data-flow-performance.md•29.7 kB
# CodeGraph Data Flow & Performance Architecture
This document details the data flow patterns, performance optimization strategies, and runtime behavior of the CodeGraph system.
## Request Processing Data Flow
```mermaid
sequenceDiagram
participant Client
participant LB as Load Balancer
participant API as API Gateway
participant Auth as Auth Service
participant Cache as Cache Layer
participant Queue as Task Queue
participant Parser as Code Parser
participant Graph as Graph Store
participant Vector as Vector Engine
participant FAISS as FAISS Index
participant RocksDB as RocksDB
Note over Client,RocksDB: Code Analysis Request Flow
Client->>LB: POST /api/v1/analyze
LB->>API: Route request
API->>Auth: Validate JWT token
alt Token Valid
Auth-->>API: ✓ Authorized
API->>Cache: Check cached result
alt Cache Hit
Cache-->>API: Return cached data
API-->>LB: Response (< 10ms)
LB-->>Client: JSON response
else Cache Miss
API->>Queue: Enqueue analysis task
Queue->>Parser: Parse source files
par Parallel Processing
Parser->>Graph: Store AST nodes
Parser->>Vector: Generate embeddings
end
Graph->>RocksDB: Persist graph data
Vector->>FAISS: Index embeddings
par Read Operations
Graph->>RocksDB: Query relationships
Vector->>FAISS: Similarity search
end
Graph-->>API: Graph results
Vector-->>API: Vector results
API->>Cache: Store computed result
API-->>LB: Combined response
LB-->>Client: JSON response
end
else Token Invalid
Auth-->>API: ✗ Unauthorized
API-->>LB: 401 Error
LB-->>Client: Authentication error
end
Note over Parser,FAISS: Async processing continues
Note over Cache: TTL-based invalidation
```
## Code Parsing Pipeline
```mermaid
flowchart TD
subgraph "Input Sources"
Git[Git Repository]
Files[File System]
Archive[Archive Files]
Stream[Streaming Input]
end
subgraph "Pre-processing"
Filter[File Filter<br/>Language Detection]
Validate[Validation<br/>Size & Format Check]
Queue_Input[Input Queue<br/>Rate Limiting]
end
subgraph "Parsing Engine (Tree-sitter)"
TS_Rust[Rust Parser]
TS_Python[Python Parser]
TS_JS[JavaScript Parser]
TS_TS[TypeScript Parser]
TS_Go[Go Parser]
TS_Java[Java Parser]
TS_CPP[C++ Parser]
end
subgraph "AST Processing"
Visitor[AST Visitor<br/>Pattern Extraction]
Normalize[Normalization<br/>Language Agnostic]
Extract[Entity Extraction<br/>Functions, Classes, etc.]
end
subgraph "Parallel Output Streams"
GraphOut[Graph Nodes<br/>→ RocksDB]
VectorOut[Embeddings<br/>→ FAISS]
CacheOut[Hot Data<br/>→ Memory Cache]
IndexOut[Search Index<br/>→ Full-text]
end
%% Input flow
Git --> Filter
Files --> Filter
Archive --> Filter
Stream --> Filter
%% Pre-processing
Filter --> Validate
Validate --> Queue_Input
%% Language routing
Queue_Input --> TS_Rust
Queue_Input --> TS_Python
Queue_Input --> TS_JS
Queue_Input --> TS_TS
Queue_Input --> TS_Go
Queue_Input --> TS_Java
Queue_Input --> TS_CPP
%% AST processing
TS_Rust --> Visitor
TS_Python --> Visitor
TS_JS --> Visitor
TS_TS --> Visitor
TS_Go --> Visitor
TS_Java --> Visitor
TS_CPP --> Visitor
Visitor --> Normalize
Normalize --> Extract
%% Parallel outputs
Extract --> GraphOut
Extract --> VectorOut
Extract --> CacheOut
Extract --> IndexOut
