45. Performance: allocations and copies

Level: intermediate · Reading time: 20 min · Prerequisite: book/chapters/44-measure-before-optimizing.html · Track: supplemental · Maturity: draft · Last review: 2026-05-09

TL;DR (5 lines)

Performance claims need a concrete flow.
Allocation and traversal cost should stay visible.
Measurement should follow a clear boundary hypothesis.
Structural choices matter more than slogans.
The chapter should teach reasoning before micro-optimizing.

Frequent mistakes

Optimizing before measuring the real boundary.
Talking about performance without naming allocation or traversal cost.
Using invalid examples that are only syntactic, not operational.

Prerequisites: book/chapters/44-measure-before-optimizing.html. See also: book/chapters/44-measure-before-optimizing.html, book/chapters/27-grammar.html, book/chapters/31-build-errors.html.

Concrete Problem

Performance chapters become hand-wavy when they talk about speed without naming what is being measured or allocated.

Red Thread (Single Project)

One data-processing path is measured before and after a structural improvement.

For what

This chapter helps the reader connect performance claims to concrete data movement and memory behavior.

Work in this chapter

You will inspect one measurable flow, identify the costly boundary, and compare it to a safer or cheaper variant.

Coherent example

space demo/performance

proc accumulate(values: list[int]) -> int {
  let total: int = 0
  for value in values {
    set total = total + value
  }
  give total
}

export *

Complete examples

Each block below is a complete reading unit with its own boundary, data shape, and observable result.

Primary coherent example

This is the compact chapter anchor used by the surrounding explanation.

space demo/performance

proc accumulate(values: list[int]) -> int {
  let total: int = 0
  for value in values {
    set total = total + value
  }
  give total
}

export *

Single traversal

This version keeps traversal count and accumulator state visible.

space examples/performance/traversal

proc sum_once(values: list[int]) -> int {
  let total: int = 0
  for value in values {
    set total = total + value
  }
  give total
}

proc main(args: list[string]) -> int {
  give sum_once([1, 2, 3])
}

export *

Avoid repeated work

This variant stores the intermediate result before branching on it.

space examples/performance/cache

proc classify_total(values: list[int]) -> int {
  let total: int = 0
  for value in values {
    set total = total + value
  }
  if total < 0 { give 10 }
  give total
}

proc main(args: list[string]) -> int {
  give classify_total([2, 3, 4])
}

export *

Immediate work for this chapter

Name the measured boundary before suggesting optimization.
Show traversal, allocation, or copy cost in the code shape.
Keep the optimized variant behaviorally equivalent to the baseline.
Keep the first code block complete and aligned with current Vitte docs syntax.
Keep the invalid block focused on one broken contract only.
Replace generic prose with one concrete rule visible in the code.
Keep every example small enough to review from top to bottom.

Chapter override: 45-performance-allocations-copies.html

Dedicated problem

45. Performance: allocations and copies needs a concrete production-grade anchor around data movement, repeated work, and measured result. The page should keep the examples, diagnostics, and review rules tied to that exact boundary instead of drifting back to generic tutorial prose.

Specific complete examples

45. Performance: allocations and copies chapter anchor

The primary example is promoted here as the first chapter-specific production reading unit.

space demo/performance

proc accumulate(values: list[int]) -> int {
  let total: int = 0
  for value in values {
    set total = total + value
  }
  give total
}

export *

Single traversal

This version keeps traversal count and accumulator state visible.

space examples/performance/traversal

proc sum_once(values: list[int]) -> int {
  let total: int = 0
  for value in values {
    set total = total + value
  }
  give total
}

proc main(args: list[string]) -> int {
  give sum_once([1, 2, 3])
}

export *

Avoid repeated work

This variant stores the intermediate result before branching on it.

space examples/performance/cache

proc classify_total(values: list[int]) -> int {
  let total: int = 0
  for value in values {
    set total = total + value
  }
  if total < 0 { give 10 }
  give total
}

proc main(args: list[string]) -> int {
  give classify_total([2, 3, 4])
}

export *

Risks and diagnostics

Risk	Diagnostic signal	Action
Boundary drift	The chapter loses sight of data movement, repeated work, and measured result.	Restate the boundary beside the first code block and every invalid case.
Generic prose	A paragraph would still be true in another chapter.	Replace it with a code-specific rule from this page.
Weak diagnostic	The failure does not point back to the chapter contract.	Reduce the invalid case until one failure explains the rule.

