Module 04: Streaming Resilience and Failure Handling

Module Position

flowchart TD
  family["Python Programming"] --> program["Python Functional Programming"]
  program --> module["Module 04: Streaming Resilience and Failure Handling"]
  module --> lessons["Lesson pages and worked examples"]
  module --> checkpoints["Exercises and closing criteria"]
  module --> capstone["Related capstone evidence"]

flowchart TD
  purpose["Start with the module purpose and main questions"] --> lesson_map["Use the lesson map to choose reading order"]
  lesson_map --> study["Read the lessons and examples with one review question in mind"]
  study --> proof["Test the idea with exercises and capstone checkpoints"]
  proof --> close["Move on only when the closing criteria feel concrete"]

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
• Engineering Question Map 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.

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

• 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

• Classify one failure path as stream-local, pipeline-fatal, or retryable, then justify the classification.
• Compare a fail-fast and accumulate-many design for the same input set and explain which evidence each path should emit.
• Trace one cleanup path and state what proof would show that resources are released under early termination.

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.