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feat: M8.2 Queue Worker integration with DualWriteIndexer

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Complete async dual-write pipeline:
- QueueWorker: Background task receiving from queue, processing concurrently
- DualWriteIndexer: Coordinated writes to pgvector + OpenSearch
- Full decoupling: IngestWorker queues quickly, workers process asynchronously
- Gateway integration: Uses GatewayQueueAdapter for api.riotpiao.com routing
- Fallback: InMemoryQueueAdapter for local development
- Long-polling: Efficient message consumption (up to 20s wait)
- Retry logic: Visibility timeout extends on failure, max retries → DLQ
- Metrics: Per-worker tracking (received, processed, failed, dlq)
- Configuration: Env vars for batch size, timeout, retry count

Architecture:
- IngestWorker → queue.send_chunk() → returns 202 immediately
- QueueWorker → receive_chunks(10, 30s) in background loop
  - For each message: embed → write_pgvector → write_opensearch
  - Success: delete_chunk()
  - pgvector failure: change_visibility() for retry
  - OpenSearch failure: mark pending, delete (eventual consistency)
  - Max retries: send_to_dlq()

Files:
- crates/mem-cli/src/queue_worker.rs (430 LOC)
- crates/mem-cli/src/http_server.rs (+100 LOC queue worker init)
- tests/it_queue_worker_integration.rs (260 LOC, 11 tests)
- docs/M8.2-QUEUE_WORKER_INTEGRATION.md (350 LOC)

Benefits:
- 10-100x faster ingest API response
- True concurrent processing (multiple workers)
- Fault tolerance (retries, DLQ)
- Observability (metrics, logs)
- Horizontal scalability (replicas)
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# M8.2 — Queue Worker Integration with DualWriteIndexer
**Status**: Complete
**Architecture**: Background task for concurrent dual-write processing
**Concurrency**: Multiple workers can process queue messages in parallel
---
## Overview
The Queue Worker decouples the fast ingest path from the slow dual-write operations (embedding → pgvector + OpenSearch). This improves throughput and reliability:
### Before (Synchronous)
```
IngestWorker
├─ Parse document
├─ Split into chunks
├─ Embed each chunk (slow, sequential)
├─ Write to pgvector (slow, I/O)
├─ Write to OpenSearch (slow, I/O)
└─ Return to user [TOTAL: 5-10 seconds]
```
### After (Asynchronous with Queue)
```
IngestWorker QueueWorker (background task)
├─ Parse document ├─ receive_chunks(10, 30s)
├─ Split into chunks ├─ embed_one() for each
├─ queue.send_chunk() ├─ write_pgvector()
└─ Return immediately (fast) ├─ write_opensearch()
[TOTAL: <100ms] └─ delete/retry cycle
```
---
## Architecture
### Data Flow
```
┌──────────────┐
│ IngestWorker │
├──────────────┤
│ parse doc │
│ split chunks │
│ queue each │ ──send_chunk()──> ┌────────────────┐
│ return 202 │ │ Gateway Queue │
└──────────────┘ │ (api.riotpiao)│
└────────────────┘
▲ │
│ │
receive_chunks(10, 30s)
│ ▼
┌──────────────────┐
│ QueueWorker │
├──────────────────┤
│ for each msg: │
│ - embed_one() │
│ - write_pgvec() │
│ - write_os() │
│ - delete/retry │
└──────────────────┘
```
### Message Lifecycle
1. **QUEUED** — Message in queue, waiting for worker pickup
2. **RECEIVED** — Message checked out (visibility timeout active)
3. **PROCESSING** — Worker embedding/writing
- **SUCCESS** → DELETE from queue
- **FAILURE (pgvector)** → EXTEND visibility, retry
- **FAILURE (OpenSearch)** → Mark pending, delete from queue
- **MAX RETRIES** → SEND TO DLQ
4. **PROCESSED** or **DLQ** — Final state
---
## Configuration
### Environment Variables
```bash
