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Author SHA1 Message Date
Naman Verma
7174733b84 test: test for values in each cached call test 2026-09-09 16:29:03 +05:30
Naman Verma
17b8f6a288 test: test for values in each cached call in sliding time range 2026-09-09 15:59:41 +05:30
Naman Verma
12783a35ad test: more descriptive var names in test 2026-09-09 15:52:13 +05:30
Naman Verma
c205ea99b5 test: add caching edge case integration tests 2026-09-09 15:43:39 +05:30

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from collections.abc import Callable
from datetime import UTC, datetime, timedelta
from http import HTTPStatus
from uuid import uuid4
from fixtures import types
from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
from fixtures.metrics import Metrics
from fixtures.querier import (
assert_results_equal,
build_builder_query,
get_series_values,
make_query_request,
)
MINUTE_MS = 60_000
def test_builder_shortening_the_time_range_at_the_end(
signoz: types.SigNoz,
create_user_admin: None, # pylint: disable=unused-argument
get_token: Callable[[str, str], str],
insert_metrics: Callable[[list[Metrics]], None],
) -> None:
# the cache outlives the run, so a fixed name would serve the previous run's
# points back to this one
metric_name = f"cache_end_shortened_{uuid4().hex[:8]}"
# 40 minutes back clears the flux interval, which holds recent data out of
# the cache. Flooring to a multiple of the 5m step makes the base query span
# two whole steps, so both its points are complete
start_time = datetime.fromtimestamp(int((datetime.now(tz=UTC) - timedelta(minutes=40)).timestamp()) // 300 * 300, tz=UTC)
start_time_ms = int(start_time.timestamp() * 1000)
end_time_ms_base_query = start_time_ms + 10 * MINUTE_MS
end_time_ms_shortened_query = start_time_ms + 7 * MINUTE_MS
query = [build_builder_query("A", metric_name, "max", "max", step_interval=300)]
# the 5m step splits the ten minutes into two points, each the max over its
# own step: minutes 0-4 and minutes 5-9. The second changes partway through,
# 256 until minute 7 and then 4096, so ending the range at minute 7 has to
# reach a different value than ending it at minute 10
insert_metrics(
[
Metrics(
metric_name=metric_name,
labels={"service": "api"},
timestamp=start_time + timedelta(minutes=minute),
value=(16, 16, 16, 16, 16, 256, 256, 4096, 4096, 4096)[minute],
type_="Gauge",
is_monotonic=False,
)
for minute in range(10)
]
)
token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
base_query = make_query_request(signoz, token, start_time_ms, end_time_ms_base_query, query, no_cache=False)
assert base_query.status_code == HTTPStatus.OK, base_query.text
points = sorted(get_series_values(base_query.json(), "A"), key=lambda point: point["timestamp"])
returned_points = [(point["value"], point.get("partial", False)) for point in points]
assert returned_points == [(16, False), (4096, False)]
from_cache = make_query_request(signoz, token, start_time_ms, end_time_ms_shortened_query, query, no_cache=False)
assert from_cache.status_code == HTTPStatus.OK, from_cache.text
uncached = make_query_request(signoz, token, start_time_ms, end_time_ms_shortened_query, query, no_cache=True)
assert uncached.status_code == HTTPStatus.OK, uncached.text
assert_results_equal(from_cache.json(), uncached.json(), "A", "shortened end")
# the shortened end reaches only minutes 5-6 of the second point, so it comes
# back as 256 and partial, where the cached one spans all five minutes
for label, response in (("from cache", from_cache), ("uncached", uncached)):
points = sorted(get_series_values(response.json(), "A"), key=lambda point: point["timestamp"])
returned_points = [(point["value"], point.get("partial", False)) for point in points]
assert returned_points == [(16, False), (256, True)], label
def test_builder_shortening_the_time_range_at_the_start(
signoz: types.SigNoz,
create_user_admin: None, # pylint: disable=unused-argument
get_token: Callable[[str, str], str],
insert_metrics: Callable[[list[Metrics]], None],
) -> None:
metric_name = f"cache_start_shortened_{uuid4().hex[:8]}"
# 40 minutes back clears the flux interval, which holds recent data out of
# the cache. Flooring to a multiple of the 5m step makes the base query span
# two whole steps, so both its points are complete
start_time = datetime.fromtimestamp(int((datetime.now(tz=UTC) - timedelta(minutes=40)).timestamp()) // 300 * 300, tz=UTC)
