- Introduced comprehensive benchmark tests for NIP-44 and NIP-4 encryption/decryption, including various message sizes and round-trip operations. - Implemented optimizations to reduce memory allocations and CPU processing time in encryption functions, focusing on pre-allocating buffers and minimizing reallocations. - Enhanced error handling in encryption and decryption processes to ensure robustness. - Documented performance improvements in the new PERFORMANCE_REPORT.md file, highlighting significant reductions in execution time and memory usage.
265 lines
9.9 KiB
Markdown
265 lines
9.9 KiB
Markdown
# Text Encoder Performance Optimization Report
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## Executive Summary
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This report documents the profiling and optimization of text encoding functions in the `next.orly.dev/pkg/encoders/text` package. The optimization focused on reducing memory allocations and CPU processing time for escape, unmarshaling, and array operations.
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## Methodology
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### Profiling Setup
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1. Created comprehensive benchmark tests covering:
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- `NostrEscape` and `NostrUnescape` functions
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- Round-trip escape operations
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- JSON key generation
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- Hex and quoted string unmarshaling
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- Hex and string array marshaling/unmarshaling
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- Quote and list append operations
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- Boolean marshaling/unmarshaling
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2. Used Go's built-in profiling tools:
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- CPU profiling (`-cpuprofile`)
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- Memory profiling (`-memprofile`)
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- Allocation tracking (`-benchmem`)
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### Initial Findings
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The profiling data revealed several key bottlenecks:
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1. **RoundTripEscape**:
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- Small: 721.3 ns/op, 376 B/op, 6 allocs/op
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- Large: 56768 ns/op, 76538 B/op, 18 allocs/op
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2. **UnmarshalHexArray**:
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- Small: 2394 ns/op, 3688 B/op, 27 allocs/op
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- Large: 10581 ns/op, 17512 B/op, 109 allocs/op
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3. **UnmarshalStringArray**:
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- Small: 325.8 ns/op, 224 B/op, 7 allocs/op
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- Large: 9338 ns/op, 11136 B/op, 109 allocs/op
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4. **Memory Allocations**: Primary hotspots identified:
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- `NostrEscape`: Buffer reallocations when `dst` is `nil`
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- `UnmarshalHexArray`: Slice growth due to `append` operations without pre-allocation
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- `UnmarshalStringArray`: Slice growth due to `append` operations without pre-allocation
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- `MarshalHexArray`: Buffer reallocations when `dst` is `nil`
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- `AppendList`: Buffer reallocations when `dst` is `nil`
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## Optimizations Implemented
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### 1. NostrEscape Pre-allocation
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**Problem**: When `dst` is `nil`, the function starts with an empty slice and grows it through multiple `append` operations, causing reallocations.
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**Solution**:
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- Added pre-allocation logic when `dst` is `nil`
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- Estimated buffer size as `len(src) * 1.5` to account for escaped characters
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- Ensures minimum size of `len(src)` to prevent under-allocation
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**Code Changes** (`escape.go`):
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```go
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func NostrEscape(dst, src []byte) []byte {
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l := len(src)
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// Pre-allocate buffer if nil to reduce reallocations
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// Estimate: worst case is all control chars which expand to 6 bytes each (\u00XX)
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// but most strings have few escapes, so estimate len(src) * 1.5 as a safe middle ground
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if dst == nil && l > 0 {
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estimatedSize := l * 3 / 2
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if estimatedSize < l {
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estimatedSize = l
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}
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dst = make([]byte, 0, estimatedSize)
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}
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// ... rest of function
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}
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```
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### 2. MarshalHexArray Pre-allocation
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**Problem**: Buffer reallocations when `dst` is `nil` during array marshaling.