Review checklist

The first example is complete and aligned with Vitte docs syntax.
The production section names where this construct belongs in real code.
The risk table connects each failure to a diagnostic or review action.
The avoid list rejects broad misuse without adding quiz-like prompts.

Production use

Use this chapter when a code review needs to preserve data movement, repeated work, and measured result.
Keep examples small enough to copy into fixtures or docs smoke tests.
Treat the invalid case as regression material for future docs checks.

What to avoid

Do not add a second topic that hides the chapter's main contract.
Do not expand examples by adding unrelated subsystems.
Do not rely on prose when a small Vitte block can show the rule.

Global explanation

A performance chapter should say what is being moved, copied, or traversed. Without that, 'faster' and 'slower' are just adjectives. The chapter needs one concrete flow that can be reasoned about before it is benchmarked.

Invalid case

proc noisy_accumulate(values: list[int]) -> int {
  if values { give 0 }
  give 0
}

This invalid case is intentionally small. It exists to isolate the contract failure that the chapter is trying to teach.

Common pitfalls

Optimizing before measuring the real boundary.
Talking about performance without naming allocation or traversal cost.
Using invalid examples that are only syntactic, not operational.

Short exercise

Take the example and explain one reason why data shape, not just arithmetic, could dominate the cost.

Summary in 5 points

Performance claims need a concrete flow.
Allocation and traversal cost should stay visible.
Measurement should follow a clear boundary hypothesis.
Structural choices matter more than slogans.
The chapter should teach reasoning before micro-optimizing.

Next best action

Extend the coherent example by one small, justified step and keep the same contract visible from input to output.

Chapter deep dive

45. Performance: allocations and copies makes cost visible before optimization starts. The chapter is written for a reader separating measured cost from intuition.

The practical boundary is: data movement, repeated work, and measured result. Keep that boundary in view while reading the example, the invalid case, and the exercise.

Role in the learning path

Performance chapters become hand-wavy when they talk about speed without naming what is being measured or allocated.

One data-processing path is measured before and after a structural improvement.

This chapter helps the reader connect performance claims to concrete data movement and memory behavior.

Profile-specific deep dive

Measured boundary

Name traversal, allocation, copying, or dispatch before optimizing.
The baseline and improved shape must return the same result.
Performance prose should point to visible data movement.

Cost review

Loops should expose accumulator and iteration count.
Avoid optimizing syntax when the data shape dominates cost.
Keep benchmark claims separate from design claims.

Reading the valid example

space demo/performance: names the ownership boundary before any behavior appears.
proc accumulate(values: list[int]) -> int {: states the callable contract: inputs first, result shape last.
let total: int = 0: keeps an intermediate decision visible for review.
for value in values {: is a point where data shape or repeated work can affect cost.
set total = total + value: is a point where data shape or repeated work can affect cost.
}: is a point where data shape or repeated work can affect cost.
give total: ends the local path with an explicit result.
}: is a point where data shape or repeated work can affect cost.
export *: is a point where data shape or repeated work can affect cost.

Lesson from the invalid example

proc noisy_accumulate(values: list[int]) -> int {: this line helps isolate the failure because it states the callable contract: inputs first, result shape last.
if values { give 0 }: this line helps isolate the failure because it guards a failure or edge case before the nominal result.
give 0: this line helps isolate the failure because it ends the local path with an explicit result.
}: this line helps isolate the failure because it is a point where data shape or repeated work can affect cost.

Engineering decisions to preserve

Name the boundary before changing code: Performance claims need a concrete flow.
Keep the smallest example executable: Allocation and traversal cost should stay visible.
Make the invalid path explain one failure only: Measurement should follow a clear boundary hypothesis.
Prefer a visible contract over an implied convention: Structural choices matter more than slogans.
Leave a review anchor that another maintainer can verify: The chapter should teach reasoning before micro-optimizing.

