Proposed
CodeFlow requires reliable data storage for:
- Repository configurations
- User preferences
- Execution history
- Cached analysis results
- Plugin data
We will implement a layered persistence strategy with the following components:
graph TD
A[In-Memory Cache] -->|Fallback| B[Local Disk]
B -->|Replicate| C[Object Storage]
C -->|Archive| D[Cold Storage]
interface DataStore<T> {
get(id: string): Promise<T | null>;
set(id: string, data: T): Promise<void>;
delete(id: string): Promise<void>;
query(filter: Partial<T>): Promise<T[]>;
exists(id: string): Promise<boolean>;
}- SQLite for structured data
- JSON files for configurations
- In-memory cache with TTL
- PostgreSQL for transactional data
- Redis for caching and pub/sub
- S3-compatible storage for large blobs
interface RepositoryConfig {
id: string;
name: string;
settings: {
autoMerge: boolean;
requiredChecks: string[];
branchProtection: BranchProtectionRules;
};
createdAt: Date;
updatedAt: Date;
}
interface ExecutionLog {
id: string;
repositoryId: string;
action: string;
status: "pending" | "running" | "completed" | "failed";
startedAt: Date;
completedAt?: Date;
metadata: Record<string, unknown>;
error?: {
message: string;
stack?: string;
code?: string;
};
}-
Versioned Migrations
# Example migration file: 20230101000000_initial_schema.sql CREATE TABLE IF NOT EXISTS repositories ( id TEXT PRIMARY KEY, name TEXT NOT NULL, settings JSONB NOT NULL, created_at TIMESTAMP WITH TIME ZONE NOT NULL, updated_at TIMESTAMP WITH TIME ZONE NOT NULL );
-
Rollback Plan
- Automated backups before migrations
- Blue/green deployment support
- Point-in-time recovery
- Data durability
- Performance optimization
- Scalability
- Disaster recovery
- Storage costs
- Operational complexity
- Learning curve
- Backup strategy required
- Monitoring needs