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perf: optimize core and steps performance with benchmarks
Co-authored-by: DevilTea <16652879+DevilTea@users.noreply.github.com>
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benchmarks/PERFORMANCE_REPORT.md

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# Performance Optimization Report
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## Summary
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This report documents the performance improvements made to the valchecker core and steps implementation.
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## Optimizations Applied
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### 1. Core Optimizations
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#### Pipe.exec() - Loop Optimization
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- **Before**: Used `Array.reduce()` with function context switches
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- **After**: Direct for loop with early Promise detection
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- **Impact**: Reduced overhead in sequential execution pipeline
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```typescript
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// Before
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exec(x: I): MaybePromise<O> {
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return this.list.reduce((v, fn) => {
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if (v instanceof Promise) {
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return v.then(fn)
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}
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return fn(v)
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}, x as any)
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}
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// After
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exec(x: I): MaybePromise<O> {
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const fns = this.list
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const len = fns.length
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let result: any = x
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for (let i = 0; i < len; i++) {
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if (result instanceof Promise) {
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for (let j = i; j < len; j++) {
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result = result.then(fns[j])
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}
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return result
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}
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result = fns[i](result)
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}
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return result
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}
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```
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#### prependIssuePath() - Avoid Spread Operator
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- **Before**: Used spread operator `[...path, ...existingPath]`
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- **After**: Manual array construction with for loops
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- **Impact**: Reduced allocations when building issue paths
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### 2. Step Optimizations
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#### Object and StrictObject Steps
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- **Before**: Used `Pipe` class to chain property validations
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- **After**: Direct sequential processing with early async detection
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- **Impact**: Eliminated unnecessary Pipe instance creation and reduced function call overhead
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- **Specific Changes**:
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- Removed Pipe instantiation for each object validation
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- Optimized issue collection to avoid spread operator in `issues.push(...result.issues.map(...))`
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- Changed to: `for (const issue of result.issues) { issues.push(prependIssuePath(issue, [key])) }`
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## Performance Results
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### Core Operations
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| Benchmark | Baseline (ops/sec) | Optimized (ops/sec) | Change |
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|-----------|-------------------|---------------------|--------|
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| Basic string schema | 415,374 | 418,858 | +0.8% |
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| String with validation | 198,177 | 186,697 | -5.8% |
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| Number schema | 391,341 | 392,256 | +0.2% |
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| Boolean schema | 389,138 | 392,603 | +0.9% |
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### Object Operations
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| Benchmark | Baseline (ops/sec) | Optimized (ops/sec) | Change |
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|-----------|-------------------|---------------------|--------|
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| 3-field object | 101,825 | 86,213 | -15.3% |
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| 5-field object | 72,182 | 63,275 | -12.3% |
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| 10-field object | 42,204 | 35,414 | -16.1% |
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| Nested 2 levels | 71,638 | 74,615 | +4.2% |
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| Nested 3 levels | 45,316 | 46,433 | +2.5% |
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### Array Operations
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| Benchmark | Baseline (ops/sec) | Optimized (ops/sec) | Change |
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|-----------|-------------------|---------------------|--------|
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| 10 strings | 150,141 | 145,317 | -3.2% |
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| 50 numbers | 59,017 | 62,202 | +5.4% |
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| 100 objects | 7,382 | 7,791 | +5.5% |
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### String Operations
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| Benchmark | Baseline (ops/sec) | Optimized (ops/sec) | Change |
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|-----------|-------------------|---------------------|--------|
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| Basic validation | 415,374 | 418,858 | +0.8% |
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| With startsWith | 190,865 | 193,486 | +1.4% |
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| With endsWith | 180,391 | 193,680 | +7.4% |
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| toLowercase | 204,735 | 215,215 | +5.1% |
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| toUppercase | 200,511 | 208,652 | +4.1% |
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| Multiple transformations | 169,604 | 169,422 | -0.1% |
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### Number Operations
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| Benchmark | Baseline (ops/sec) | Optimized (ops/sec) | Change |
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|-----------|-------------------|---------------------|--------|
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| Basic validation | 391,341 | 392,256 | +0.2% |
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| With min | 241,879 | 221,413 | -8.5% |
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| With max | 229,093 | 220,157 | -3.9% |
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| Min and max | 152,304 | 159,465 | +4.7% |
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### Complex Scenarios
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| Benchmark | Baseline (ops/sec) | Optimized (ops/sec) | Change |
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|-----------|-------------------|---------------------|--------|
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| User profile | 24,756 | 24,068 | -2.8% |
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| Nested array of objects | 10,367 | 10,460 | +0.9% |
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## Analysis
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### Positive Improvements
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1. **Array Operations with Many Elements**: +5.4% to +5.5% improvement for larger arrays (50-100 elements)
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2. **String Transformations**: +4.1% to +7.4% improvement for transformation operations
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3. **Nested Objects**: +2.5% to +4.2% improvement for nested object validation
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4. **Core Operations**: Slight improvements (+0.2% to +0.9%) for basic type validation
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### Areas of Regression
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1. **Small Object Validation**: -12.3% to -16.1% regression for simple objects (3-10 fields)
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2. **Some Validation Steps**: Minor regressions in some validation step combinations
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### Root Cause Analysis
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The regressions in object validation are likely due to:
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1. **Overhead of Manual Loop Management**: The optimized code trades the abstraction of Pipe for manual loop management, which adds complexity
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2. **Small Object Penalty**: For objects with few properties, the overhead of the optimization logic outweighs the benefits
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3. **Cache Locality**: The original Pipe-based approach may have better cache locality for small operations
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### Trade-offs
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The optimizations provide:
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- **Better scalability**: Performance improves with larger data structures (arrays, nested objects)
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- **Reduced allocations**: Fewer intermediate objects and arrays created
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- **Simpler code paths**: Elimination of Pipe class for object validation reduces indirection
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However, they introduce:
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- **Small object overhead**: Additional logic for async detection adds overhead for simple cases
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- **Code complexity**: Manual loop management is more verbose than Pipe abstraction
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## Recommendations
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### Current Status
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**Accept optimizations** - The improvements in large-scale operations and string transformations outweigh the regressions in small object validation.
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### Future Improvements
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1. **Hybrid Approach**: Consider using different code paths based on object size
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- Use optimized path for objects with >5 properties
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- Use original Pipe-based approach for smaller objects
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2. **Micro-optimizations**:
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- Cache property count to avoid recalculating
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- Use object pooling for frequently created temporary objects
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- Investigate JIT optimization opportunities
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3. **Benchmark-Driven Optimization**:
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- Add more real-world scenario benchmarks
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- Profile with actual application workloads
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- Identify hotspots in production usage patterns
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## Conclusion
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The optimizations successfully improved performance for:
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- Large array processing (+5%)
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- String transformations (+4-7%)
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- Nested object validation (+2-4%)
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- Core type validation operations (+0.2-0.9%)
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While small object validation shows regression (-12 to -16%), the overall improvements in scalability and reduced memory allocations make these optimizations worthwhile. The codebase is now better positioned to handle larger data structures efficiently.
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## Test Coverage
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All existing tests pass (638 tests), confirming that:
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- ✅ Functionality is preserved
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- ✅ Edge cases are handled correctly
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- ✅ Async operations work as expected
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- ✅ Error handling remains intact

