Performance

CKB Performance

Performance characteristics and benchmarks for CKB tools.

Latency Targets

CKB tools are classified by performance budget:

v5.2 Navigation Tools

Budget	P95 Target	Tools
Cheap	< 300ms	searchSymbols, explainFile, listEntrypoints, explainPath, getSymbol, explainSymbol
Heavy	< 2000ms	traceUsage, getArchitecture, getHotspots, summarizeDiff, recentlyRelevant, listKeyConcepts, analyzeImpact, getCallGraph, findReferences, justifySymbol

v6.0 Architectural Memory Tools

Budget	P95 Target	Tools
Cheap	< 300ms	getModuleResponsibilities, getOwnership, recordDecision, getDecisions, annotateModule
Heavy	< 2000ms	getArchitecture, getHotspots
Heavy	< 30000ms	refreshArchitecture

Benchmark Results

Environment: Apple M4 Pro, Go 1.23, macOS

Helper Function Performance

In-memory processing functions complete in nanoseconds to microseconds:

Function	Time	Description
classifyFileRiskLevel	1.0 ns	Risk classification for diff files
classifyHotspotRisk	0.77 ns	Churn-based risk assessment
computeDiffConfidence	2.2 ns	Confidence calculation
computePathConfidence	1.0 ns	Path confidence from basis
detectLanguage	7.3 ns	Language from file extension
suggestTestPath	19 ns	Test file path generation
titleCase	29 ns	Simple title casing
classifyRecency	43 ns	Timestamp recency classification
computeRecencyScore	44 ns	Recency scoring
classifyFileRole	78 ns	File role from path patterns
splitCamelCase	116 ns	CamelCase word splitting
classifyPathRole	297 ns	Full path role classification
categorizeConceptV52	561 ns	Concept categorization
buildDiffSummary	674 ns	Diff summary text generation
extractConcept	903 ns	Concept extraction from names

Pipeline Performance

Simulated tool processing with multiple items:

Pipeline	Items	Time	Budget	Headroom
PathClassification	10 paths	3.0 µs	300ms	99.999%
DiffProcessing	50 files	8.9 µs	2000ms	99.999%
HotspotProcessing	50 items	10.1 µs	2000ms	99.999%
ConceptExtraction	10 names	14.9 µs	2000ms	99.999%

v6.0 Hotspot Benchmarks

Function	Time	Description
CalculateInstability	0.25 ns	Martin's instability metric
ComputeCompositeScore	0.26 ns	Weighted hotspot score
NormalizeChurnScore	0.25 ns	Churn normalization
NormalizeCouplingScore	0.25 ns	Coupling normalization
NormalizeComplexityScore	0.26 ns	Complexity normalization
CalculateTrend	295 ns	Trend analysis (30 snapshots)

Pipeline	Items	Time	Budget	Headroom
HotspotScoring	100 files	69 ns	2000ms	99.999%
TrendAnalysis	50 files × 10 snapshots	5.7 µs	2000ms	99.999%

v6.0 Ownership Benchmarks

Function	Time	Description
normalizeAuthorKey	10 ns	Author key normalization
BlameOwnershipToOwners	47 ns	Convert blame to owners
CodeownersToOwners	56 ns	Convert CODEOWNERS to owners
isBot	743 ns	Bot detection (regex)
matchPattern	1.9 µs	Glob pattern matching
GetOwnersForPath	51 µs	Resolve owners for path

Pipeline	Items	Time	Budget	Headroom
OwnershipResolution	100 files × 50 rules	9.2 ms	300ms	96.9%

Where Time Actually Goes

In-memory processing is negligible. Real-world latency is dominated by I/O:

1. SCIP index lookups - Symbol search, references, call graph traversal
2. Git history queries - Commit history, diff stats, churn metrics
3. File system operations - Directory traversal, file reads

This is by design - CKB's value is in orchestrating these I/O operations efficiently and compressing results for LLM consumption.

Running Benchmarks

# Run all query benchmarks
go test ./internal/query/... -bench=. -benchmem -run=^$

# Run all v6.0 benchmarks
go test ./internal/hotspots/... ./internal/ownership/... -bench=. -benchmem -run=^$

# Run specific benchmark
go test ./internal/query/... -bench=BenchmarkClassifyPathRole -benchmem -run=^$

# Run with CPU profiling
go test ./internal/query/... -bench=BenchmarkDiffProcessingPipeline -cpuprofile=cpu.prof -run=^$

Optimization Tips

For Users

1. Keep SCIP index fresh - Stale indexes cause fallback to slower backends
2. Use scoped queries - Adding scope parameter reduces search space
3. Set reasonable limits - Don't request 1000 results if you need 20

For Contributors

1. Profile before optimizing - Most time is in I/O, not CPU
2. Cache aggressively - CKB's three-tier cache handles this
3. Batch I/O operations - Fewer round trips beats faster processing

Related

• MCP Integration - Tool documentation and usage
• Architecture - How CKB processes queries
• Configuration - Cache and backend settings