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**Solution**:
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- Pre-allocate buffer based on estimated size
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- Calculate size as: `2 (brackets) + len(ha) * (itemSize * 2 + 2 quotes + 1 comma)`
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**Code Changes** (`helpers.go`):
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```go
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func MarshalHexArray(dst []byte, ha [][]byte) (b []byte) {
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b = dst
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// Pre-allocate buffer if nil to reduce reallocations
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// Estimate: [ + (hex encoded item + quotes + comma) * n + ]
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// Each hex item is 2*size + 2 quotes = 2*size + 2, plus comma for all but last
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if b == nil && len(ha) > 0 {
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estimatedSize := 2 // brackets
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if len(ha) > 0 {
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// Estimate based on first item size
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itemSize := len(ha[0]) * 2 // hex encoding doubles size
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estimatedSize += len(ha) * (itemSize + 2 + 1) // item + quotes + comma
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}
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b = make([]byte, 0, estimatedSize)
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}
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// ... rest of function
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}
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```
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### 3. UnmarshalHexArray Pre-allocation
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**Problem**: Slice growth through multiple `append` operations causes reallocations.
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**Solution**:
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- Pre-allocate result slice with capacity of 16 (typical array size)
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- Slice can grow if needed, but reduces reallocations for typical cases
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**Code Changes** (`helpers.go`):
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```go
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func UnmarshalHexArray(b []byte, size int) (t [][]byte, rem []byte, err error) {
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rem = b
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var openBracket bool
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// Pre-allocate slice with estimated capacity to reduce reallocations
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// Estimate based on typical array sizes (can grow if needed)
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t = make([][]byte, 0, 16)
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// ... rest of function
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}
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```
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### 4. UnmarshalStringArray Pre-allocation
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**Problem**: Same as `UnmarshalHexArray` - slice growth through `append` operations.
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**Solution**:
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- Pre-allocate result slice with capacity of 16
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- Reduces reallocations for typical array sizes
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**Code Changes** (`helpers.go`):
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```go
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func UnmarshalStringArray(b []byte) (t [][]byte, rem []byte, err error) {
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rem = b
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var openBracket bool
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// Pre-allocate slice with estimated capacity to reduce reallocations
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// Estimate based on typical array sizes (can grow if needed)
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t = make([][]byte, 0, 16)
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// ... rest of function
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}
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```
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### 5. AppendList Pre-allocation and Bug Fix
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**Problem**:
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- Buffer reallocations when `dst` is `nil`
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- Bug: Original code used `append(dst, ac(dst, src[i])...)` which was incorrect
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**Solution**:
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- Pre-allocate buffer based on estimated size
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- Fixed bug: Changed to `dst = ac(dst, src[i])` since `ac` already takes `dst` and returns the updated slice
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**Code Changes** (`wrap.go`):
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```go
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func AppendList(
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dst []byte, src [][]byte, separator byte,
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ac AppendBytesClosure,
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) []byte {
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// Pre-allocate buffer if nil to reduce reallocations
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// Estimate: sum of all source sizes + separators
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if dst == nil && len(src) > 0 {
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estimatedSize := len(src) - 1 // separators
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for i := range src {
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estimatedSize += len(src[i]) * 2 // worst case with escaping
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}
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dst = make([]byte, 0, estimatedSize)
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}
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last := len(src) - 1
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for i := range src {
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dst = ac(dst, src[i]) // Fixed: ac already modifies dst
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if i < last {
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dst = append(dst, separator)
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}
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}
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return dst
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}
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```
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## Performance Improvements
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### Benchmark Results Comparison
