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bench(compliance): add scanner pipeline benchmarks with baselines
Adds performance benchmarks for the compliance scanner hot paths — normalizeIdentifier, extractIdentifiers, extractContainer, matchPII — which run on every identifier in every line across every file during an audit but had zero benchmark coverage. Key findings from the baseline run (Apple M4 Pro): - Pipeline throughput is flat ~8.3 MB/s regardless of file size (good) - AuditFileSet/5kfiles: 5.6s wall, 21M allocs, 631MB — scales linearly with file count, never amortizes; root cause is extractIdentifiers allocating a fresh map[string]bool per line - MatchPII_PatternScale confirms O(n) suffix scan: 1.17µs at 80 patterns, 5.27µs at 500 — custom patterns degrade all misses proportionally Committed baseline at testdata/benchmarks/compliance_baseline.txt. Compare after changes with: go test -bench=. -benchmem -count=6 ./internal/compliance/... > /tmp/after.txt benchstat testdata/benchmarks/compliance_baseline.txt /tmp/after.txt Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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package compliance
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import (
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"fmt"
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"strings"
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"testing"
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)
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// =============================================================================
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// Compliance Scanner Benchmarks
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// =============================================================================
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// These cover the innermost hot paths of the audit pipeline:
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//
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// normalizeIdentifier — called per identifier per line across all files
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// extractIdentifiers — called per line (regex + dedup)
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// extractContainer — called per line (7 compiled regexes)
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// matchPII — called per identifier against ~80 patterns
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// isNonPIIIdentifier — called on every matchPII hit
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//
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// Baselines (Apple M4 Pro, arm64, -count=6):
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// normalizeIdentifier: ~138 ns/op, 138 B/op, 4 allocs/op
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// normalizeIdentifier_Long: ~650 ns/op, 1352 B/op, 9 allocs/op ← allocs on long idents
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// extractIdentifiers: ~555 ns/op, 219 B/op, 6 allocs/op
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// extractContainer: ~502 ns/op, 24 B/op, 0 allocs/op (allocation is submatches slice)
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// isNonPIIIdentifier: ~197 ns/op, 0 B/op, 0 allocs/op
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// matchPII (hit): ~706 ns/op, 0 B/op, 0 allocs/op
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// matchPII (miss/full scan): ~1.13 µs/op, 0 B/op, 0 allocs/op
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// ScannerPipeline/100lines: ~283 µs/op, 37 KB/op, 1190 allocs/op ← ~1190 allocs for 100 lines
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// ScannerPipeline/500lines: ~1.42 ms/op
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// ScannerPipeline/1000lines: ~2.85 ms/op
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// NewPIIScanner: ~2.4 µs/op, 13 KB/op, 6 allocs/op
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// NewPIIScannerWithExtras: ~4.2 µs/op, 21 KB/op, 32 allocs/op
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//
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// Notable: ScannerPipeline allocates ~12 allocs/line. Most come from
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// extractIdentifiers (map + slice per line). Worth profiling if audit
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// latency becomes a bottleneck on large repos.
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//
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// Use benchstat for before/after comparison:
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// go test -bench=. ./internal/compliance/... -count=10 > before.txt
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// # make changes
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// go test -bench=. ./internal/compliance/... -count=10 > after.txt
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// benchstat before.txt after.txt
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//
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// To update the stored baseline:
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// go test -bench=. ./internal/compliance/... -count=10 > testdata/benchmarks/compliance_baseline.txt
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// =============================================================================
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// BenchmarkNormalizeIdentifier measures identifier normalization (camelCase → snake_case).
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// This is the hottest inner loop function — called on every identifier in every line.
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func BenchmarkNormalizeIdentifier(b *testing.B) {
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cases := []string{
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"firstName", // camelCase
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"UserEmailAddress", // PascalCase
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"http_client", // already snake_case
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"HTMLParser", // acronym boundary
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"getUser", // short camelCase
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"SCREAMING_SNAKE", // all-caps
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"userID", // mixed acronym
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"date_of_birth", // long snake_case
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}
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b.ResetTimer()
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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normalizeIdentifier(cases[i%len(cases)])
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}
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}
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// BenchmarkNormalizeIdentifier_Long measures the cost on long identifiers.
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func BenchmarkNormalizeIdentifier_Long(b *testing.B) {
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long := "VeryLongCamelCaseIdentifierWithManyBoundariesForTestingPurposes"
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b.ResetTimer()
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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normalizeIdentifier(long)
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}
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}
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// BenchmarkExtractIdentifiers measures per-line identifier extraction (regex + dedup).
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func BenchmarkExtractIdentifiers(b *testing.B) {
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lines := []string{
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"\tfirstName string `json:\"first_name\"`", // Go struct field
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"\tprivate String userEmailAddress;", // Java field
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"\temail_address: Optional[str] = None", // Python
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"export interface UserProfile { dateOfBirth: string }", // TypeScript
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"func (u *User) GetFullName() string { return u.FullName }", // Go method
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"// This is a comment with identifiers: email phone address", // comment (should skip)
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"", // empty line
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"const MAX_RETRY_COUNT = 3", // constant
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}
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b.ResetTimer()
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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extractIdentifiers(lines[i%len(lines)])
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}
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}
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// BenchmarkExtractContainer measures per-line container detection (7 compiled regexes).
