="1"} 220

# Nested subqueries with different timestamps on both.

# Since vector selector has timestamp, the result value does not depend on the timestamp of subqueries.
# Sum of innermost subquery is 220 as above. The outer subquery repeats it 3 times.
eval instant at 0s sum_over_time(sum_over_time(sum_over_time(metric{job="1"}[100s] @ 100)[100s:25s] @ 50)[3s:1s] @ 3000)
  {job="1"} 660

# Testing the inner subquery timestamp since vector selector does not have @.

# Inner sum for subquery [100s:25s] @ 50 are
#   at -50 nothing, at -25 nothing, at 0=0, at 25=2, at 50=5.
# This sum of 7 is repeated 3 times by outer subquery.
eval instant at 0s sum_over_time(sum_over_time(sum_over_time(metric{job="1"}[10s])[100s:25s] @ 50)[3s:1s] @ 200)
  {job="1"} 21

# Inner sum for subquery [100s:25s] @ 200 are
#   at 125=12, at 150=15, at 175=17, at 200=20.
# This sum of 64 is repeated 3 times by outer subquery.
eval instant at 0s sum_over_time(sum_over_time(sum_over_time(metric{job="1"}[10s])[100s:25s] @ 200)[3s:1s] @ 50)
  {job="1"} 192

# Nested subqueries with timestamp only on outer subquery.
# Outer most subquery:
#   at 925=360
#     inner subquery: at 905=90+89, at 915=91+90
#   at 950=372
#     inner subquery: at 930=93+92, at 940=94+93
#   at 975=380
#     inner subquery: at 955=95+94, at 965=96+95
#   at 1000=392
#     inner subquery: at 980=98+97, at 990=99+98
eval instant at 0s sum_over_time(sum_over_time(sum_over_time(metric{job="1"}[20s])[20s:10s] offset 10s)[100s:25s] @ 1000)
  {job="1"} 1504

# minute is counted on the value of the sample.
eval instant at 10s minute(metric @ 1500)
  {job="1"} 2
  {job="2"} 5

# timestamp() takes the time of the sample and not the evaluation time.
eval instant at 10m timestamp(metric{job="1"} @ 10)
  {job="1"} 10

# The result of inner timestamp() will have the timestamp as the
# eval time, hence entire expression is not step invariant and depends on eval time.
eval instant at 10m timestamp(timestamp(metric{job="1"} @ 10))
  {job="1"} 600

eval instant at 15m timestamp(timestamp(metric{job="1"} @ 10))
  {job="1"} 900

# Time functions inside a subquery.

# minute is counted on the value of the sample.
eval instant at 0s sum_over_time(minute(metric @ 1500)[100s:10s])
  {job="1"} 20
  {job="2"} 50

# If nothing passed, minute() takes eval time.
# Here the eval time is determined by the subquery.
# [50m:1m] at 6000, i.e. 100m, is 50m to 100m.
# sum=51+52+...+59+0+1+2+...+40.
eval instant at 0s sum_over_time(minute()[50m:1m] @ 6000)
  {} 1315

# sum=46+47+...+59+0+1+2+...+35.
eval instant at 0s sum_over_time(minute()[50m:1m] @ 6000 offset 5m)
  {} 1365

# time() is the eval time which is determined by subquery here.
# 2901+...+3000 = (3000*3001 - 2899*2900)/2.
eval instant at 0s sum_over_time(vector(time())[100s:1s] @ 3000)
  {} 295050

# 2301+...+2400 = (2400*2401 - 2299*2300)/2.
eval instant at 0s sum_over_time(vector(time())[100s:1s] @ 3000 offset 600s)
  {} 235050

# timestamp() takes the time of the sample and not the evaluation time.
eval instant at 0s sum_over_time(timestamp(metric{job="1"} @ 10)[100s:10s] @ 3000)
  {job="1"} 100

# The result of inner timestamp() will have the timestamp as the
# eval time, hence entire expression is not step invariant and depends on eval time.
# Here eval time is determined by the subquery.
eval instant at 0s sum_over_time(timestamp(timestamp(metric{job="1"} @ 999))[10s:1s] @ 10)
  {job="1"} 55


clear