%% Performance annotations
Filter -.->|"~1ms/file"| Validate
Validate -.->|"~500μs"| Queue_Input
Queue_Input -.->|"~50k files/min"| TS_Rust
Visitor -.->|"~10ms/1k LOC"| Normalize
Extract -.->|"~4 streams"| GraphOut
%% Styling
classDef input fill:#e3f2fd
classDef preprocess fill:#e8f5e8
classDef parser fill:#fff3e0
classDef ast fill:#f3e5f5
classDef output fill:#fce4ec
class Git,Files,Archive,Stream input
class Filter,Validate,Queue_Input preprocess
class TS_Rust,TS_Python,TS_JS,TS_TS,TS_Go,TS_Java,TS_CPP parser
class Visitor,Normalize,Extract ast
class GraphOut,VectorOut,CacheOut,IndexOut output
```
## Vector Search Performance Flow
```mermaid
graph TB
subgraph "Query Processing"
QueryIn[Search Query<br/>Text/Code]
Embedding[Query Embedding<br/>OpenAI/Local Model]
Normalize[Vector Normalization<br/>L2 Norm]
end
subgraph "Index Selection (FAISS)"
IndexFlat[IndexFlatL2<br/>Exact Search<br/>O(n)]
IndexIVF[IndexIVF<br/>Inverted File<br/>O(nprobe)]
IndexPQ[IndexPQ<br/>Product Quantization<br/>O(1) approx]
IndexHNSW[IndexHNSW<br/>Graph-based<br/>O(log n)]
end
subgraph "Search Strategy"
Strategy{Search Strategy}
Exact[Exact Search<br/>100% Recall]
Fast[Fast Search<br/>~95% Recall]
Approximate[Approximate<br/>~85% Recall]
end
subgraph "Post-processing"
Rerank[Re-ranking<br/>Graph Context]
Filter[Result Filtering<br/>Relevance Score]
Format[Response Formatting<br/>JSON/GraphQL]
end
subgraph "Performance Metrics"
Latency[Latency<br/>< 50ms p99]
Throughput[Throughput<br/>1000+ QPS]
Memory[Memory Usage<br/>< 500MB/1M vectors]
Accuracy[Accuracy<br/>85-100% recall]
end
%% Query flow
QueryIn --> Embedding
Embedding --> Normalize
%% Index routing
Normalize --> Strategy
Strategy -->|High Accuracy| Exact
Strategy -->|Balanced| Fast
Strategy -->|High Speed| Approximate
%% Index selection
Exact --> IndexFlat
Fast --> IndexIVF
Fast --> IndexHNSW
Approximate --> IndexPQ
%% Search execution
IndexFlat --> Rerank
IndexIVF --> Rerank
IndexPQ --> Rerank
IndexHNSW --> Rerank
%% Post-processing
Rerank --> Filter
Filter --> Format
%% Performance connections
IndexFlat -.-> Latency
IndexIVF -.-> Throughput
IndexPQ -.-> Memory
IndexHNSW -.-> Accuracy
%% Performance annotations
IndexFlat -.->|"~100ms/1M"| Rerank
IndexIVF -.->|"~10ms/1M"| Rerank
IndexPQ -.->|"~1ms/1M"| Rerank
IndexHNSW -.->|"~5ms/1M"| Rerank
%% Styling
classDef query fill:#e3f2fd
classDef index fill:#e8f5e8
classDef strategy fill:#fff3e0
classDef post fill:#f3e5f5
classDef metrics fill:#fce4ec
class QueryIn,Embedding,Normalize query
class IndexFlat,IndexIVF,IndexPQ,IndexHNSW index
class Strategy,Exact,Fast,Approximate strategy
class Rerank,Filter,Format post
class Latency,Throughput,Memory,Accuracy metrics
```
## Memory Management & Optimization
```mermaid
graph TB
subgraph "Memory Hierarchy"
CPU[CPU Cache<br/>L1/L2/L3]
RAM[System RAM<br/>Working Set]
SSD[NVMe SSD<br/>Persistent Data]
HDD[Network Storage<br/>Cold Data]
end
subgraph "Rust Memory Model"
Stack[Stack Memory<br/>Local Variables<br/>Function Calls]
Heap[Heap Memory<br/>Dynamic Allocation<br/>Vec, HashMap]
Static[Static Memory<br/>Global Constants<br/>Binary Data]
end
subgraph "Zero-Copy Optimizations"
