rust-heavy-performance
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Rust-Heavy Performance
Use this skill when a feature can become slow or memory-heavy. Prefer implementing heavy paths in src-tauri/ and keep the frontend thin.
Core rule
- If work is CPU-heavy, row-heavy, or repeatedly recomputed, do it in Rust.
- JavaScript/React should orchestrate UI state and rendering, not run bulk processing loops.
Rust-first implementation pattern
- Add or extend an async command in
src-tauri/src/commands.rs. - Keep query execution and pooling logic in
src-tauri/src/db.rsand related Rust modules. - Return a compact DTO to the frontend (already shaped for display).
- Call it from
src/data/repositories/using existinginvokepatterns. - Render in feature UI with virtualization for large result sets.
Memory efficiency guardrails
- Use bounded reads (
LIMIT, pagination, or chunked iteration), never unbounded materialization. - Avoid cloning large vectors/strings when references or incremental building is enough.
- Prefer streaming/chunked processing over collecting everything at once.
- Keep structs narrow for IPC responses; do not ship unused fields.
- Reuse pooled database connections; avoid ad hoc new clients per action.
Speed guardrails
- Push filtering, sorting, grouping, and aggregation down to SQL/Rust.
- Minimize Rust<->webview payload size; serialize only what UI needs now.
- Cache or memoize expensive Rust-side intermediate results only when reuse is likely and bounded.
- Use async command handlers; avoid blocking I/O in command paths.
Easy-to-implement defaults
- Start from existing command and repository patterns instead of inventing new data paths.
- Keep each command focused: one clear job, typed input, typed output.
- Introduce feature flags/options only after proving a real need.
- Keep frontend changes small: trigger command, handle loading/error, display bounded data.
Anti-patterns to avoid
- Doing large data transforms in React
useMemooruseEffect. - Returning huge raw row sets to the UI and shaping them in JavaScript.
- Recomputing heavy derived data on every render or keystroke.
- Mixing unrelated concerns into a single Rust command handler.
Review checklist
- Is the heavy work in Rust instead of JavaScript?
- Are reads and payloads explicitly bounded?
- Is memory growth controlled for worst-case input size?
- Is command/repository wiring following existing VeloxDB patterns?
- Does the UI virtualize large lists/tables and avoid unnecessary recomputation?
<!-- tomevault:4.0:skill_md:2026-05-23 -->Source: abeni16/veloxdb — distributed by TomeVault.
- Fluxly category
- Other
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- No explicit declaration found; this is not inferred or tested compatibility
- Static check
- 88 / 100 · heuristic scan, not runtime safety proof
- Author / version / license
- @tomevault-io · no license declared
- Fluxly token estimate
- Lean
- Fluxly setup estimate
- Guided setup
- External API key
- No requirement detected
- Detected OS requirements
- Unspecified
- Runtime requirements
- Unspecified
- Detected file/system behavior
-
- Read-only
- Shell exec
- Detected network behavior
- Local-only
- Install commands
- None (reference only)
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
The current SKILL.md does not define a fixed output example.If work is CPU-heavy, row-heavy, or repeatedly recomputed, do it in Rust. JavaScript/React should orchestrate UI state and rendering, not run bulk processing loops.
Add or extend an async command in src-tauri/src/commands.rs. Keep query execution and pooling logic in src-tauri/src/db.rs and related Rust modules. Return a compact DTO to the frontend (already shaped for display).
Use bounded reads (LIMIT, pagination, or chunked iteration), never unbounded materialization. Avoid cloning large vectors/strings when references or incremental building is enough. Prefer streaming/chunked processing over collecting everything at once.
Push filtering, sorting, grouping, and aggregation down to SQL/Rust. Minimize Rust<->webview payload size; serialize only what UI needs now. Cache or memoize expensive Rust-side intermediate results only when reuse is likely and bounded.
Start from existing command and repository patterns instead of inventing new data paths. Keep each command focused: one clear job, typed input, typed output. Introduce feature flags/options only after proving a real need.
Doing large data transforms in React useMemo or useEffect. Returning huge raw row sets to the UI and shaping them in JavaScript. Recomputing heavy derived data on every render or keystroke.
# Rust-Heavy Performance
Use this skill when a feature can become slow or memory-heavy. Prefer implementing heavy paths in `src-tauri/` and keep the frontend thin.
## Core rule
- If work is CPU-heavy, row-heavy, or repeatedly recomputed, do it in Rust.
- JavaScript/React should orchestrate UI state and rendering, not run bulk processing loops.
## Rust-first implementation pattern
1. Add or extend an async command in `src-tauri/src/commands.rs`.
2. Keep query execution and pooling logic in `src-tauri/src/db.rs` and related Rust modules.
3. Return a compact DTO to the frontend (already shaped for display).
4. Call it from `src/data/repositories/` using existing `invoke` patterns.
5. Render in feature UI with virtualization for large result sets.
## Memory efficiency guardrails
- Use bounded reads (`LIMIT`, pagination, or chunked iteration), never unbounded materialization.
- Avoid cloning large vectors/strings when references or incremental building is enough.
- Prefer streaming/chunked processing over collecting everything at once.
- Keep structs narrow for IPC responses; do not ship unused fields.
- Reuse pooled database connections; avoid ad hoc new clients per action.
## Speed guardrails
- Push filtering, sorting, grouping, and aggregation down to SQL/Rust.
- Minimize Rust<->webview payload size; serialize only what UI needs now.
- Cache or memoize expensive Rust-side intermediate results only when reuse is likely and bounded.
- Use async command handlers; avoid blocking I/O in command paths.
## Easy-to-implement defaults
- Start from existing command and repository patterns instead of inventing new data paths.
- Keep each command focused: one clear job, typed input, typed output.
- Introduce feature flags/options only after proving a real need.
…Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.
sections -> Core rule → Rust-first implementation pattern → Memory efficiency guardrails → Speed guardrails → Easy-to-implement defaults → Anti-patterns to avoid
terms -> Use this skill when a feature can become slow or memory-heavy. · - If work is CPU-heavy, row-heavy, or repeatedly recomputed, do it in Rust. · 1. Add or extend an async command in src-tauri/src/commands.rs. · - Use bounded reads (LIMIT, pagination, or chunked iteration), never unbounded materialization. · - Push filtering, sorting, grouping, and aggregation down to SQL/Rust. · - Start from existing command and repository patterns instead of inventing new data paths. · - Doing large data transforms in React useMemo or useEffect. · - Is the heavy work in Rust instead of JavaScript?
files/cmd -> src-tauri/ · src-tauri/src/commands.rs · src-tauri/src/db.rs · src/data/repositories/ · invoke · LIMIT · useMemo · useEffect
body sha256 -> 1b0de4f9a619
Decide Fit First
Design Intent
How To Use It
Boundaries And Review