System Architecture

System architecture encompasses the fundamental structural patterns, design decisions, and organizational principles that define how Geode’s components interact, scale, and evolve. A well-designed architecture ensures maintainability, performance, and extensibility.

Geode’s Architectural Principles

Layered Architecture

Geode implements a clean separation of concerns across multiple layers:

Presentation Layer - Protocol handlers and client interfaces Application Layer - Query processing and transaction management Domain Layer - Graph model and GQL semantics Infrastructure Layer - Storage, networking, and system services

This layered approach enables independent evolution of components while maintaining clear boundaries and dependencies.

Component-Based Design

Geode’s architecture is built around well-defined, loosely coupled components:

  • Query Engine - Parses, optimizes, and executes GQL queries
  • Storage Engine - Manages persistent graph data structures
  • Transaction Manager - Ensures ACID properties
  • Security Manager - Enforces authentication and authorization
  • Network Layer - Handles QUIC/TLS communication

Each component has clearly defined interfaces, making the system modular and testable.

Core Architectural Patterns

Event-Driven Architecture

Geode uses event-driven patterns for asynchronous operations:

// Change Data Capture triggers events
CREATE TRIGGER user_changes
ON GRAPH social
FOR INSERT, UPDATE, DELETE
EXECUTE NOTIFY 'user_events';

Events flow through the system without tight coupling between producers and consumers.

Command Query Responsibility Segregation (CQRS)

Geode separates read and write operations:

Write Path - Optimized for consistency and durability Read Path - Optimized for query performance and caching

This separation allows independent scaling and optimization of each path.

Repository Pattern

Data access is abstracted through repository interfaces:

pub const GraphRepository = struct {
    pub fn findNodes(self: *Self, pattern: NodePattern) ![]Node
    pub fn createNode(self: *Self, labels: []Label, props: Properties) !Node
    pub fn deleteNode(self: *Self, id: NodeId) !void
};

Repositories provide a clean abstraction over storage details.

Distributed Architecture

Sharding Strategy

Geode supports horizontal partitioning of graph data:

Hash-Based Sharding - Distribute nodes by property hash Range-Based Sharding - Partition by property ranges Graph-Aware Sharding - Co-locate connected subgraphs

-- Configure sharding for a graph property type
CREATE GRAPH TYPE social_network (
    Person LABEL (
        id STRING SHARD KEY,
        created DATETIME
    )
) SHARD BY HASH(Person.id) SHARDS 16;

Replication Model

Geode implements multi-master replication:

Synchronous Replication - Strong consistency for critical data Asynchronous Replication - Higher throughput for read replicas Conflict Resolution - Last-write-wins with vector clocks

Service Mesh Integration

For distributed deployments, Geode integrates with service meshes:

  • Load Balancing - Client-side and server-side balancing
  • Service Discovery - Dynamic endpoint registration
  • Circuit Breaking - Fault isolation and recovery
  • Observability - Distributed tracing and metrics

Storage Architecture

Multi-Layer Storage

Geode implements a tiered storage architecture:

Hot Tier - In-memory cache for active data Warm Tier - SSD storage for recent data Cold Tier - Object storage for archival data

Data moves between tiers based on access patterns and policies.

Write-Ahead Log (WAL)

All mutations are logged before execution:

WAL Entry:
- Transaction ID
- Operation Type (INSERT/UPDATE/DELETE)
- Before Image (for rollback)
- After Image (for replay)
- Timestamp
- Checksum

The WAL enables crash recovery and replication.

Index Architecture

Geode maintains multiple index types:

Primary Index - B-tree for node/edge lookup by ID Property Index - Hash/B-tree for property-based queries Full-Text Index - Inverted index for text search Spatial Index - R-tree for geospatial queries

Indexes are automatically maintained as data changes.

