codingjoe / codingjoe/threadmill

ExponentialBackoff overflows timedelta above attempt 46

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描述

`ExponentialBackoff.__call__` computes

```python
delay = min(self.base_delay * (self.factor**context.attempt), self.max_delay)
```

so the product is evaluated before `min()` clamps it. With the default shape (base delay 1 s, factor 2.0, max delay 1 h) the product exceeds the `timedelta` limit at attempt 47:

```
OverflowError: days=1628906115; must have magnitude <= 999999999
```

### Repro (threadmill 0.7.1)

```python
import datetime
from types import SimpleNamespace
from threadmill.retry import ExponentialBackoff

policy = ExponentialBackoff(
base_delay=datetime.timedelta(seconds=1),
max_delay=datetime.timedelta(hours=1),
factor=2.0,
max_retries=720,
)

def context(attempt):
error = SimpleNamespace(exception_class=ValueError)
return SimpleNamespace(attempt=attempt, task_result=SimpleNamespace(errors=[error]))

for attempt in range(1, 60):
try:
print(attempt, policy(context(attempt)))
except Exception as exc:
print(attempt, type(exc).__name__, exc)
break
```

Attempts 1 to 46 return a delay (2 s doubling to the 1 h cap at attempt 12), attempt 47 raises.

### Why it matters

`Executor.retry_delay` catches the exception, logs `Retry callback failed`, and returns `None`, so the backend acknowledges the result and the retry chain ends. The task looks like it exhausted its policy, but `max_retries` was never reached: a policy of 720 attempts really stops after 46 retries.

### Suggested fix

Clamp in seconds before building the `timedelta`, and derive the capped attempt count from `max_delay` so the exponential is never evaluated past the cap:

```python
seconds = min(
self.base_delay.total_seconds() * self.factor**context.attempt,
self.max_delay.total_seconds(),
)
return datetime.timedelta(seconds=seconds)
```

A plain `min()` on the two `timedelta` values does not help on its own, because the product still overflows before the comparison.

Found while bounding the spam scan retry budget in codingjoe/relay#230.

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调研方向

从 threadmill.retry.ExponentialBackoff.__call__ 开始,检查在应用 min() 之前其延迟是如何计算的。使用提供的尝试循环重现该问题,然后检查 Executor.retry_delay,以确认 callback 在尝试次数较高时不再失败。当直到 max_retries 的尝试(包括 max_retries)都保持限制在 max_delay,而不是以 overflow 结束时,即表示完成。

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评估

技术栈
python
领域
backend
Issue 类型
缺陷
难度
2/5
预计耗时
1-3 小时
活跃度
活跃
描述清晰度
描述清楚
新手友好度
78/100

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