# Queue Worker Enable/Disable
ENABLE_QUEUE_WORKER=true # Default: true
# Message Processing
QUEUE_BATCH_SIZE=10 # Max messages per receive (1-10)
QUEUE_VISIBILITY_TIMEOUT=300 # Seconds before retry (5 min)
QUEUE_WAIT_TIME=20 # Long-poll timeout (0-20s)
QUEUE_MAX_RETRIES=3 # Retries before DLQ
QUEUE_PROJECT= # Optional: process specific project only
# Gateway (if using GatewayQueueAdapter)
GATEWAY_URL=https://api.riotpiao.com
AUTHENTIK_ISSUER=https://authentik.riotpiao.com/application/o/poimen-memory/
AUTHENTIK_CLIENT_ID=poimen-memory
AUTHENTIK_CLIENT_SECRET=<secret>
# Fallback (if GATEWAY_URL not set)
# Uses InMemoryQueueAdapter for development
```
### QueueWorkerConfig struct
```rust
pub struct QueueWorkerConfig {
pub max_messages_per_batch: i32, // 1-10
pub visibility_timeout_secs: i32, // 30-600 recommended
pub wait_time_secs: i32, // 0-20
pub project: Option<String>, // Filter by project
pub max_retries: i32, // 2-5 typical
pub retry_backoff_initial_secs: i32, // 60 default
pub empty_poll_interval_secs: u64, // 5 default
pub enable_metrics: bool, // Collect stats
}
```
---
## Usage
### Starting the Server (with Queue Worker)
```bash
# Kubernetes
kubectl set env deployment/poimen-memory \
ENABLE_QUEUE_WORKER=true \
QUEUE_BATCH_SIZE=10 \
GATEWAY_URL=https://api.riotpiao.com
# Local development
ENABLE_QUEUE_WORKER=true \
QUEUE_BATCH_SIZE=5 \
cargo run --bin mem -- serve --port 9090
```
### Queue Worker is Automatic
The queue worker starts automatically when:
1. `ENABLE_QUEUE_WORKER=true` (default)
2. HTTP server starts
3. Spawned as background tokio task
No additional code needed:
```rust
// http_server.rs - automatically initialized
if enable_queue_worker {
tokio::spawn(async move {
let worker = QueueWorker::new(indexer, embeddings, config);
worker.start().await // Runs forever (long-polling loop)
});
}
```
### Monitoring Queue Worker
```bash
# Check logs
kubectl logs -f deployment/poimen-memory | grep "Queue worker"
# Expected output
# INFO Queue worker starting: config=QueueWorkerConfig { ... }
# INFO M8.2 Queue Worker started (background task)
# DEBUG Processing message: msg-550e8400-e29b-41d4-a716-446655440000
# DEBUG Message processed successfully: msg-550e8400-...
```
### Metrics
The QueueWorker tracks:
```rust
pub struct WorkerMetrics {
pub messages_received: u64, // Total received from queue
pub messages_processed: u64, // Successfully processed
pub messages_failed: u64, // Failed (will retry)
pub messages_dlq: u64, // Sent to DLQ (max retries)
pub total_processing_time_ms: u64, // Cumulative processing time
}
```
Access metrics:
```rust
let metrics = worker.metrics().await;
println!("Processed: {}", metrics.messages_processed);
println!("Failed: {}", metrics.messages_failed);
println!("Avg time/msg: {}ms",
metrics.total_processing_time_ms / metrics.messages_processed.max(1));
```
---
## Error Handling
### Retry Logic
1. **pgvector write fails** → Extend visibility (300s), retry
2. **OpenSearch write fails** → Mark pending, delete from queue, retry later via background retry task
3. **Max retries exceeded** → Send to DLQ, alert operators
### DLQ (Dead-Letter Queue)
Messages are sent to DLQ when:
- `receive_count >= max_retries` (default: 3)
- pgvector consistently fails (data issues)
- Invalid message format
DLQ messages can be examined via:
```bash
# In development:
# Check queue adapter's failed_messages state
# In production:
# Query OpenSearch DLQ index for analysis
```
---
## Performance Tuning
### Throughput Optimization
```bash
# For high-volume workloads
QUEUE_BATCH_SIZE=10 # Max messages per poll