Query Processing Architecture

Query Pipeline

Queries flow through a multi-stage pipeline:

  1. Parsing - Convert GQL text to AST
  2. Validation - Check syntax and semantics
  3. Planning - Generate logical query plan
  4. Optimization - Apply cost-based transformations
  5. Execution - Run optimized physical plan
  6. Result Formatting - Convert to wire format

Each stage is independently testable and optimizable.

Execution Engine

Geode uses a vectorized execution model:

Batch Processing - Process rows in batches (1000-10000) Column-Oriented - Operate on columns for better cache locality SIMD Operations - Leverage CPU vector instructions

This architecture maximizes throughput for analytical queries.

Caching Strategy

Multi-level caching improves performance:

Query Result Cache - Cache complete query results Plan Cache - Reuse compiled query plans Metadata Cache - Cache schema and statistics Data Cache - Cache frequently accessed nodes/edges

Caches use LRU eviction with size and TTL limits.

Security Architecture

Defense in Depth

Geode implements multiple security layers:

  1. Network Security - TLS 1.3 encryption, mutual authentication
  2. Authentication - JWT tokens, certificate-based auth
  3. Authorization - Role-based and attribute-based access control
  4. Data Security - Encryption at rest, secure key management
  5. Audit Logging - Comprehensive audit trail

Secure by Default

Security features are enabled by default:

  • TLS required for all connections
  • Strong password policies enforced
  • Minimal default privileges
  • Audit logging always on
  • Encryption at rest enabled

Monitoring Architecture

Metrics Collection

Geode exposes comprehensive metrics:

-- Query system metrics
SELECT *
FROM SYSTEM.metrics
WHERE component = 'query_engine'
  AND timestamp > NOW() - INTERVAL '1 hour';

Metrics include:

  • Query latency percentiles (p50, p95, p99)
  • Throughput (queries/second)
  • Resource utilization (CPU, memory, disk)
  • Error rates by type

Distributed Tracing

Every request is traced end-to-end:

Trace Context - Propagated across service boundaries Span Details - Timing, tags, logs for each operation Sampling - Configurable sampling rates

Traces integrate with OpenTelemetry-compatible systems.

Scalability Architecture

Horizontal Scaling

Geode scales out by adding nodes:

Read Scaling - Add read replicas for query workloads Write Scaling - Shard data across multiple masters Compute Scaling - Add query execution nodes

Scaling is transparent to clients.

Vertical Scaling

Geode efficiently uses resources on larger machines:

Parallel Execution - Leverage all CPU cores Memory Management - Efficient allocation and caching Disk I/O - Asynchronous, batched operations

Best Practices

Architectural Decisions

Document significant architectural choices:

# ADR 001: Choice of QUIC Protocol

## Context
Need a modern, performant protocol for client-server communication.

## Decision
Use QUIC over TCP for lower latency and better multiplexing.

## Consequences
- Requires TLS 1.3
- Better performance under packet loss
- Reduced head-of-line blocking

Component Boundaries

Define clear interfaces between components:

// Well-defined component interface
pub const StorageEngine = struct {
    pub const Interface = struct {
        allocator: Allocator,
        vtable: *const VTable,

        pub const VTable = struct {
            read: *const fn(*Interface, NodeId) !Node,
            write: *const fn(*Interface, Node) !void,
        };
    };
};

Dependency Management

Follow the dependency inversion principle:

  • High-level modules don’t depend on low-level modules
  • Both depend on abstractions
  • Abstractions don’t depend on details

Testing Strategy

Test at multiple architectural levels:

Unit Tests - Individual component behavior Integration Tests - Component interactions System Tests - End-to-end scenarios Performance Tests - Scalability and throughput

Performance Considerations

Latency Optimization

Minimize request latency through:

  • Connection pooling and reuse
  • Query plan caching
  • Strategic data denormalization
  • Asynchronous processing

Throughput Optimization

Maximize system throughput via:

  • Batch processing
  • Parallel execution
  • Efficient serialization
  • Resource pooling

Resource Management

Manage system resources effectively:

  • Memory pooling and recycling
  • Connection limits and backpressure
  • Disk I/O scheduling
  • CPU affinity for critical threads