start_time_ms_base_query = int(start_time.timestamp() * 1000)
start_time_ms_shortened_query = start_time_ms_base_query + 3 * MINUTE_MS
end_time_ms = start_time_ms_base_query + 10 * MINUTE_MS
query = [build_builder_query("A", metric_name, "max", "max", step_interval=300)]
# the 5m step splits the ten minutes into two points, each the max over its
# own step: minutes 0-4 and minutes 5-9. Only minute 0 holds 65536, so a first
# point reaching it says the whole step was read even though the shortened
# range opens at minute 3
insert_metrics(
[
Metrics(
metric_name=metric_name,
labels={"service": "api"},
timestamp=start_time + timedelta(minutes=minute),
value=(65536, 16, 16, 16, 16, 4096, 4096, 4096, 4096, 4096)[minute],
type_="Gauge",
is_monotonic=False,
)
for minute in range(10)
]
)
token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
base_query = make_query_request(signoz, token, start_time_ms_base_query, end_time_ms, query, no_cache=False)
assert base_query.status_code == HTTPStatus.OK, base_query.text
points = sorted(get_series_values(base_query.json(), "A"), key=lambda point: point["timestamp"])
returned_points = [(point["value"], point.get("partial", False)) for point in points]
assert returned_points == [(65536, False), (4096, False)]
from_cache = make_query_request(signoz, token, start_time_ms_shortened_query, end_time_ms, query, no_cache=False)
assert from_cache.status_code == HTTPStatus.OK, from_cache.text
uncached = make_query_request(signoz, token, start_time_ms_shortened_query, end_time_ms, query, no_cache=True)
assert uncached.status_code == HTTPStatus.OK, uncached.text
assert_results_equal(from_cache.json(), uncached.json(), "A", "shortened start")
# starting inside the first point's step flags that point partial without
# clipping its value, which still covers the whole step and so reaches the
# 65536 at minute 0
for label, response in (("from cache", from_cache), ("uncached", uncached)):
points = sorted(get_series_values(response.json(), "A"), key=lambda point: point["timestamp"])
returned_points = [(point["value"], point.get("partial", False)) for point in points]
assert returned_points == [(65536, True), (4096, False)], label
def test_promql_running_the_same_query_twice(
signoz: types.SigNoz,
create_user_admin: None, # pylint: disable=unused-argument
get_token: Callable[[str, str], str],
insert_metrics: Callable[[list[Metrics]], None],
) -> None:
metric_name = f"cache_repeat_total_{uuid4().hex[:8]}"
# 40 minutes back clears the flux interval, which holds recent data out of
# the cache
start_time = datetime.fromtimestamp(int((datetime.now(tz=UTC) - timedelta(minutes=40)).timestamp()) // 60 * 60, tz=UTC)
start_time_ms = int(start_time.timestamp() * 1000)
end_time_ms = start_time_ms + 2 * MINUTE_MS
query = [{"type": "promql", "spec": {"name": "A", "query": f"sum(increase({metric_name}[2m]))", "step": 60}}]
# the counter opens a minute before the query so its first point has something
# to increase over, and starts far above its own rise across the range, below
# which increase clips its back-extrapolation at the counter's zero point. It
# rises by a different amount each minute, so every point is its own number
insert_metrics(
[
Metrics(
metric_name=metric_name,
labels={"service": "api"},
timestamp=start_time + timedelta(minutes=minute),
value=(1000, 1010, 1030, 1060, 1100)[minute + 1],
temporality="Cumulative",
type_="Sum",
is_monotonic=True,
)
for minute in range(-1, 4)
]
)
token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
first = make_query_request(signoz, token, start_time_ms, end_time_ms, query, no_cache=False)
assert first.status_code == HTTPStatus.OK, first.text
second = make_query_request(signoz, token, start_time_ms, end_time_ms, query, no_cache=False)
assert second.status_code == HTTPStatus.OK, second.text
assert_results_equal(first.json(), second.json(), "A", "the same query twice")
# promql reports a point at the instant the range closes, and the second run,
# answered out of what the first one cached, has to keep it
for run, response in (("first", first), ("second", second)):
points = sorted(get_series_values(response.json(), "A"), key=lambda point: point["timestamp"])
returned_points = [(point["timestamp"], point["value"]) for point in points]
