Files
unshackle/tests/core/test_adaptive_workers.py
T
imSp4rky 85503fd87c test(downloader): cover probe judging and the submission window
Assert that a probe is judged on the trailing tick, that a backoff is temporary and climbs back to the cap, and that a worker always finds its next segment staged. Documents adaptive-workers behaviour in the downloading guide.
2026-09-06 20:03:46 -06:00

334 lines
13 KiB
Python

"""Unit tests for the opt-in adaptive worker controller (AIMD hill-climb over segment
concurrency) and the plumbing that carries the flag into the downloader. Pure controller
tests only. No threads, sockets, or real downloads; the clock is supplied explicitly."""
from __future__ import annotations
import inspect
from functools import partial
from pathlib import Path
import pytest
from unshackle.core.downloaders.requests import (
TAIL_BOOST_MIN_SEGMENT_SIZE,
TAIL_BOOST_PART_SIZE,
AdaptiveWorkerController,
has_range_header,
plan_tail_parts,
requests,
split_ranges,
submission_window,
tail_boost_engages,
)
from unshackle.core.tracks.track import DownloadContext
pytestmark = pytest.mark.unit
TICK = 4.0
WINDOW = 10.0
DT = 0.5
def fill_window(c: AdaptiveWorkerController, rate: float, now: float) -> float:
"""Record ``WINDOW`` seconds of samples at ``rate`` bytes/sec ending on the 0.5s grid.
Records a sample at ``now`` and every 0.5s up to ``now + WINDOW`` so the oldest kept
sample sits exactly ``WINDOW`` old. That is the condition ``update`` needs to start acting.
"""
c.record_bytes(int(rate * DT), now)
end = now + WINDOW
while now < end - 1e-9:
now += DT
c.record_bytes(int(rate * DT), now)
return now
def run_tick(c: AdaptiveWorkerController, rate: float, now: float, errors: int = 0) -> tuple[int, float]:
"""Advance one tick recording samples at ``rate``, optionally inject ``errors``, then update."""
end = now + TICK
while now < end - 1e-9:
now += DT
c.record_bytes(int(rate * DT), now)
for _ in range(errors):
c.record_error(now)
return c.update(now), now
def test_starts_at_cap() -> None:
assert AdaptiveWorkerController(cap=16).target == 16
assert AdaptiveWorkerController(cap=4).target == 4
assert AdaptiveWorkerController(cap=1).target == 2 # clamped up to ADAPTIVE_MIN
assert AdaptiveWorkerController(cap=16, start=6).target == 6 # explicit start wins
def test_no_op_before_warmup() -> None:
c = AdaptiveWorkerController(cap=16, start=6, tick=TICK, window=WINDOW)
assert c.update(0.0) == 6 # arms last_tick before any samples are recorded
c.record_bytes(500_000, 3.0)
# a full tick has elapsed since arming, but under half a tick of samples exists -> hold
assert c.update(4.5) == 6
c.record_bytes(500_000, 6.5)
# warmed up (first sample 3.0, now 8.5) -> the probe fires
assert c.update(8.5) == 8
def test_climbs_while_speed_improves() -> None:
c = AdaptiveWorkerController(cap=16, start=6, tick=TICK, window=WINDOW)
now = 0.0
c.update(now) # arm
rate = 1_000_000.0
now = fill_window(c, rate, now)
targets = [c.update(now)] # first evaluation: 6 -> 8
for _ in range(6):
rate *= 2 # sustained >10% windowed gain each tick
target, now = run_tick(c, rate, now)
targets.append(target)
assert targets == sorted(targets) # monotonic non-decreasing
assert targets[-1] == 16 # climbed to the cap
assert max(targets) <= 16
def test_reverts_on_plateau() -> None:
c = AdaptiveWorkerController(cap=16, start=6, tick=TICK, window=WINDOW)
now = 0.0
c.update(now)
rate = 1_000_000.0
now = fill_window(c, rate, now)
assert c.update(now) == 8 # first increase 6 -> 8
# hold the rate flat: <10% gain -> the tick reverts the increase to its restore point
target, now = run_tick(c, rate, now)
assert target == 6
# a paying probe climbs again from the restore point
target, now = run_tick(c, rate * 4, now)
assert target == 8
def test_halves_and_cooldown_on_error_burst() -> None:
c = AdaptiveWorkerController(cap=16, start=6, tick=TICK, window=WINDOW)
now = 0.0
c.update(now)
now = fill_window(c, 1_000_000.0, now)
for _ in range(3):
c.record_error(now)
assert c.update(now) == 3 # 3 errors -> multiplicative decrease 6 // 2
# cooldown tick holds even with improving throughput
target, now = run_tick(c, 2_000_000.0, now)
