⚡ Bolt: [performance improvement] Optimize derived state arrays across components

[ereezyy]

💡 What

Wrapped expensive derived state calculations (e.g., playerHorses filtering, winRate, profitMargin) in useMemo and useCallback hooks across several components (PlayerProfile, TrainingCenter, AIAssistant, AchievementSystem).

🎯 Why

Filtering the global horses array to find the current player's horses is an O(N) operation. Doing this directly in the component body caused it to recalculate on every single render, which could lead to noticeable UI lag as the number of horses in the game scales.

📊 Impact

Prevents redundant array filtering and mathematical calculations on every re-render. These values now only re-calculate when the underlying source data (horses, player.walletAddress, player.stats, etc.) actually changes.

🔬 Measurement

Verify by adding a console.log inside the useMemo hooks. Notice that the log is only printed during initial load or when the specific dependencies change, whereas previously it would print on every render (such as when switching tabs within the PlayerProfile or AIAssistant). Tests pass.

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PR created automatically by Jules for task 10239184075370774447 started by @ereezyy

Summary by Sourcery

Optimize performance of player-related components by memoizing expensive derived state computations based on horses and player data.

Enhancements:

• Memoize player-specific horse filtering and computed stats (win rate, profit margin, facility level) to avoid recalculation on every render.
• Refactor achievement generation logic to use memoized player horse data and a callback-based generator tied to relevant dependencies.
• Reuse memoized player horse list in the AI assistant for training insights instead of recalculating on each render.
• Tighten training engine implementation by making stat change mappings immutable with a const declaration.
• Add an internal Bolt learning note documenting best practices for memoizing derived state from large global stores.

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[sourcery-ai]

Reviewer's Guide

Optimizes several React components by memoizing expensive derived state (filtered player horse lists and computed stats) with useMemo/useCallback, slightly cleans up a training engine variable declaration, and documents the performance pattern in a Bolt/Jules note.

Sequence diagram for memoized achievement generation in AchievementSystem

sequenceDiagram
  participant React
  participant AchievementSystem
  participant useMemo_playerHorses
  participant useCallback_generateAchievements
  participant useEffect_generateAchievements

  React->>AchievementSystem: render()
  AchievementSystem->>useMemo_playerHorses: compute playerHorses(horses, player.walletAddress)
  useMemo_playerHorses-->>AchievementSystem: cached or recomputed playerHorses

  AchievementSystem->>useCallback_generateAchievements: define generateAchievements(player, playerHorses)
  useCallback_generateAchievements-->>AchievementSystem: memoized generateAchievements

  AchievementSystem->>useEffect_generateAchievements: register effect(deps: generateAchievements)
  React-->>useEffect_generateAchievements: run effect on mount or when generateAchievements changes
  useEffect_generateAchievements->>useCallback_generateAchievements: invoke generateAchievements()
  useCallback_generateAchievements->>AchievementSystem: build achievementList using playerHorses
  AchievementSystem-->>React: setAchievements(achievementList) triggers re-render

  React->>AchievementSystem: subsequent render (tabs change, local state changes)
  AchievementSystem->>useMemo_playerHorses: check deps(horses, player.walletAddress)
  useMemo_playerHorses-->>AchievementSystem: return cached playerHorses (if deps unchanged)
  AchievementSystem->>useCallback_generateAchievements: check deps(player, playerHorses)
  useCallback_generateAchievements-->>AchievementSystem: return cached generateAchievements (if deps unchanged)
  AchievementSystem->>useEffect_generateAchievements: deps unchanged
  useEffect_generateAchievements-->>React: no re-run of generateAchievements()

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Flow diagram for memoized derived state in PlayerProfile, TrainingCenter, AIAssistant, and AchievementSystem

flowchart TD
  Render["Component render (PlayerProfile, TrainingCenter, AIAssistant, AchievementSystem)"]
  CheckHorsesDeps["useMemo: deps include horses and player.walletAddress"]
  ComputePlayerHorses["Compute playerHorses = horses filtered by owner"]
  UseCachedPlayerHorses["Reuse cached playerHorses"]

  CheckStatsDeps["useMemo: deps include player.stats fields"]
  ComputeWinRate["Compute winRate from wins and totalRaces"]
  ComputeProfitMargin["Compute profitMargin from netProfit and totalEarnings"]
  UseCachedStats["Reuse cached winRate and profitMargin"]