## at each timestamp t, promql looks at points in (t-2minutes, t].
assert returned_points == [
(start_time_ms, 20), # t = 0, points taken 1000, 1010. hence diff over 1m is 10, extrapolated to 20.
(start_time_ms + MINUTE_MS, 40), # t = 1m, points taken 1010, 1030. hence diff over 1m is 20, extrapolated to 40.
(end_time_ms, 60), # t = 2m, points taken 1030, 1060. hence diff over 1m is 30, extrapolated to 60.
], f"{run} run"
def test_promql_shifting_the_time_range(
signoz: types.SigNoz,
create_user_admin: None, # pylint: disable=unused-argument
get_token: Callable[[str, str], str],
insert_metrics: Callable[[list[Metrics]], None],
) -> None:
metric_name = f"cache_shift_gauge_{uuid4().hex[:8]}"
# 40 minutes back clears the flux interval, which holds recent data out of
# the cache. Flooring to a whole minute is what makes the first query aligned
# to its 1m step, and the unaligned one half a step off it
start_time = datetime.fromtimestamp(int((datetime.now(tz=UTC) - timedelta(minutes=40)).timestamp()) // 60 * 60, tz=UTC)
aligned_start_time_ms = int(start_time.timestamp() * 1000)
aligned_end_time_ms = aligned_start_time_ms + 3 * MINUTE_MS
unaligned_start_time_ms = aligned_start_time_ms + MINUTE_MS // 2
unaligned_end_time_ms = aligned_end_time_ms + MINUTE_MS // 2
query = [{"type": "promql", "spec": {"name": "A", "query": f"max_over_time({metric_name}[2m])", "step": 60}}]
# a sample every 30s, rising by 100 each time. The two queries report 30s
# apart, so they land on different samples and share no value between them
insert_metrics(
[
Metrics(
metric_name=metric_name,
labels={"service": "api"},
timestamp=start_time + timedelta(seconds=30 * half_minute),
value=100 * (half_minute + 4),
type_="Gauge",
is_monotonic=False,
)
for half_minute in range(-3, 8)
]
)
token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
aligned_and_cached = make_query_request(signoz, token, aligned_start_time_ms, aligned_end_time_ms, query, no_cache=False)
assert aligned_and_cached.status_code == HTTPStatus.OK, aligned_and_cached.text
# what the cache now holds, and what the unaligned query must not be served
points = sorted(get_series_values(aligned_and_cached.json(), "A"), key=lambda point: point["timestamp"])
returned_points = [(point["timestamp"], point["value"]) for point in points]
