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f3b0a91 44084af f3b0a91 44084af f3b0a91 44084af f3b0a91 44084af f3b0a91 44084af f3b0a91 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 | """Alert kinds: spec → oracle rule text, and spec → reference condition (independent of the text).
Seven kinds (U3): two SLO burn tiers plus five fleet/service templates modeled on common community
rules (error ratio with a traffic guard, memory near limit, crash looping, p99 latency, target down).
"""
from __future__ import annotations
from dataclasses import dataclass, field
from fractions import Fraction as F
from typing import Callable
from . import promsim as ps
from .series import FAMILIES
RECORD_PREFIX = "slo:sli_error:ratio_rate"
WINDOW_MIN = {"5m": 5, "30m": 30, "1h": 60, "6h": 360}
FOR_MIN = {"burn_page": 2, "burn_ticket": 15, "error_ratio": 5, "mem_high": 5, "crashloop": 10,
"latency_p99": 10, "target_down": 3}
# Lowest accepted `for` when a repair leaves the duration to the agent (B02 on fleet alerts): the postmortem
# only says spikes of "a minute or two" must not page. Instant conditions need 3m (2m spike + 1m margin);
# 5m-rate conditions keep a short spike inside the window for ~5 evaluations, so they need 5m.
FOR_FLOOR = {"error_ratio": 5, "latency_p99": 5, "mem_high": 3, "crashloop": 3}
BURN = {"burn_page": ("1h", "5m", F("14.4")), "burn_ticket": ("6h", "30m", F(6))}
FLEET_KINDS = ("error_ratio", "mem_high", "crashloop", "latency_p99")
FLEET_NAMES = {
"error_ratio": ["HighErrorRatio", "ServiceErrorRateHigh", "HTTPErrorRatioHigh"],
"mem_high": ["ContainerMemoryNearLimit", "PodMemoryHigh"],
"crashloop": ["PodCrashLooping", "ContainerRestartingOften"],
"latency_p99": ["LatencyP99High", "SlowRequestsP99"],
}
class NearThreshold(Exception):
"""A reference value is too close to a threshold; the scenario must be re-sampled."""
@dataclass
class AlertSpec:
req_id: str
kind: str
name: str
service: str | None # per-service alerts (burn_*, target_down); None for fleet alerts
family: str # SLI family: http | grpc | latency
labels: dict[str, str]
runbook: str
params: dict = field(default_factory=dict)
@property
def for_min(self) -> int:
return int(self.params.get("for_min", FOR_MIN[self.kind]))
@property
def for_range(self) -> tuple[int, int]:
"""Accepted `for` minutes (lo, hi); a single value unless the task text leaves it open."""
lo, hi = self.params.get("for_range", (self.for_min, self.for_min))
return int(lo), int(hi)
@property
def uses_records(self) -> bool:
return self.kind in BURN
@property
def threshold(self) -> F:
if self.kind in BURN:
return F(self.params.get("factor", BURN[self.kind][2])) * (1 - F(self.params["target"]))
return F(self.params.get("thr", 0))
def records_used(self) -> list[str]:
if self.kind not in BURN:
return []
lw, sw, _ = BURN[self.kind]
return [RECORD_PREFIX + lw, RECORD_PREFIX + sw]
def fmt(x: F) -> str:
s = f"{float(x):.10f}".rstrip("0").rstrip(".")
return s or "0"
def _sel(service: str | None) -> str:
return f'{{service="{service}"}}' if service else ""
def oracle_expr(a: AlertSpec) -> str:
k, p = a.kind, a.params
if k in BURN:
lw, sw, factor = BURN[k]
b = fmt(1 - F(p["target"]))
return (f"{RECORD_PREFIX}{lw}{_sel(a.service)} > ({fmt(factor)} * {b})"
f" and {RECORD_PREFIX}{sw}{_sel(a.service)} > ({fmt(factor)} * {b})")
if k == "error_ratio":
fam = FAMILIES[a.family]
m, bad = fam["metric"], fam["bad_sel"]
return (f"(sum by (service) (rate({m}{{{bad}}}[5m])) / sum by (service) (rate({m}[5m])))"
f" > {fmt(F(p['thr']))} and sum by (service) (rate({m}[5m])) > {fmt(F(p['min_rps']))}")
if k == "mem_high":
return ('max by (service) (container_memory_working_set_bytes{container="app"}'
' / container_spec_memory_limit_bytes{container="app"})'
f" > {fmt(F(p['thr']))}")
if k == "crashloop":
return (f"sum by (service) (increase(kube_pod_container_status_restarts_total[15m]))"
f" > {fmt(F(p['thr']))}")
if k == "latency_p99":
return ("histogram_quantile(0.99, sum by (service, le) "
f"(rate(http_request_duration_seconds_bucket[5m]))) > {fmt(F(p['thr']))}")
if k == "target_down":
s = a.service
return f'sum by (service) (up{{service="{s}"}}) == 0 or absent(up{{service="{s}"}})'
raise ValueError(k)
SUMMARY = {
"burn_page": "{{ $labels.service }} is burning its error budget fast (14.4x over 1h and 5m)",
"burn_ticket": "{{ $labels.service }} is burning its error budget steadily (6x over 6h and 30m)",
