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Permanent failure

A permanent failure is a failure condition that is not resolved by repeating the same operation without changing the request or relevant system conditions.

How 2 Manning books explain it

Think Distributed Systems

A failure whose subsequent occurrence is certain and whose resolution requires manual intervention.

How it is used
Classifies failures by recurrence likelihood and repair behavior when analyzing distributed-system reliability.
How deep it goes
Defines permanent failure through conditional probability and manual repair, contrasting it with two autorepairing failure types.
Examples
Uses a departing employee to model permanent failure within a distributed-company analogy.
What you can do after
Evaluate whether a failure is self-repairing or requires intervention by examining recurrence likelihood and repair behavior.

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Distributed Machine Learning Patterns

A permanent failure is a failure condition expected to persist such that retrying is ineffective.

How it is used
Diagnoses machine-learning workflow failures before allocating resources to retries or alternative tasks.
How deep it goes
Explains root-cause checks, dependency recovery, resource verification, and runtime metadata, but does not specify a framework implementation.
Examples
Uses lost in-memory data, preempted workers, out-of-memory errors, and memory-usage spikes during model training.
What you can do after
Diagnose/debug with it by distinguishing ineffective retries from recoverable failures and restarting prerequisite workflow steps when necessary.

Read how Distributed Machine Learning Patterns explains permanent failure →

Related concepts

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