Migration Patterns

Zero-Downtime Deployments

Deploy new versions without service interruption:

  1. Rolling Updates - Gradually replace instances
  2. Blue-Green Deployment - Switch traffic between environments
  3. Canary Releases - Gradually increase traffic to new version

Schema Evolution

Evolve schemas without breaking existing clients:

  • Additive changes (new optional fields)
  • Backward-compatible transformations
  • Versioned APIs with deprecation cycles

Learn More

Advanced Architectural Patterns

Microservices Integration

Integrate Geode into microservices architecture:

# Docker Compose for microservices + Geode
version: '3.8'

services:
  geode:
    image: geodedb/geode:latest
    ports:
      - "3141:3141"
    environment:
      - GEODE_CLUSTER_MODE=standalone
      - GEODE_MEMORY_LIMIT=4G
    volumes:
      - geode-data:/data
    networks:
      - backend

  api-gateway:
    image: myapp/api-gateway
    ports:
      - "8080:8080"
    environment:
      - GEODE_URL=geode:3141
    depends_on:
      - geode
    networks:
      - backend
      - frontend

  user-service:
    image: myapp/user-service
    environment:
      - GEODE_URL=geode:3141
    depends_on:
      - geode
    networks:
      - backend

  product-service:
    image: myapp/product-service
    environment:
      - GEODE_URL=geode:3141
    depends_on:
      - geode
    networks:
      - backend

networks:
  frontend:
  backend:

volumes:
  geode-data:

Event-Driven Microservices

Use Geode with event streaming:

from geode_client import Client, open_database
from kafka import KafkaProducer, KafkaConsumer

class EventDrivenService:
    def __init__(self, geode_url, kafka_brokers):
        self.geode = open_database(geode_url)
        self.producer = KafkaProducer(bootstrap_servers=kafka_brokers)
        self.consumer = KafkaConsumer('geode-events', bootstrap_servers=kafka_brokers)
    
    async def process_events(self):
        """Process events from Kafka and update Geode"""
        async with self.geode.connection() as conn:
            async for message in self.consumer:
                event = json.loads(message.value)

                # Update graph based on event
                if event['type'] == 'UserCreated':
                    await conn.execute("""
                        CREATE (u:User {
                            id: $id,
                            name: $name,
                            created_at: datetime()
                        })
                    """, event['data'])

                elif event['type'] == 'FriendshipCreated':
                    await conn.execute("""
                        MATCH (a:User {id: $user1}), (b:User {id: $user2})
                        CREATE (a)-[:FRIENDS_WITH {since: datetime()}]->(b)
                    """, event['data'])
    
    async def publish_graph_changes(self):
        """Publish Geode changes to Kafka"""
        last_seen = "1970-01-01T00:00:00Z"
        async with self.geode.connection() as conn:
            while True:
                result, _ = await conn.query(
                    """
                    MATCH (e:ChangeLog)
                    WHERE e.emitted_at > $since
                    RETURN e.operation AS operation,
                           e.entity_type AS entity_type,
                           e.after AS after,
                           e.emitted_at AS emitted_at
                    ORDER BY emitted_at
                    """,
                    {"since": last_seen},
                )
                for row in result.rows:
                    self.producer.send('graph-changes', json.dumps({
                        'type': row["operation"].raw_value,
                        'entity': row["entity_type"].raw_value,
                        'data': row["after"].raw_value,
                        'timestamp': row["emitted_at"].raw_value
                    }).encode())
                    last_seen = row["emitted_at"].raw_value

API Gateway Pattern

Centralized API layer for Geode:

from fastapi import FastAPI, HTTPException, Depends
from geode_client import Client
from pydantic import BaseModel

app = FastAPI()

class UserCreate(BaseModel):
    name: str
    email: str

class FriendshipCreate(BaseModel):
    user1_id: str
    user2_id: str

async def get_geode_client():
    client = Client(host="geode", port=3141)
    async with client.connection() as conn:
        yield client