RKYV[rkyv Archives<br/>Zero Deserialization]
MemMap[Memory Mapping<br/>mmap() Files]
Bytes[Bytes Crate<br/>Reference Counting]
Arc[Arc<T><br/>Shared Ownership]
end
subgraph "Cache Layers"
L1_Code[L1: Parsed AST<br/>Hot Code Objects]
L2_Vector[L2: Vector Cache<br/>Embedding Results]
L3_Graph[L3: Graph Cache<br/>Query Results]
L4_Disk[L4: Persistent<br/>RocksDB + FAISS]
end
subgraph "Memory Pools"
ParsePool[Parser Pool<br/>AST Node Reuse]
BufferPool[Buffer Pool<br/>I/O Buffer Reuse]
ObjectPool[Object Pool<br/>Heavy Object Reuse]
end
subgraph "Garbage Collection"
RefCount[Reference Counting<br/>Automatic Cleanup]
RAII[RAII Pattern<br/>Deterministic Cleanup]
WeakRef[Weak References<br/>Cycle Prevention]
end
%% Memory hierarchy flow
CPU --> RAM
RAM --> SSD
SSD --> HDD
%% Rust memory model
Stack -.->|"Fast allocation"| CPU
Heap -.->|"Heap allocation"| RAM
Static -.->|"Read-only"| RAM
%% Zero-copy optimizations
RKYV --> RAM
MemMap --> SSD
Bytes --> Heap
Arc --> Heap
%% Cache hierarchy
L1_Code --> CPU
L2_Vector --> RAM
L3_Graph --> SSD
L4_Disk --> HDD
%% Memory pools
ParsePool --> Heap
BufferPool --> Heap
ObjectPool --> Heap
%% Garbage collection
RefCount --> Heap
RAII --> Stack
WeakRef --> Heap
%% Performance annotations
CPU -.->|"~1ns access"| L1_Code
RAM -.->|"~100ns access"| L2_Vector
SSD -.->|"~100μs access"| L3_Graph
HDD -.->|"~10ms access"| L4_Disk
%% Optimization flows
RKYV -.->|"Zero copy"| L1_Code
MemMap -.->|"Virtual memory"| L3_Graph
ParsePool -.->|"Object reuse"| L1_Code
%% Styling
classDef hierarchy fill:#e3f2fd
classDef rust fill:#e8f5e8
classDef zerocopy fill:#fff3e0
classDef cache fill:#f3e5f5
classDef pool fill:#fce4ec
classDef gc fill:#e8f5e8
class CPU,RAM,SSD,HDD hierarchy
class Stack,Heap,Static rust
class RKYV,MemMap,Bytes,Arc zerocopy
class L1_Code,L2_Vector,L3_Graph,L4_Disk cache
class ParsePool,BufferPool,ObjectPool pool
class RefCount,RAII,WeakRef gc
```
## Concurrency & Parallelism Model
```mermaid
graph TB
subgraph "Tokio Runtime"
Executor[Async Executor<br/>Work-Stealing Scheduler]
IOReactor[I/O Reactor<br/>epoll/kqueue/IOCP]
Timer[Timer Wheel<br/>Timeout Management]
end
subgraph "Task Types"
CPUTask[CPU-bound Tasks<br/>Parsing, Analysis]
IOTask[I/O-bound Tasks<br/>Database, Network]
BlockingTask[Blocking Tasks<br/>File System, Sync APIs]
end
subgraph "Thread Pools"
MainPool[Main Pool<br/>8-16 threads<br/>async tasks]
RayonPool[Rayon Pool<br/>CPU cores<br/>parallel iterators]
BlockingPool[Blocking Pool<br/>512+ threads<br/>blocking operations]
end
subgraph "Synchronization Primitives"
DashMap[DashMap<br/>Concurrent HashMap<br/>Lock-free reads]
ArcSwap[ArcSwap<br/>Atomic Reference<br/>Lock-free updates]
ParkingLot[Parking Lot<br/>Efficient Mutexes<br/>Micro-park]
Crossbeam[Crossbeam<br/>MPMC Channels<br/>Wait-free queues]
end
subgraph "Work Distribution"
WorkStealing[Work Stealing<br/>Load Balancing]
Sharding[Data Sharding<br/>Partition by Hash]
Pipeline[Pipeline Pattern<br/>Producer-Consumer]
ForkJoin[Fork-Join<br/>Divide & Conquer]
end
subgraph "Performance Monitoring"
Metrics[Task Metrics<br/>Runtime Statistics]
Tracing[Async Tracing<br/>Span Tracking]
Profiling[CPU Profiling<br/>Flamegraph Generation]