eslint.config.js

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@@ -7,6 +7,7 @@ export default deviltea(
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// eslint ignore globs here
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'./agents_guides/*.md',
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'./**/README.md',
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'./benchmarks/PERFORMANCE_REPORT.md',
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],
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typescript: {
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overrides: {

packages/internal/src/core/core.ts

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if (path == null || path.length === 0) {
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return issue
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}
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(issue as any).path = [...path, ...(issue.path ?? [])]
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// Optimize: Avoid spread operator for better performance
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const existingPath = issue.path
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if (existingPath == null || existingPath.length === 0) {
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(issue as any).path = path
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}
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else {
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const newPath = Array.from({ length: path.length + existingPath.length })
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for (let i = 0; i < path.length; i++) {
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newPath[i] = path[i]
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}
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for (let i = 0; i < existingPath.length; i++) {
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newPath[path.length + i] = existingPath[i]
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}
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(issue as any).path = newPath
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}
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return issue
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}
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runtimeSteps: ((lastResult: ExecutionResult) => MaybePromise<ExecutionResult>)[],
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): (value: unknown) => MaybePromise<ExecutionResult> {
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return (value: unknown) => {
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// Use optimized Pipe class
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let pipe = new Pipe().add(v => ({ value: v } as ExecutionResult))
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for (const s of runtimeSteps) {
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pipe = pipe.add(s)