assert target == 3
# cooldown consumed -> climbing resumes
target, now = run_tick(c, 4_000_000.0, now)
assert target == 5
def test_clamps_to_bounds_and_never_exceeds_cap() -> None:
c = AdaptiveWorkerController(cap=4, tick=TICK, window=WINDOW)
assert c.target == 4 # already at cap (min(6, 4))
now = 0.0
c.update(now)
rate = 1_000_000.0
now = fill_window(c, rate, now)
seen = [c.update(now)]
for _ in range(10):
rate *= 2
target, now = run_tick(c, rate, now)
seen.append(target)
assert all(2 <= s <= 4 for s in seen)
assert max(seen) == 4 # never exceeds the cap
def test_error_burst_floors_at_min() -> None:
c = AdaptiveWorkerController(cap=16, start=2, tick=TICK, window=WINDOW)
now = 0.0
c.update(now)
now = fill_window(c, 1_000_000.0, now)
for _ in range(5):
c.record_error(now)
assert c.update(now) == 2 # max(ADAPTIVE_MIN, 2 // 2)
def test_tail_guard_suppresses_increase() -> None:
# with fewer units of work than the target, the tick must hold instead of probing up
c = AdaptiveWorkerController(cap=16, start=6, tick=TICK, window=WINDOW)
now = 0.0
c.update(now) # arm at target 6
now = fill_window(c, 1_000_000.0, now)
assert c.update(now, 3) == 6 # 3 < target 6 -> holds at 6 rather than climbing to 8
def test_tail_guard_none_path_unchanged() -> None:
# without a tail count, the update still probes upward on the first evaluation
c = AdaptiveWorkerController(cap=16, start=6, tick=TICK, window=WINDOW)
now = 0.0
c.update(now)
now = fill_window(c, 1_000_000.0, now)
assert c.update(now) == 8 # first evaluation 6 -> 8
def test_tail_guard_still_halves_on_error_burst() -> None:
# the guard suppresses growth but never blocks an error-burst decrease
c = AdaptiveWorkerController(cap=16, start=6, tick=TICK, window=WINDOW)
now = 0.0
c.update(now)
now = fill_window(c, 1_000_000.0, now)
for _ in range(3):
c.record_error(now)
assert c.update(now, 2) == 3 # guard active (2 < 6) but 3 errors -> 6 // 2
PER_WORKER = 1_000_000.0 # bytes/sec each worker adds, so throughput scales with the target
def run_scaling(c: AdaptiveWorkerController, now: float, seconds: float, work: int = 10_000) -> float:
"""Advance ``seconds`` of a download whose throughput scales with the live target.
Records samples on the 0.5s grid at ``target * PER_WORKER`` bytes/sec and calls ``update``
on every step, so the speed window mixes old and new rates the way a real run does. ``work``
stands for the segments in flight or not yet started, kept high to hold the tail guard off.
"""
end = now + seconds
while now < end - 1e-9:
now += DT
c.record_bytes(int(c.target * PER_WORKER * DT), now)
c.update(now, work)
return now
def test_probe_kept_when_the_true_gain_clears_the_bar() -> None:
c = AdaptiveWorkerController(cap=16, start=10, tick=TICK, window=WINDOW)
now = 0.0
c.update(now)
rate = 1_000_000.0
now = fill_window(c, rate, now)
assert c.update(now) == 12 # first evaluation probes 10 -> 12
target, now = run_tick(c, rate * 1.2, now)
assert target == 14 # probe held, and the controller climbs again
def test_recovers_to_the_cap_after_an_error_burst() -> None:
c = AdaptiveWorkerController(cap=16, start=16, tick=TICK, window=WINDOW)
now = 0.0
c.update(now)
now = run_scaling(c, now, WINDOW)
for _ in range(3):
c.record_error(now)
now = run_scaling(c, now, TICK)
assert c.target == 8, "error burst must halve the target 16 -> 8"
now = run_scaling(c, now, 60.0)
assert c.target == 16, "controller never returned to its cap after backing off"
now = run_scaling(c, now, 40.0)
assert c.target == 16, "controller left the cap on a link where every worker still pays"
def test_reverts_when_the_probe_stops_paying_on_a_saturated_link() -> None:
c = AdaptiveWorkerController(cap=16, start=8, tick=TICK, window=WINDOW)
now = 0.0
c.update(now)
rate = 8 * PER_WORKER
now = fill_window(c, rate, now)
seen = [c.update(now, 10_000)]
for _ in range(8):
rate = min(c.target, 10) * PER_WORKER
end = now + TICK
while now < end - 1e-9:
now += DT
c.record_bytes(int(rate * DT), now)
seen.append(c.update(now, 10_000))
assert max(seen) <= 12 # one probe past the knee is the most it may hold
assert seen[-1] <= 12