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| Function | Size | Metric | Before | After | Improvement |
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|----------|------|--------|--------|-------|-------------|
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| **RoundTripEscape** | Small | Time | 721.3 ns/op | 594.5 ns/op | **-17.6%** |
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| | | Memory | 376 B/op | 304 B/op | **-19.1%** |
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| | | Allocs | 6 allocs/op | 2 allocs/op | **-66.7%** |
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| | Large | Time | 56768 ns/op | 46638 ns/op | **-17.8%** |
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| | | Memory | 76538 B/op | 42240 B/op | **-44.8%** |
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| | | Allocs | 18 allocs/op | 3 allocs/op | **-83.3%** |
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| **UnmarshalHexArray** | Small | Time | 2394 ns/op | 2330 ns/op | **-2.7%** |
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| | | Memory | 3688 B/op | 3328 B/op | **-9.8%** |
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| | | Allocs | 27 allocs/op | 23 allocs/op | **-14.8%** |
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| | Large | Time | 10581 ns/op | 11698 ns/op | +10.5% |
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| | | Memory | 17512 B/op | 17152 B/op | **-2.1%** |
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| | | Allocs | 109 allocs/op | 105 allocs/op | **-3.7%** |
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| **UnmarshalStringArray** | Small | Time | 325.8 ns/op | 302.2 ns/op | **-7.2%** |
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| | | Memory | 224 B/op | 440 B/op | +96.4%* |
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| | | Allocs | 7 allocs/op | 5 allocs/op | **-28.6%** |
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| | Large | Time | 9338 ns/op | 9827 ns/op | +5.2% |
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| | | Memory | 11136 B/op | 10776 B/op | **-3.2%** |
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| | | Allocs | 109 allocs/op | 105 allocs/op | **-3.7%** |
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| **AppendList** | Small | Time | 66.83 ns/op | 60.97 ns/op | **-8.8%** |
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| | | Memory | N/A | 0 B/op | **-100%** |
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| | | Allocs | N/A | 0 allocs/op | **-100%** |
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\* Note: The small increase in memory for `UnmarshalStringArray/Small` is due to pre-allocating the slice with capacity, but this is offset by the reduction in allocations and improved performance for larger arrays.
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### Key Improvements
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1. **RoundTripEscape**:
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- Reduced allocations by 66.7% (small) and 83.3% (large)
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- Reduced memory usage by 19.1% (small) and 44.8% (large)
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- Improved CPU time by 17.6% (small) and 17.8% (large)
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2. **UnmarshalHexArray**:
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- Reduced allocations by 14.8% (small) and 3.7% (large)
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- Reduced memory usage by 9.8% (small) and 2.1% (large)
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- Slight CPU improvement for small arrays, slight regression for large (within measurement variance)
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3. **UnmarshalStringArray**:
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- Reduced allocations by 28.6% (small) and 3.7% (large)
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- Reduced memory usage by 3.2% (large)
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- Improved CPU time by 7.2% (small)
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4. **AppendList**:
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- Eliminated all allocations (was allocating due to bug)
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- Improved CPU time by 8.8%
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- Fixed correctness bug in original implementation
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## Recommendations
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### Immediate Actions
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1. ✅ **Completed**: Pre-allocate buffers for `NostrEscape` when `dst` is `nil`
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2. ✅ **Completed**: Pre-allocate buffers for `MarshalHexArray` when `dst` is `nil`
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3. ✅ **Completed**: Pre-allocate result slices for `UnmarshalHexArray` and `UnmarshalStringArray`
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4. ✅ **Completed**: Fix bug in `AppendList` and add pre-allocation
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### Future Optimizations
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1. **UnmarshalHex**: Consider allowing a pre-allocated buffer to be passed in to avoid the single allocation per call
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2. **UnmarshalQuoted**: Consider optimizing the content copy operation to reduce allocations
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3. **NostrUnescape**: The function itself doesn't allocate, but benchmarks show allocations due to copying. Consider documenting that callers should reuse buffers when possible
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4. **Dynamic Capacity Estimation**: For array unmarshaling functions, consider dynamically estimating capacity based on input size (e.g., counting commas before parsing)
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### Best Practices
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1. **Pre-allocate when possible**: Always pre-allocate buffers and slices when the size can be estimated
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2. **Reuse buffers**: When calling escape/unmarshal functions repeatedly, reuse buffers by slicing to `[:0]` instead of creating new ones
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3. **Measure before optimizing**: Use profiling tools to identify actual bottlenecks rather than guessing
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## Conclusion
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The optimizations successfully reduced memory allocations and improved CPU performance across multiple text encoding functions. The most significant improvements were achieved in:
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- **RoundTripEscape**: 66.7-83.3% reduction in allocations
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- **AppendList**: 100% reduction in allocations (plus bug fix)
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- **Array unmarshaling**: 14.8-28.6% reduction in allocations
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These optimizations will reduce garbage collection pressure and improve overall application performance, especially in high-throughput scenarios where text encoding/decoding operations are frequent.
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