Summary
Use Dapr State Management API for caching frequently accessed data and session state, reducing database load and improving response times.
Current State
- All state reads go directly to PostgreSQL
- No caching layer for hot data
- Session/conversation state stored in DB
- Every request hits the database
Proposed Architecture
┌─────────────┐ ┌─────────────┐ ┌─────────────────┐
│ API Service │────►│ Dapr │────►│ State Store │
│ │ │ Sidecar │ │ (Redis/Postgres)│
│ │ │ :3500 │ │ │
└─────────────┘ └─────────────┘ └─────────────────┘
# Instead of:
task = await session.get(Task, task_id)
# Use Dapr State for hot data:
task = await dapr_client.get_state("statestore", f"task-{task_id}")
Use Cases
- User Session Cache — Active user data, permissions
- Project Membership — Frequently checked access control
- Task Count/Stats — Dashboard metrics
- Conversation State — Chat history for MCP
- Rate Limiting — Request counters per user
Implementation Steps
Acceptance Criteria
Technical Notes
# Dapr State API example
from dapr.clients import DaprClient
async with DaprClient() as client:
# Save state
await client.save_state("statestore", key, value, state_metadata={"ttlInSeconds": "300"})
# Get state
state = await client.get_state("statestore", key)
# Delete state
await client.delete_state("statestore", key)
Labels
enhancement learning dapr performance phase-v2
📚 Learning Goal: Dapr State Management Building Block
Summary
Use Dapr State Management API for caching frequently accessed data and session state, reducing database load and improving response times.
Current State
Proposed Architecture
Use Cases
Implementation Steps
dapr_state.pyutility moduleAcceptance Criteria
Technical Notes
Labels
enhancementlearningdaprperformancephase-v2📚 Learning Goal: Dapr State Management Building Block