Context-specific review criteria

The page makes the data movement, repeated work, and measured result boundary visible before the first code block.
The intended reader, a reader separating measured cost from intuition, can follow the valid example through named contracts instead of memorized tokens.
The invalid example fails for the same reason the prose discusses.
The exercise extends the same contract instead of introducing an unrelated concept.
The next chapter can reuse the vocabulary introduced here without redefining it.
The chapter stays specific enough that its title materially changes the meaning of the page.
Every warning connects to a concrete code shape.
The summary leaves one durable engineering rule behind.

Contract matrix

Concern	Chapter rule	Evidence to keep
Ownership	Code belongs behind the boundary named by the chapter.	The chapter keeps ownership visible through data movement, repeated work, and measured result.
Input contract	The procedure receives a shape that is named before branching.	The valid example names the accepted shape before branching.
Nominal path	The clean path remains readable without hidden state.	The successful result can be found without reading hidden state.
Failure path	The invalid case isolates one failure reason.	The broken example has one main reason to fail.
Naming	Names explain the domain rather than only the mechanism.	Names remain tied to the chapter goal.
Types	Types remove ambiguity from values and results.	Fields and return values carry domain meaning.
Control flow	Branches stay traceable from guard to result.	Guards appear before the result they protect.
Module boundary	The public surface stays smaller than implementation detail.	The public surface remains smaller than the implementation detail.
Diagnostic value	The failure path points back to the exact contract.	The invalid example points back to the exact contract.
Test value	Regression evidence covers one passing path and one failing path.	One passing case and one failing case cover the lesson.
Refactor value	Implementation cleanup preserves the result shape.	The result shape stays stable during local cleanup.
Publication value	The chapter leaves one concrete engineering rule.	The chapter leaves one concrete engineering rule.

Rewrite path for this chapter

Rewrite the opening paragraph so it names data movement, repeated work, and measured result before naming syntax.
Keep the valid example small enough that the full contract fits on screen.
Move any broad claim back to a specific line in the example.
Preserve one invalid case that fails for the chapter's main reason.
Add one sentence explaining why the invalid case is not a random error.
Make every pitfall actionable by naming the code shape it damages.
Keep the exercise inside the same domain as the example.
Avoid introducing a second unrelated project just to show variety.
Use the summary to restate the chapter rule, not the table of contents.
Check that the next chapter can build on this vocabulary.
Remove any sentence that would still be true in every other chapter.
Keep the last action small, local, and testable.

Diagnostic anchors

The first inspected line is the one that declares the chapter's main contract.
The central type, field, procedure, or branch carries the chapter's main idea.
The invalid example includes a sentence-level explanation of its failure.
Refactors preserve the detail that would otherwise mislead a future reader.
The behavior that must stay stable is named before implementation changes begin.
Vague names are replaced before they become review friction.
Regression coverage protects the chapter's main contract.
Implementation details stay out of public API unless the chapter explicitly teaches that surface.
Beginner-facing diagnostics point to the contract, not to a random syntax detail.
The next chapter can assume one clearly named concept from this page.

When extending this chapter

Extend toward a reader separating measured cost from intuition, not toward a broader catalog of features.
Add a second example only if it sharpens the same contract.
Prefer a small variant over a new subsystem.
Keep prose close to code; every abstract claim should point to a visible shape.
Do not hide a new concept in the exercise.
If a paragraph explains policy, add the concrete code boundary it protects.
If a paragraph explains syntax, add the semantic reason the syntax matters.
If a paragraph explains architecture, identify the owner of each boundary.
If a paragraph explains failure, keep the failing line close to the explanation.
Stop expanding when the chapter has one complete, testable lesson.

Failure modes to avoid

Optimizing before measuring the real boundary.
Talking about performance without naming allocation or traversal cost.
Using invalid examples that are only syntactic, not operational.

Practice scenario

Start from the coherent example in 45. Performance: allocations and copies. Change one identifier, one guard, and one returned value. After each change, write down whether the public contract is still the same contract or a new one.

If the contract changed, update the type or result shape first. If only the implementation changed, keep the external name stable and add one regression note explaining what should not change again.

Before moving on

You can state the chapter role: makes cost visible before optimization starts.
You can point to the main boundary: data movement, repeated work, and measured result.
You can connect the invalid case to the problem statement: Performance chapters become hand-wavy when they talk about speed without naming what is being measured or allocated.
You can perform the exercise: Take the example and explain one reason why data shape, not just arithmetic, could dominate the cost.