@app.post("/users")
async def create_user(user: UserCreate, client=Depends(get_geode_client)):
    """Create new user"""
    result, _ = await conn.query("""
        CREATE (u:User {
            id: randomUUID(),
            name: $name,
            email: $email,
            created_at: datetime()
        })
        RETURN u.id as user_id
    """, {"name": user.name, "email": user.email})
    
    return {"user_id": result.rows[0]['user_id']}

@app.get("/users/{user_id}/friends")
async def get_friends(user_id: str, client=Depends(get_geode_client)):
    """Get user's friends"""
    result, _ = await conn.query("""
        MATCH (u:User {id: $id})-[:FRIENDS_WITH]-(friend:User)
        RETURN friend.id, friend.name, friend.email
    """, {"id": user_id})
    
    return {"friends": [dict(r) for r in result]}

@app.post("/friendships")
async def create_friendship(
    friendship: FriendshipCreate,
    client=Depends(get_geode_client)
):
    """Create friendship between users"""
    await conn.execute("""
        MATCH (a:User {id: $user1}), (b:User {id: $user2})
        CREATE (a)-[:FRIENDS_WITH {since: datetime()}]->(b)
    """, {"user1": friendship.user1_id, "user2": friendship.user2_id})
    
    return {"status": "created"}

Performance Architecture

Caching Layers

Multi-tier caching strategy:

from redis import Redis
from functools import wraps

class GeodeCacheLayer:
    def __init__(self, geode_client, redis_client):
        self.geode = geode_client
        self.redis = redis_client
    
    def cached_query(self, ttl=300):
        """Decorator for query result caching"""
        def decorator(func):
            @wraps(func)
            async def wrapper(*args, **kwargs):
                # Generate cache key
                cache_key = f"{func.__name__}:{hash((args, tuple(kwargs.items())))}"
                
                # Check cache
                cached = self.redis.get(cache_key)
                if cached:
                    return json.loads(cached)
                
                # Execute query
                result = await func(*args, **kwargs)
                
                # Store in cache
                self.redis.setex(cache_key, ttl, json.dumps(result))
                
                return result
            return wrapper
        return decorator
    
    @cached_query(ttl=60)
    async def get_user_friends_count(self, user_id):
        """Get friend count with caching"""
        result = await self.geode.execute("""
            MATCH (u:User {id: $id})-[:FRIENDS_WITH]-(friend)
            RETURN COUNT(friend) as count
        """, {"id": user_id})
        return result.rows[0]['count']

Query Result Streaming

Memory-efficient large result handling:

async def stream_large_dataset(client, batch_size=1000):
    """Stream large result sets without loading into memory"""
    offset = 0
    
    while True:
        batch, _ = await client.query("""
            MATCH (n:Node)
            RETURN n
            ORDER BY n.id
            SKIP $offset
            LIMIT $limit
        """, {"offset": offset, "limit": batch_size})
        
        if not batch:
            break
        
        # Process batch
        for row in batch:
            yield row
        
        offset += batch_size

# Usage
async for node in stream_large_dataset(client):
    process_node(node)

Partitioning Strategy

Horizontal scaling through sharding:

class ShardedClient:
    def __init__(self, shard_configs):
        self.shards = [
            Client(config['url'])
            for config in shard_configs
        ]
    
    def get_shard(self, key):
        """Determine shard for key using consistent hashing"""
        shard_index = hash(key) % len(self.shards)
        return self.shards[shard_index]
    
    async def get_user(self, user_id):
        """Get user from appropriate shard"""
        shard = self.get_shard(user_id)
        result, _ = await shard.query("""
            MATCH (u:User {id: $id})
            RETURN u
        """, {"id": user_id})
        return result.rows[0]['u'] if result else None
    
    async def create_user(self, user_id, name, email):
        """Create user in appropriate shard"""
        shard = self.get_shard(user_id)
        await shard.execute("""
            CREATE (u:User {
                id: $id,
                name: $name,
                email: $email
            })
        """, {"id": user_id, "name": name, "email": email})