  CheckFacilityDeps["useMemo: deps include player.assets.facilities"]
  ComputeFacilityLevel["Compute facilityLevel from Training Ground"]
  UseCachedFacility["Reuse cached facilityLevel"]

  CheckGenerateDeps["useCallback: deps include player and playerHorses"]
  MemoizedGenerateAchievements["Memoized generateAchievements callback"]
  EffectRuns["useEffect runs generateAchievements only when callback changes"]

  Render --> CheckHorsesDeps
  CheckHorsesDeps -->|deps changed| ComputePlayerHorses
  CheckHorsesDeps -->|deps unchanged| UseCachedPlayerHorses

  Render --> CheckStatsDeps
  CheckStatsDeps -->|deps changed| ComputeWinRate --> ComputeProfitMargin
  CheckStatsDeps -->|deps unchanged| UseCachedStats

  Render --> CheckFacilityDeps
  CheckFacilityDeps -->|deps changed| ComputeFacilityLevel
  CheckFacilityDeps -->|deps unchanged| UseCachedFacility

  Render --> CheckGenerateDeps
  CheckGenerateDeps --> MemoizedGenerateAchievements
  MemoizedGenerateAchievements --> EffectRuns

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File-Level Changes

Change	Details	Files
Memoize player-specific horses and achievement generation logic in AchievementSystem to avoid recomputing on every render.	Introduce a useMemo-derived playerHorses array that filters horses by the current player wallet address and depends on horses and player?.walletAddress. Convert generateAchievements from an inline function to a useCallback that depends on player and playerHorses, and move its invocation into a useEffect dependent on the callback. Remove the previous useEffect that recomputed achievements directly off player and horses each render.	src/components/AchievementSystem.tsx
Memoize derived player statistics and horses in PlayerProfile for performance.	Wrap playerHorses filtering over the global horses array in useMemo with horses and player.walletAddress as dependencies. Wrap winRate calculation in useMemo dependent on player.stats.totalRaces and player.stats.wins. Wrap profitMargin calculation in useMemo dependent on player.stats.totalEarnings and player.stats.netProfit.	src/components/PlayerProfile.tsx
Memoize player horses and facility level in TrainingCenter to avoid repeated array scans.	Wrap playerHorses filtering in useMemo with horses and player?.walletAddress as dependencies. Wrap facilityLevel derivation from player.assets.facilities in useMemo with player?.assets.facilities as dependency.	src/components/TrainingCenter.tsx
Share memoized playerHorses between AIAssistant component state and AI insight generation.	Add a memoized playerHorses array using React.useMemo based on horses and player?.walletAddress, returning an empty array when no player exists. Remove the per-call filtering of horses inside the AI insights generation logic and replace it with the shared memoized playerHorses.	src/components/AIAssistant.tsx
Minor refactor in trainingEngine to use a const for statChanges instead of let.	Change statChanges declaration from let to const while keeping its type and usage the same.	src/services/trainingEngine.ts
Document the performance optimization pattern in a Bolt/Jules note for future reference.	Add .jules/bolt.md describing the performance issue with repeated playerHorses filtering and the recommended useMemo pattern with appropriate dependencies.	.jules/bolt.md

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[sourcery-ai]

Hey - I've left some high level feedback:

• In PlayerProfile, the winRate and profitMargin computations are simple numeric operations; wrapping them in useMemo likely adds more overhead than it saves, so you might keep those as plain derived values while reserving memoization for the array filtering.
• In AchievementSystem, generateAchievements is only used inside a useEffect; you could simplify the hook graph by inlining that logic directly in the effect instead of introducing a useCallback, relying on the playerHorses memo to avoid redundant work.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- In `PlayerProfile`, the `winRate` and `profitMargin` computations are simple numeric operations; wrapping them in `useMemo` likely adds more overhead than it saves, so you might keep those as plain derived values while reserving memoization for the array filtering.
- In `AchievementSystem`, `generateAchievements` is only used inside a `useEffect`; you could simplify the hook graph by inlining that logic directly in the effect instead of introducing a `useCallback`, relying on the `playerHorses` memo to avoid redundant work.

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