"error_ratio": "{{ $labels.service }} error ratio is {{ $value | humanizePercentage }}",
"mem_high": "{{ $labels.service }} has a container above its memory limit threshold",
"crashloop": "{{ $labels.service }} containers restarted more than {{ $value }} times in 15m",
"latency_p99": "{{ $labels.service }} p99 latency is {{ $value | humanizeDuration }}",
"target_down": "{{ $labels.service }} has no healthy scrape targets",
}
def oracle_rule(a: AlertSpec) -> dict:
rule = {"alert": a.name, "expr": oracle_expr(a)}
if a.for_min:
rule["for"] = f"{a.for_min}m"
rule["labels"] = dict(a.labels)
rule["annotations"] = {"summary": SUMMARY[a.kind], "runbook_url": a.runbook}
return rule
def record_expr(family: str, window: str, slo_le: str = "0.5") -> str:
if family == "latency":
return (f'1 - (sum by (service) (rate(http_request_duration_seconds_bucket{{le="{slo_le}"}}[{window}]))'
f" / sum by (service) (rate(http_request_duration_seconds_count[{window}])))")
fam = FAMILIES[family]
m = fam["metric"]
return f"sum by (service) (rate({m}{{{fam['bad_sel']}}}[{window}])) / sum by (service) (rate({m}[{window}]))"
def record_rules(family: str, windows: list[str], slo_le: str = "0.5") -> list[dict]:
return [{"record": RECORD_PREFIX + w, "expr": record_expr(family, w, slo_le)} for w in windows]
# ---------------------------------------------------------------------------- reference semantics
def sli_ratio(scen: ps.Scenario, family: str, t: int, w: int, slo_le: str = "0.5") -> dict[str, F]:
if family == "latency":
return ps.ratio_by(scen, "http_request_duration_seconds_bucket", None, t, w,
total_metric="http_request_duration_seconds_count",
good=lambda l, le=slo_le: l.get("le") == le)
fam = FAMILIES[family]
return ps.ratio_by(scen, fam["metric"], fam["bad"], t, w)
def _gt(values: dict[str, F], thr: F, only: str | None = None) -> set[str]:
out = set()
for svc, v in values.items():
if only and svc != only:
continue
if ps.near(v, thr):
raise NearThreshold(f"{svc}={float(v)} vs {float(thr)}")
if v > thr:
out.add(svc)
return out
def condition(a: AlertSpec, scen: ps.Scenario) -> Callable[[int], set[str]]:
"""Reference: services for which the alert expression returns a sample at minute t."""
k, p, thr = a.kind, a.params, a.threshold
def burn(t: int) -> set[str]:
lw, sw, _ = BURN[k]
le = p.get("slo_le", "0.5")
long_ = _gt(sli_ratio(scen, a.family, t, WINDOW_MIN[lw], le), thr, a.service)
short = _gt(sli_ratio(scen, a.family, t, WINDOW_MIN[sw], le), thr, a.service)
return long_ & short
def error_ratio(t: int) -> set[str]:
fam = FAMILIES[a.family]
ratio = _gt(ps.ratio_by(scen, fam["metric"], fam["bad"], t, 5), thr)
traffic = _gt(ps.sum_rate_by(scen.select(fam["metric"]), "service", t, 5), F(p["min_rps"]))
return ratio & traffic
def mem_high(t: int) -> set[str]:
best: dict[str, F] = {}
limits = {tuple(sorted(s.labels.items())): s for s in scen.select("container_spec_memory_limit_bytes")}
for s in scen.select("container_memory_working_set_bytes"):
lim = limits.get(tuple(sorted(s.labels.items())))
v, lv = ps.instant(s.values, t), ps.instant(lim.values, t) if lim else None
if v is None or not lv:
continue
svc = s.labels["service"]
best[svc] = max(best.get(svc, F(-1)), F(v, lv))
return _gt(best, thr)
def crashloop(t: int) -> set[str]:
inc = ps.sum_rate_by(scen.select("kube_pod_container_status_restarts_total"), "service", t, 15,
is_rate=False)
return _gt(inc, thr)
def latency_p99(t: int) -> set[str]:
return _gt(ps.quantile_by_service(scen, "http_request_duration_seconds_bucket", F(99, 100), t, 5), thr)
def target_down(t: int) -> set[str]:
vals = [ps.instant(s.values, t) for s in scen.select("up", lambda l: l["service"] == a.service)]
present = [v for v in vals if v is not None]
if not present or sum(present) == 0:
return {a.service}
return set()
return {"burn_page": burn, "burn_ticket": burn, "error_ratio": error_ratio, "mem_high": mem_high,
"crashloop": crashloop, "latency_p99": latency_p99, "target_down": target_down}[k]
def timeline(a: AlertSpec, scen: ps.Scenario, for_min: int | None = None) -> tuple[list[set[str]], list[set[str]]]:
"""(condition per minute, firing per minute) for the whole scenario (`for_min` defaults to the oracle's)."""
cond = condition(a, scen)
conds = [cond(t) for t in range(scen.minutes + 1)]
fires = ps.firing_timeline(scen.minutes, lambda t: conds[t], a.for_min if for_min is None else for_min)
return conds, fires
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