end
%% Runtime components
Executor --> IOReactor
Executor --> Timer
%% Task routing
CPUTask --> MainPool
CPUTask --> RayonPool
IOTask --> MainPool
BlockingTask --> BlockingPool
%% Thread pool management
MainPool --> Executor
RayonPool -.->|"CPU-intensive"| WorkStealing
BlockingPool -.->|"Blocking ops"| Pipeline
%% Synchronization usage
DashMap --> MainPool
ArcSwap --> MainPool
ParkingLot --> RayonPool
Crossbeam --> BlockingPool
%% Work distribution patterns
WorkStealing --> RayonPool
Sharding --> DashMap
Pipeline --> Crossbeam
ForkJoin --> RayonPool
%% Performance monitoring
Metrics --> Executor
Tracing --> IOTask
Profiling --> CPUTask
%% Performance annotations
MainPool -.->|"8-16 threads"| Executor
RayonPool -.->|"CPU cores"| WorkStealing
BlockingPool -.->|"512+ threads"| Pipeline
DashMap -.->|"Lock-free"| MainPool
%% Styling
classDef runtime fill:#e3f2fd
classDef task fill:#e8f5e8
classDef pool fill:#fff3e0
classDef sync fill:#f3e5f5
classDef work fill:#fce4ec
classDef monitor fill:#e8f5e8
class Executor,IOReactor,Timer runtime
class CPUTask,IOTask,BlockingTask task
class MainPool,RayonPool,BlockingPool pool
class DashMap,ArcSwap,ParkingLot,Crossbeam sync
class WorkStealing,Sharding,Pipeline,ForkJoin work
class Metrics,Tracing,Profiling monitor
```
## Real-time Performance Monitoring
```mermaid
graph TB
subgraph "Application Metrics"
QPS[Queries Per Second<br/>Request Rate]
Latency[Response Latency<br/>p50, p95, p99]
ErrorRate[Error Rate<br/>4xx, 5xx responses]
Throughput[Data Throughput<br/>MB/s processed]
end
subgraph "System Metrics"
CPU[CPU Usage<br/>Per-core utilization]
Memory[Memory Usage<br/>RSS, heap, cache]
Disk[Disk I/O<br/>Read/write IOPS]
Network[Network I/O<br/>Bandwidth usage]
end
subgraph "Database Metrics"
RocksStats[RocksDB Stats<br/>Read/write amplification]
CacheHit[Cache Hit Rate<br/>L1, L2, L3 layers]
CompactionLag[Compaction Lag<br/>Background tasks]
BloomFilter[Bloom Filter<br/>False positive rate]
end
subgraph "Vector Engine Metrics"
IndexSize[Index Size<br/>Memory footprint]
SearchTime[Search Time<br/>Query latency]
RecallRate[Recall Rate<br/>Search accuracy]
UpdateRate[Update Rate<br/>Index rebuilds]
end
subgraph "Custom Metrics"
ParseTime[Parse Time<br/>Per file/project]
QueueDepth[Queue Depth<br/>Pending tasks]
ConcurrentUsers[Concurrent Users<br/>Active connections]
ResourceUsage[Resource Usage<br/>Per-tenant metrics]
end
subgraph "Alerting Rules"
HighLatency[High Latency<br/>> 100ms p95]
HighErrorRate[High Error Rate<br/>> 1% errors]
HighMemory[High Memory<br/>> 80% usage]
DiskFull[Disk Full<br/>> 90% usage]
end
subgraph "Monitoring Stack"
Prometheus[Prometheus<br/>Metrics Collection]
Grafana[Grafana<br/>Visualization]
AlertManager[AlertManager<br/>Notification routing]
Jaeger[Jaeger<br/>Distributed tracing]
end
%% Metric collection
QPS --> Prometheus
Latency --> Prometheus
ErrorRate --> Prometheus
Throughput --> Prometheus
CPU --> Prometheus
Memory --> Prometheus
Disk --> Prometheus
Network --> Prometheus
RocksStats --> Prometheus
CacheHit --> Prometheus
CompactionLag --> Prometheus
BloomFilter --> Prometheus
IndexSize --> Prometheus
SearchTime --> Prometheus