Deployment Architectures

Kubernetes Deployment

Production-grade Kubernetes configuration:

# geode-statefulset.yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: geode
spec:
  serviceName: geode
  replicas: 3
  selector:
    matchLabels:
      app: geode
  template:
    metadata:
      labels:
        app: geode
    spec:
      containers:
      - name: geode
        image: geodedb/geode:0.1.3
        ports:
        - containerPort: 3141
          name: client
        - containerPort: 7687
          name: cluster
        env:
        - name: GEODE_CLUSTER_MODE
          value: "cluster"
        - name: GEODE_NODE_ID
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        - name: GEODE_CLUSTER_PEERS
          value: "geode-0.geode:7687,geode-1.geode:7687,geode-2.geode:7687"
        volumeMounts:
        - name: data
          mountPath: /data
        resources:
          requests:
            memory: "4Gi"
            cpu: "2"
          limits:
            memory: "8Gi"
            cpu: "4"
        livenessProbe:
          httpGet:
            path: /health
            port: 3141
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 3141
          initialDelaySeconds: 10
          periodSeconds: 5
  volumeClaimTemplates:
  - metadata:
      name: data
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 100Gi
      storageClassName: fast-ssd

Serverless Architecture

Run Geode queries from serverless functions:

# AWS Lambda handler
import json
from geode_client import Client

# Reuse connection across invocations
geode_client = None

def lambda_handler(event, context):
    global geode_client
    
    # Initialize connection (reused across warm starts)
    if geode_client is None:
        geode_client = Client(
            os.environ['GEODE_URL'],
            connection_pool_size=1
        )
    
    # Extract query from event
    query = event['query']
    params = event.get('params', {})
    
    # Execute query
    result = geode_client.execute(query, params)
    
    return {
        'statusCode': 200,
        'body': json.dumps({
            'results': [dict(r) for r in result]
        })
    }

Security Architecture Patterns

Zero-Trust Security

Implement defense in depth:

class SecureClient:
    def __init__(self, url, cert_file, key_file):
        host, port = url.split(":")
        self.client = Client(
            host=host,
            port=int(port),
            ca_cert="/etc/geode/ca.crt",
            client_cert=cert_file,
            client_key=key_file,
        )
    
    async def execute_with_rls(self, query, params, user_context):
        """Execute query with row-level security"""
        async with self.client.connection() as conn:
            await conn.begin()
            try:
                # Set user context for RLS
                await conn.execute(
                    """
                    SET app.user_id = $user_id;
                    SET app.tenant_id = $tenant_id;
                    SET app.roles = $roles;
                    """,
                    {
                        "user_id": user_context["user_id"],
                        "tenant_id": user_context["tenant_id"],
                        "roles": json.dumps(user_context["roles"]),
                    },
                )

                # Execute query (RLS policies auto-applied)
                result, _ = await conn.query(query, params)
                await conn.commit()
                return result.rows
            except Exception:
                await conn.rollback()
                raise

Audit Logging Architecture

Comprehensive audit trail:

class AuditedClient:
    def __init__(self, client, audit_logger):
        self.client = client
        self.audit = audit_logger
    
    async def execute(self, query, params=None, user_id=None):
        """Execute query with audit logging"""
        start_time = time.time()
        
        try:
            result, _ = await self.client.query(query, params)
            
            # Log successful execution
            await self.audit.log({
                'timestamp': datetime.now().isoformat(),
                'user_id': user_id,
                'query': query,
                'params': params,
                'status': 'success',
                'duration_ms': (time.time() - start_time) * 1000,
                'rows_returned': len(result.rows)
            })
            
            return result
            
        except Exception as e:
            # Log failed execution
            await self.audit.log({
                'timestamp': datetime.now().isoformat(),
                'user_id': user_id,
                'query': query,
                'params': params,
                'status': 'error',
                'error': str(e),
                'duration_ms': (time.time() - start_time) * 1000
            })
            raise

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