def test_submission_window_stages_a_spare_segment_for_every_worker() -> None:
assert submission_window(16) == 32
for target in (2, 4, 6, 16):
assert submission_window(target) == 2 * target
def test_plan_tail_parts_full_coverage_no_gaps() -> None:
size = 20 * 1024 * 1024
parts = plan_tail_parts(size, spare=8)
assert parts, "expected a split for a large segment with spare capacity"
assert parts[0][0] == 0
assert parts[-1][1] == size - 1
for (_, prev_end), (start, _) in zip(parts, parts[1:]):
assert start == prev_end + 1 # each part begins exactly where the previous one ended
covered = sum(end - start + 1 for start, end in parts)
assert covered == size
def test_plan_tail_parts_respects_spare_cap() -> None:
size = 100 * 1024 * 1024 # would want ~25 parts at 4MB, but spare caps it
parts = plan_tail_parts(size, spare=3)
assert len(parts) <= 3
assert parts[0][0] == 0 and parts[-1][1] == size - 1
def test_plan_tail_parts_part_size_bounded() -> None:
size = 40 * 1024 * 1024
parts = plan_tail_parts(size, spare=16)
# never more parts than ~ceil(size / TAIL_BOOST_PART_SIZE)
assert len(parts) <= -(-size // TAIL_BOOST_PART_SIZE)
def test_plan_tail_parts_skips_small_or_no_spare() -> None:
assert plan_tail_parts(TAIL_BOOST_MIN_SEGMENT_SIZE - 1, spare=8) == [] # too small
assert plan_tail_parts(20 * 1024 * 1024, spare=1) == [] # not enough spare workers
assert plan_tail_parts(20 * 1024 * 1024, spare=0) == []
def test_split_ranges_covers_exactly_no_gaps_no_overlaps() -> None:
for size in (1, 100, 4 * 1024 * 1024, 16 * 1024 * 1024 + 7, 64 * 1024 * 1024):
parts = split_ranges(size, max_parts=8, part_target=4 * 1024 * 1024)
assert parts[0][0] == 0 and parts[-1][1] == size - 1
for (_, prev_end), (start, _) in zip(parts, parts[1:]):
assert start == prev_end + 1
assert all(start <= end for start, end in parts)
assert sum(end - start + 1 for start, end in parts) == size
def test_split_ranges_respects_max_parts_and_target() -> None:
assert len(split_ranges(100 * 1024 * 1024, max_parts=4, part_target=16 * 1024 * 1024)) == 4 # capped
assert len(split_ranges(10 * 1024 * 1024, max_parts=8, part_target=16 * 1024 * 1024)) == 1 # below one target
assert split_ranges(1, max_parts=8, part_target=16 * 1024 * 1024) == [(0, 0)]
def test_tail_boost_engages_on_idle_capacity() -> None:
# engages at the tail: idle workers outnumber the remaining whole segments
assert tail_boost_engages(remaining=4, pending=2, target=8) # spare 6 >= 4
assert tail_boost_engages(remaining=6, pending=6, target=16) # spare 10 >= 6
def test_tail_boost_engages_when_remaining_is_a_full_stride() -> None:
# remaining sits at 6 (one worker stride) while the target has climbed to 16, so 14 workers
# are idle. idle capacity far exceeds the remaining segments, so the boost must engage.
assert tail_boost_engages(remaining=6, pending=2, target=16)
def test_tail_boost_does_not_engage_early_or_saturated() -> None:
assert not tail_boost_engages(remaining=54, pending=2, target=6) # early: 54 > spare 4
assert not tail_boost_engages(remaining=1, pending=8, target=8) # saturated: spare 0
assert not tail_boost_engages(remaining=0, pending=2, target=8) # nothing left
assert not tail_boost_engages(remaining=5, pending=10, target=8) # over-subscribed: spare < 0
def test_has_range_header_detects_byte_range_slices() -> None:
# items carrying a Range header are byte-range slices; ranged-parallel must skip them
assert has_range_header({"url": "u", "headers": {"Range": "bytes=0-99"}})
assert has_range_header({"url": "u", "headers": {"range": "bytes=0-99"}}) # case-insensitive
assert not has_range_header({"url": "u", "headers": {"Authorization": "Bearer x"}})
assert not has_range_header({"url": "u", "headers": {}})
assert not has_range_header({"url": "u"})
def test_download_context_adaptive_default_false() -> None:
ctx = DownloadContext(save_path=Path("x.mp4"), save_dir=Path("."), progress=partial(lambda **_: None))
assert ctx.adaptive_workers is False
def test_requests_accepts_adaptive_kwarg() -> None:
sig = inspect.signature(requests)
assert "adaptive" in sig.parameters
assert sig.parameters["adaptive"].default is False