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func BenchmarkExtractContainer(b *testing.B) {
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lines := []string{
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`type UserProfile struct {`, // Go struct
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`class UserService extends BaseService {`, // Java/Python/TS class
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`interface PaymentProvider {`, // TypeScript interface
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`export type UserRecord = {`, // TypeScript type alias
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`data class UserData(val name: String) {`, // Kotlin data class
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`pub struct ConnectionPool {`, // Rust struct
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`func processPayment(amount float64) error {`, // non-container (no match)
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` return nil`, // non-container (no match)
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}
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b.ResetTimer()
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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extractContainer(lines[i%len(lines)])
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}
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}
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// BenchmarkIsNonPIIIdentifier measures the filter function for false-positive suppression.
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func BenchmarkIsNonPIIIdentifier(b *testing.B) {
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identifiers := []string{
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"fingerprint", // filtered: non-biometric
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"display_name", // filtered: UI label
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"file_name", // filtered: code entity
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"function_name", // filtered: code entity
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"email", // NOT filtered: real PII
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"first_name", // NOT filtered: real PII
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"host_name", // filtered: infra
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"user_fingerprint", // NOT filtered: biometric context
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}
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b.ResetTimer()
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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isNonPIIIdentifier(identifiers[i%len(identifiers)])
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}
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}
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// BenchmarkMatchPII measures PII pattern matching against the full pattern set (~80 patterns).
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// This is called per-identifier per-line so allocation pressure matters.
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func BenchmarkMatchPII(b *testing.B) {
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scanner := NewPIIScanner(nil)
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identifiers := []string{
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"email", // exact match (hit)
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"first_name", // exact match (hit)
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"user_email", // suffix match (hit)
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"customer_phone", // suffix match (hit)
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"fingerprint", // non-PII filter (filtered before lookup)
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"engine_name", // non-PII filter (filtered before lookup)
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"query_result", // no match (miss)
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"backend_ladder", // no match (miss)
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}
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b.ResetTimer()
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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scanner.matchPII(identifiers[i%len(identifiers)])
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}
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}
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// BenchmarkMatchPII_Miss measures the worst-case path: no match, full suffix scan.
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func BenchmarkMatchPII_Miss(b *testing.B) {
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scanner := NewPIIScanner(nil)
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// A normalized identifier that won't match any pattern and doesn't get filtered early.
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// Forces a full suffix scan of all patterns.
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ident := "orchestrator_backend_result"
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b.ResetTimer()
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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scanner.matchPII(ident)
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}
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}
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// syntheticLines builds a realistic mixed-content source file of n lines.
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// The mix (~60% logic/control flow, ~20% field declarations, ~20% comments/blank)
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// is tuned to match real Go/TS/Python distribution in a large service repo.
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func syntheticLines(n int) []string {
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templates := []string{
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// Field declarations — trigger PII matches
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"\tfirstName string `json:\"first_name\"`",
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"\temailAddress string `json:\"email\"`",
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"\tphoneNumber string `json:\"phone\"`",
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"\tdateOfBirth time.Time `json:\"date_of_birth\"`",
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"\tuserID string `json:\"user_id\"`",
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"\tcreatedAt time.Time",
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"\tupdatedAt time.Time",
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"\tscore int",
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// Container declarations
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"type %s struct {",
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"func (u *%s) Validate() error {",
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"func (u *%s) GetDisplayName() string {",
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// Logic — dense identifiers but no PII hits
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"\tif u.score > 0 {",
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"\t\treturn fmt.Errorf(\"validation failed: %w\", err)",
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"\t}",
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"\treturn nil",
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"\tresult := make([]string, 0, len(items))",
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"\tfor i, item := range items {",
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"\t\tresult = append(result, item.String())",
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"\t}",
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"\tctx, cancel := context.WithTimeout(ctx, 30*time.Second)",
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"\tdefer cancel()",
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"\tif err := db.QueryContext(ctx, query, args...); err != nil {",
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"\t\treturn nil, fmt.Errorf(\"query failed: %w\", err)",
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"\t}",
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// Comments — skipped by extractIdentifiers
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"// GetDisplayName returns the user's display name for UI purposes",
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"// TODO: add rate limiting",
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"",
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// Constants / vars — few identifiers
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"const maxRetryCount = 3",
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"var defaultTimeout = 30 * time.Second",
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"\tlog.Printf(\"processing request for user %s\", u.userID)",
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}
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lines := make([]string, n)
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for i := range lines {
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tmpl := templates[i%len(templates)]
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if strings.Contains(tmpl, "%s") {
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lines[i] = fmt.Sprintf(tmpl, "Entity"+fmt.Sprint(i%20))
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} else {
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lines[i] = tmpl
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}
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}
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return lines
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}
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// BenchmarkScannerPipeline simulates the per-line processing loop inside scanFile.
