Follow-up · depth 8
What would you monitor for dataclasses and __slots__ on-call?
What would you monitor for dataclasses and __slots__ on-call?
Answers use simple, clear English.
Quick interview answer
On-call for dataclasses and __slots__: alert on lag/latency, error spikes, and saturation — with runbooks. Baseline: @dataclass auto-generates __init__, __repr__, __eq__. slots=True (Py3.10+) fixes attributes and saves memory.
Detailed answer
On-call monitoring for dataclasses and __slots__: • Golden signals: latency, traffic, errors, saturation • dataclasses and __slots__-specific gauges (queue delay, pool wait, cache miss, etc.) • Alerting: burn-rate / multi-window so pages are actionable • Runbook: mitigate → diagnose → escalate Parent context: Baseline: @dataclass auto-generates __init__, __repr__, __eq__. slots=True (Py3.10+) fixes attributes and saves memory.
Full explanation
Tie each alert to a user-visible or SLO impact for dataclasses and __slots__. Baseline: @dataclass auto-generates __init__, __repr__, __eq__. slots=True (Py3.10+) fixes attributes and saves memory.
Follow-up questions
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Parent context — dataclasses and __slots__
Baseline: @dataclass auto-generates __init__, __repr__, __eq__. slots=True (Py3.10+) fixes attributes and saves memory.
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