Module 04: Streaming Resilience and Failure Handling

Page Maps

graph LR
  family["Python Programming"]
  program["Python Functional Programming"]
  section["Streaming Resilience Failure Handling"]
  page["Module 04: Streaming Resilience and Failure Handling"]
  capstone["Capstone evidence"]

  family --> program --> section --> page
  page -.applies in.-> capstone

flowchart LR
  orient["Orient on the page map"] --> read["Read the main claim and examples"]
  read --> inspect["Inspect the related code, proof, or capstone surface"]
  inspect --> verify["Run or review the verification path"]
  verify --> apply["Apply the idea back to the module and capstone"]

Read the first diagram as a placement map: this page sits between the course promise, the lesson pages listed below, and the capstone surfaces that pressure-test the module. Read the second diagram as the study route for this page, so the diagrams point you toward the Lesson map, Exercises, and Closing criteria instead of acting like decoration.

Keep These Pages Open

Use these support surfaces while reading so failure handling remains a design boundary instead of turning into hidden retry folklore:

• Mid-Course Map for the bridge into failure and effect pressure
• Pressure Routes for pressure-first entry into the course
• Proof Matrix for the matching executable route
• Capstone Map for the result, retry, and breaker surfaces in FuncPipe

Carry this question into the module:

How do failures stay explicit enough to compose, retry, or aggregate without disappearing into control flow?

This module turns lazy pipelines into production-minded pipelines. Once computation is streaming, failures, retries, cleanup, and error aggregation can no longer be treated as afterthoughts.

Start with Module 04 Capstone Delta. It separates the behavioral resilience work from the broad Module 03→04 file split and gives one runnable route through the cumulative RAG change.

Learning outcomes

• how to turn recursion and reductions into explicit, reviewable pipeline behavior
• how to model record-level failures without collapsing whole streams
• how to choose between fail-fast and accumulate-many error strategies
• how to keep retries and resource cleanup explicit in streaming code

Lesson map

• Module 04 Capstone Delta
• Structural Recursion and Iteration
• Folds and Reductions
• Memoization
• Result and Option Failures
• Streaming Error Handling
• Error Aggregation
• Circuit Breakers
• Resource-Aware Streams
• Functional Retries
• Structured Error Reports
• Refactoring Guide

Exercises

Use Module 04 Exercises for cumulative work against the tracked reference state. Each exercise requires a predicted demand/output trace, a preserved Module 03 contract, executable evidence, and an explicit limit on what that evidence proves. Consult Exercise Answers only after recording your own prediction and review notes.

Capstone checkpoints

• Inspect how per-record failures become data rather than hidden exceptions.
• Review where retries are policy decisions instead of ad hoc loops.
• Verify that early termination still releases resources cleanly.

Before moving on

You should be able to explain which failures belong in the stream, which should stop the pipeline, and what evidence proves that cleanup still happens under both paths. Use Refactoring Guide and compare against capstone/_history/worktrees/module-04 before moving forward.

Closing criteria

• You can defend an error strategy in terms of stream semantics, not personal preference.
• You can point to where retries, circuit breaking, and cleanup are encoded as policy instead of scattered control flow.
• You can review resilience code and explain what evidence proves it under both success and failure.

Directory glossary

Use Glossary when you want the recurring language in this module kept stable while you move between lessons, exercises, and capstone checkpoints.