## at each timestamp t, promql takes the highest sample in (t-2minutes, t],
## which is the one at t itself since the gauge only rises.
assert returned_points == [
(aligned_start_time_ms, 400), # t = 0
(aligned_start_time_ms + MINUTE_MS, 600), # t = 1m
(aligned_start_time_ms + 2 * MINUTE_MS, 800), # t = 2m
(aligned_end_time_ms, 1000), # t = 3m
]
unaligned_and_uncached = make_query_request(signoz, token, unaligned_start_time_ms, unaligned_end_time_ms, query, no_cache=True)
assert unaligned_and_uncached.status_code == HTTPStatus.OK, unaligned_and_uncached.text
# promql reports at the range start plus whole steps, so these points sit 30s
# off the cached ones. The first run stores them, the second reads them back
for run in ("first", "second"):
unaligned_and_cached = make_query_request(signoz, token, unaligned_start_time_ms, unaligned_end_time_ms, query, no_cache=False)
assert unaligned_and_cached.status_code == HTTPStatus.OK, unaligned_and_cached.text
assert_results_equal(unaligned_and_cached.json(), unaligned_and_uncached.json(), "A", f"unaligned query, {run} run")
points = sorted(get_series_values(unaligned_and_cached.json(), "A"), key=lambda point: point["timestamp"])
returned_points = [(point["timestamp"], point["value"]) for point in points]
## every point falls on a sample the aligned run never reported, so being
## served the cached run's answer shows up in the values and not only the
## timestamps.
assert returned_points == [
(unaligned_start_time_ms, 500), # t = 30s
(unaligned_start_time_ms + MINUTE_MS, 700), # t = 1m30s
(unaligned_start_time_ms + 2 * MINUTE_MS, 900), # t = 2m30s
(unaligned_end_time_ms, 1100), # t = 3m30s
], f"unaligned query, {run} run"
def test_builder_refreshing_a_sliding_time_range(
signoz: types.SigNoz,
create_user_admin: None, # pylint: disable=unused-argument
get_token: Callable[[str, str], str],
insert_metrics: Callable[[list[Metrics]], None],
) -> None:
metric_name = f"cache_sliding_{uuid4().hex[:8]}"
# 90 minutes back so even the twentieth refresh closes clear of the flux
# interval, which holds recent data out of the cache
start_time = datetime.fromtimestamp(int((datetime.now(tz=UTC) - timedelta(minutes=90)).timestamp()) // 60 * 60, tz=UTC)
start_time_ms = int(start_time.timestamp() * 1000)
query = [build_builder_query("A", metric_name, "max", "max")]
# the 1m step gives one point per seeded minute, and a value no other minute
# carries, so a point stitched in from the wrong range reads as the wrong minute
insert_metrics(
[
Metrics(
metric_name=metric_name,
labels={"service": "api"},
timestamp=start_time + timedelta(minutes=minute),
value=1000 + minute,
type_="Gauge",
is_monotonic=False,
)
for minute in range(80)
]
)
token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
# a dashboard left open on a one hour range, re-running a minute later each time
for refresh in range(20):
refresh_start_ms = start_time_ms + refresh * MINUTE_MS
from_cache = make_query_request(signoz, token, refresh_start_ms, refresh_start_ms + 60 * MINUTE_MS, query, no_cache=False)
assert from_cache.status_code == HTTPStatus.OK, from_cache.text
# each refresh is stitched out of overlapping cached ranges, so this catches
# a point served twice, dropped, or carried over from an earlier refresh
points = sorted(get_series_values(from_cache.json(), "A"), key=lambda point: point["timestamp"])
returned_points = [(point["timestamp"], point["value"], point.get("partial", False)) for point in points]
expected_points = [(start_time_ms + minute * MINUTE_MS, 1000 + minute, False) for minute in range(refresh, refresh + 60)]
assert returned_points == expected_points, f"refresh {refresh} did not return the minutes it covers"
last_refresh_start_ms = start_time_ms + 19 * MINUTE_MS
uncached = make_query_request(signoz, token, last_refresh_start_ms, last_refresh_start_ms + 60 * MINUTE_MS, query, no_cache=True)
assert uncached.status_code == HTTPStatus.OK, uncached.text
assert_results_equal(from_cache.json(), uncached.json(), "A", "the twentieth refresh")