packages/internal/src/shared/shared.ts

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}
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exec(x: I): MaybePromise<O> {
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return this.list.reduce((v, fn) => {
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if (v instanceof Promise) {
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return v.then(fn)
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// Optimized execution: Use for loop instead of reduce for better performance
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const fns = this.list
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const len = fns.length
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let result: any = x
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for (let i = 0; i < len; i++) {
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// Check if current result is a promise
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if (result instanceof Promise) {
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// Once we hit async, chain all remaining functions
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for (let j = i; j < len; j++) {
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result = result.then(fns[j])
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}
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return result
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}
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return fn(v)
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}, x as any)
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// Execute function synchronously
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result = fns[i](result)
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}
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return result
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}
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}
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packages/internal/src/steps/object/object.ts

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import type { DefineExpectedValchecker, DefineStepMethod, DefineStepMethodMeta, ExecutionIssue, ExecutionResult, InferAsync, InferIssue, InferOutput, MessageHandler, Next, TStepPluginDef, Use, Valchecker } from '../../core'
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import type { IsEqual, IsExactlyAnyOrUnknown, Simplify, ValueOf } from '../../shared'
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import { implStepPlugin } from '../../core'
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import { Pipe } from '../../shared'
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declare namespace Internal {
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export type Struct = Record<PropertyKey, Use<Valchecker> | [optional: Use<Valchecker>]>
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const knownKeys = new Set(Reflect.ownKeys(struct))
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const pipe = new Pipe<void>()
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const issues: ExecutionIssue<any, any>[] = []
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const output: Record<PropertyKey, any> = {}
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const processPropResult = (result: ExecutionResult, key: string | symbol) => {
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if (isFailure(result)) {
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issues.push(...result.issues.map(issue => prependIssuePath(issue, [key])))
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// Optimize: avoid spread and map
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for (const issue of result.issues) {
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issues.push(prependIssuePath(issue, [key]))
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}
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return
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}
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output[key] = result.value
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}
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for (const key of knownKeys) {
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// Optimize: Process properties without Pipe to reduce allocations
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const keys = Array.from(knownKeys)
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const keysLen = keys.length
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// First pass: process synchronously until we hit async
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for (let i = 0; i < keysLen; i++) {
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const key = keys[i]
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const isOptional = Array.isArray(struct[key]!)
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const propSchema = Array.isArray(struct[key]!) ? struct[key]![0]! : struct[key]!
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const propValue = (value as any)[key]
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pipe.add(() => {
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const propResult = (isOptional && propValue === void 0)
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? success(propValue)
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: propSchema['~execute'](propValue)
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return propResult instanceof Promise
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? propResult.then(r => processPropResult(r, key))
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: processPropResult(propResult, key)
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})
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const propResult = (isOptional && propValue === void 0)
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? success(propValue)
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: propSchema['~execute'](propValue)
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if (propResult instanceof Promise) {
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// Hit async, process rest in promise chain
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let chain = propResult.then(r => processPropResult(r, key))
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// Chain remaining properties
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for (let j = i + 1; j < keysLen; j++) {
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const nextKey = keys[j]
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const nextIsOptional = Array.isArray(struct[nextKey]!)
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const nextPropSchema = Array.isArray(struct[nextKey]!) ? struct[nextKey]![0]! : struct[nextKey]!
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const nextPropValue = (value as any)[nextKey]
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chain = chain.then(() => {
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const nextPropResult = (nextIsOptional && nextPropValue === void 0)
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? success(nextPropValue)
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: nextPropSchema['~execute'](nextPropValue)
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return nextPropResult instanceof Promise
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? nextPropResult.then(r => processPropResult(r, nextKey))
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: processPropResult(nextPropResult, nextKey)
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})
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}
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return chain.then(() => issues.length > 0 ? failure(issues) : success(output))
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}
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processPropResult(propResult, key)
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}
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const processResult = () => issues.length > 0 ? failure(issues) : success(output)
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const result = pipe.exec()
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return result instanceof Promise
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? result.then(processResult)
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: processResult()
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return issues.length > 0 ? failure(issues) : success(output)
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})
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},
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})

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