RecallRate --> Prometheus
UpdateRate --> Prometheus
ParseTime --> Prometheus
QueueDepth --> Prometheus
ConcurrentUsers --> Prometheus
ResourceUsage --> Prometheus
%% Alerting
Prometheus --> HighLatency
Prometheus --> HighErrorRate
Prometheus --> HighMemory
Prometheus --> DiskFull
%% Monitoring stack
Prometheus --> Grafana
Prometheus --> AlertManager
HighLatency --> AlertManager
HighErrorRate --> AlertManager
HighMemory --> AlertManager
DiskFull --> AlertManager
%% Distributed tracing
QPS --> Jaeger
Latency --> Jaeger
%% Performance thresholds
QPS -.->|"Target: 1000+ QPS"| Prometheus
Latency -.->|"Target: <50ms p99"| Prometheus
CacheHit -.->|"Target: >95%"| Prometheus
SearchTime -.->|"Target: <10ms"| Prometheus
%% Styling
classDef app fill:#e3f2fd
classDef system fill:#e8f5e8
classDef database fill:#fff3e0
classDef vector fill:#f3e5f5
classDef custom fill:#fce4ec
classDef alert fill:#ffebee
classDef monitor fill:#e8f5e8
class QPS,Latency,ErrorRate,Throughput app
class CPU,Memory,Disk,Network system
class RocksStats,CacheHit,CompactionLag,BloomFilter database
class IndexSize,SearchTime,RecallRate,UpdateRate vector
class ParseTime,QueueDepth,ConcurrentUsers,ResourceUsage custom
class HighLatency,HighErrorRate,HighMemory,DiskFull alert
class Prometheus,Grafana,AlertManager,Jaeger monitor
```
## Deployment Performance Profile
```ascii
╔══════════════════════════════════════════════════════════════════════════════════════════════════════════════╗
║ CodeGraph Performance Profiles ║
╠══════════════════════════════════════════════════════════════════════════════════════════════════════════════╣
║ ║
║ ┌─────────────────────────────────────────────────────────────────────────────────────────────────────┐ ║
║ │ Deployment Configurations │ ║
║ └─────────────────────────────────────────────────────────────────────────────────────────────────────┘ ║
║ ║
║ 🔹 DEVELOPMENT (Local) 🔹 STAGING (Single Node) 🔹 PRODUCTION (Cluster) ║
║ ║
║ CPU: 4-8 cores CPU: 8-16 cores CPU: 32+ cores/node ║
║ RAM: 8-16 GB RAM: 32-64 GB RAM: 128+ GB/node ║
║ Storage: 100 GB SSD Storage: 500 GB NVMe Storage: 2+ TB NVMe ║
║ Network: Local Network: 1 Gbps Network: 10+ Gbps ║
║ ║
║ Performance Targets: Performance Targets: Performance Targets: ║
║ • QPS: 10-50 • QPS: 100-500 • QPS: 1000-10000 ║
║ • Latency: < 100ms • Latency: < 75ms • Latency: < 50ms ║
║ • Projects: 1-5 • Projects: 10-50 • Projects: 100-1000 ║
║ • Users: 1-3 • Users: 5-25 • Users: 100-10000 ║
║ ║
║ ┌─────────────────────────────────────────────────────────────────────────────────────────────────────┐ ║
║ │ Performance Optimization Matrix │ ║
║ ├─────────────────────────────────────────────────────────────────────────────────────────────────────┤ ║
║ │ Component │ Development │ Staging │ Production │ Optimization Strategy │ ║
║ ├─────────────────────────────────────────────────────────────────────────────────────────────────────┤ ║
║ │ API Server │ 1 instance │ 2 instances │ 3-10 instances│ Auto-scaling, load balancing │ ║
║ │ Parser Workers │ 2 threads │ 8 threads │ 32+ threads │ Multi-threading, work stealing │ ║
║ │ RocksDB │ Default │ Tuned │ Optimized │ Column families, bloom filters │ ║