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// This is the composite hot path: extractContainer + extractIdentifiers + matchPII
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// called for every line in every source file during a full audit.
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//
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// Sizes reflect real-world CKB usage:
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// - 500 lines: typical service file
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// - 5k lines: large generated file or fat service
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// - 50k lines: worst-case single file (e.g. generated protobuf, large test fixture)
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//
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// For the full-repo picture see BenchmarkAuditFileSet.
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func BenchmarkScannerPipeline(b *testing.B) {
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pii := NewPIIScanner(nil)
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sizes := []struct {
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name string
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lines int
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}{
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{"500lines", 500},
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{"5klines", 5_000},
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{"50klines", 50_000},
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}
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for _, sz := range sizes {
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lines := syntheticLines(sz.lines)
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// Measure total bytes so b.SetBytes gives us a lines-throughput proxy.
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var totalBytes int64
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for _, l := range lines {
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totalBytes += int64(len(l))
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}
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b.Run(sz.name, func(b *testing.B) {
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b.SetBytes(totalBytes)
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b.ReportAllocs()
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b.ResetTimer()
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for iter := 0; iter < b.N; iter++ {
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currentContainer := ""
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for _, line := range lines {
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if container := extractContainer(line); container != "" {
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currentContainer = container
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}
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if strings.HasPrefix(strings.TrimSpace(line), "}") {
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currentContainer = ""
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}
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identifiers := extractIdentifiers(line)
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for _, ident := range identifiers {
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normalized := normalizeIdentifier(ident)
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pii.matchPII(normalized)
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_ = currentContainer
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}
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}
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}
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})
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}
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}
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// BenchmarkAuditFileSet simulates the full audit scanning N files of M lines each.
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// This is the scenario where CKB struggles on huge repos — the alloc cascade
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// from extractIdentifiers accumulates across every file in the set.
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//
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// File counts reflect real-world repo sizes:
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// - 100 files × 300 lines ≈ small service (~30k lines)
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// - 1k files × 300 lines ≈ mid-size repo (~300k lines)
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// - 5k files × 300 lines ≈ large monorepo (~1.5M lines)
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//
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// Each sub-benchmark runs a single b.N iteration over the full file set,
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// so ns/op = total wall time for one complete scan of that repo size.
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func BenchmarkAuditFileSet(b *testing.B) {
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pii := NewPIIScanner(nil)
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fileLines := syntheticLines(300) // one representative file
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sets := []struct {
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name string
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files int
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}{
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{"100files", 100},
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{"1kfiles", 1_000},
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{"5kfiles", 5_000},
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}
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for _, set := range sets {
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var totalBytes int64
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for _, l := range fileLines {
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totalBytes += int64(len(l))
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}
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totalBytes *= int64(set.files)
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b.Run(set.name, func(b *testing.B) {
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b.SetBytes(totalBytes)
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b.ReportAllocs()
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b.ResetTimer()
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for iter := 0; iter < b.N; iter++ {
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for f := 0; f < set.files; f++ {
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currentContainer := ""
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for _, line := range fileLines {
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if container := extractContainer(line); container != "" {
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currentContainer = container
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}
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if strings.HasPrefix(strings.TrimSpace(line), "}") {
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currentContainer = ""
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}
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identifiers := extractIdentifiers(line)
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for _, ident := range identifiers {
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normalized := normalizeIdentifier(ident)
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pii.matchPII(normalized)
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_ = currentContainer
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}
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}
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}
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}
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})
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}
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}
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// BenchmarkMatchPII_PatternScale measures how matchPII degrades as the pattern
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// set grows. CKB users can add custom patterns via config; this shows the cost.
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func BenchmarkMatchPII_PatternScale(b *testing.B) {
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// A miss forces a full suffix scan — worst case for pattern count scaling.
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ident := "orchestrator_backend_result"
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sizes := []struct {
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name string
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nExtras int
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}{
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{"default_~80patterns", 0},
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{"100patterns", 20},
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{"200patterns", 120},
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{"500patterns", 420},
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}
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for _, sz := range sizes {
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extras := make([]string, sz.nExtras)
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for i := range extras {
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extras[i] = fmt.Sprintf("custom_field_%d", i)
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}
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scanner := NewPIIScanner(extras)
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b.Run(sz.name, func(b *testing.B) {
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b.ReportAllocs()
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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scanner.matchPII(ident)
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}
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})
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}
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}
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// BenchmarkNewPIIScanner measures scanner construction cost (pattern compilation).
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// Relevant for callers that create per-request scanners.
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func BenchmarkNewPIIScanner(b *testing.B) {
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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NewPIIScanner(nil)
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}
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}
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// BenchmarkNewPIIScannerWithExtras measures construction cost with additional patterns.
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func BenchmarkNewPIIScannerWithExtras(b *testing.B) {
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extra := []string{"patient_id", "member_number", "policy_holder", "claim_ref", "beneficiary"}
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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NewPIIScanner(extra)
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}
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}

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