║ │ FAISS Index │ Flat │ IVF │ HNSW/PQ │ Index type selection │ ║
║ │ Memory Cache │ 512 MB │ 4 GB │ 32+ GB │ Cache layers, TTL policies │ ║
║ │ Network I/O │ HTTP/1.1 │ HTTP/2 │ HTTP/2+gRPC │ Protocol optimization │ ║
║ │ Observability │ Basic logs │ Metrics │ Full tracing │ Prometheus, Grafana, Jaeger │ ║
║ └─────────────────────────────────────────────────────────────────────────────────────────────────────┘ ║
║ ║
║ ┌─────────────────────────────────────────────────────────────────────────────────────────────────────┐ ║
║ │ Scaling Strategies │ ║
║ ├─────────────────────────────────────────────────────────────────────────────────────────────────────┤ ║
║ │ Horizontal Scaling: │ ║
║ │ • API Layer: Load balancer + multiple instances │ ║
║ │ • Processing: Distributed task queue across nodes │ ║
║ │ • Storage: Sharded RocksDB instances │ ║
║ │ • Vector Search: Distributed FAISS indices │ ║
║ │ │ ║
║ │ Vertical Scaling: │ ║
║ │ • CPU: More cores for parsing and vector operations │ ║
║ │ • Memory: Larger caches and in-memory indices │ ║
║ │ • Storage: Faster NVMe for better I/O performance │ ║
║ │ • Network: Higher bandwidth for distributed operations │ ║
║ │ │ ║
║ │ Auto-scaling Triggers: │ ║
║ │ • CPU utilization > 70% for 5 minutes │ ║
║ │ • Queue depth > 1000 pending tasks │ ║
║ │ • Response latency > 100ms p95 for 2 minutes │ ║
║ │ • Memory usage > 80% for 5 minutes │ ║
║ └─────────────────────────────────────────────────────────────────────────────────────────────────────┘ ║
║ ║
║ ┌─────────────────────────────────────────────────────────────────────────────────────────────────────┐ ║
║ │ Bottleneck Analysis │ ║
║ ├─────────────────────────────────────────────────────────────────────────────────────────────────────┤ ║
║ │ Common Bottlenecks: │ Mitigation Strategies: │ ║
║ │ │ │ ║
║ │ 1. Parser CPU Usage (Tree-sitter intensive) │ • Multi-threading with rayon │ ║
║ │ • Large codebases overwhelm single thread │ • File-level parallelism │ ║
║ │ • Complex language grammars slow parsing │ • Incremental parsing │ ║
║ │ │ • Parser result caching │ ║
║ │ 2. Vector Search Latency (FAISS queries) │ • Index optimization (HNSW/PQ) │ ║
║ │ • Large embedding spaces (>1M vectors) │ • Query batching │ ║
║ │ • High-dimensional vectors (768/1536 dims) │ • Approximate search │ ║
║ │ │ • Multi-level caching │ ║
║ │ 3. Database I/O (RocksDB operations) │ • SST file optimization │ ║
║ │ • Write amplification during bulk inserts │ • Bloom filter tuning │ ║
║ │ • Compaction lag during heavy writes │ • Background compaction threads │ ║
║ │ │ • Write buffer optimization │ ║
║ │ 4. Memory Pressure (Large projects) │ • Memory mapping for large files │ ║
║ │ • AST nodes consume significant memory │ • Lazy loading strategies │ ║
║ │ • Vector embeddings cache bloat │ • Memory pool management │ ║
║ │ │ • Garbage collection tuning │ ║
║ └─────────────────────────────────────────────────────────────────────────────────────────────────────┘ ║
╚══════════════════════════════════════════════════════════════════════════════════════════════════════════════╝
```
---
*Generated by CodeGraph Documentation Specialist - Data Flow & Performance Analysis*