QUEUE_VISIBILITY_TIMEOUT=300 # 5 min timeout
QUEUE_WAIT_TIME=20 # Full 20s long-poll
# Result: ~100 msgs/sec (depends on embedding latency)
```
### Latency Optimization
```bash
# For low-latency requirements
QUEUE_BATCH_SIZE=1 # Process one at a time
QUEUE_VISIBILITY_TIMEOUT=60 # 1 min timeout
QUEUE_WAIT_TIME=1 # Short poll
# Result: Faster feedback, lower throughput
```
### Resource Constraints
If embedding service is slow:
```bash
# Run multiple worker replicas
kubectl scale deployment/poimen-memory --replicas=3
# Each replica runs its own QueueWorker
# Total concurrency = 3 × QUEUE_BATCH_SIZE = 30 messages
```
---
## Testing
### Unit Tests
```bash
cargo test --lib queue_worker
```
Tests cover:
- Config validation
- Message roundtrip (send → receive → delete)
- Batch operations (multiple messages)
- DLQ transitions
- Attributes preservation
- Stats tracking
### Integration Tests
```bash
cargo test --test it_queue_worker_integration
```
Tests verify:
- Full pipeline (IngestWorker → Queue → DualWriteIndexer)
- Message lifecycle states
- Error handling and retries
- Concurrent processing
### Local Development
Use in-memory adapter (no GATEWAY_URL):
```bash
# Development server
ENABLE_QUEUE_WORKER=true \
QUEUE_BATCH_SIZE=3 \
cargo run --bin mem -- serve --port 9090
# Queue worker logs
# ...INFO M8.2 Queue Worker started
# ...DEBUG Received 0 messages from queue (max_messages=3)
# ...INFO Queue empty, waiting 5s before retry
# Test ingestion
curl -X POST http://localhost:9090/memory/ingest \
-H "apikey: test-key" \
-H "Content-Type: application/json" \
-d '{"project":"test", "source":"cli", "ingest_id":"123", "records":[{"text":"hello"}]}'
# Watch worker process it
```
---
## Deployment Checklist
- [ ] `ENABLE_QUEUE_WORKER=true` set in K8s env
- [ ] `GATEWAY_URL` and Authentik credentials configured (if using gateway)
- [ ] Queue topic/queue created in message broker (if applicable)
- [ ] OpenSearch cluster healthy (for dual-write)
- [ ] Embedding service accessible and responsive
- [ ] Replica count ≥ 1 (recommended: 2-3 for HA)
- [ ] Logs monitored for "Queue worker error"
- [ ] Health checks passing (`/health`)
- [ ] DLQ monitoring set up (alert on high DLQ count)
---
## Troubleshooting
### Queue Worker Not Starting
**Symptom**: No "Queue worker starting" in logs
**Check**:
```bash
# Verify env var
kubectl get deployment poimen-memory -o json | \
jq '.spec.template.spec.containers[0].env' | grep ENABLE_QUEUE_WORKER
# Verify logs
kubectl logs deployment/poimen-memory | grep -i "queue worker"
```
**Fix**:
```bash
kubectl set env deployment/poimen-memory ENABLE_QUEUE_WORKER=true
kubectl rollout restart deployment/poimen-memory
```
### Messages Stuck in Queue
**Symptom**: Queue not emptying, messages keep retrying
**Check**:
```bash
# Check embedding service
curl http://embedding-service:8000/health
# Check OpenSearch
curl http://opensearch:9200/_cluster/health
# Check pgvector
psql -h memory-db -U app memory -c "SELECT count(*) FROM chunks;"
```
**Fix**:
- Restart embedding service if slow/hung
- Check OpenSearch cluster health
- Increase visibility timeout: `QUEUE_VISIBILITY_TIMEOUT=600`
### Too Many DLQ Messages
**Symptom**: High rate of messages in DLQ
**Check**:
```bash
# Inspect DLQ messages
# (implementation-specific)
# Check message format
# Ensure ChunkInput JSON is valid
```
**Fix**:
- Verify ingest source is producing valid JSON
- Check for data corruption in ingest pipeline
- Increase retries: `QUEUE_MAX_RETRIES=5`
---
## Architecture Notes
### Why Async Queue?
1. **Decoupling**: Ingest doesn't wait for embedding + write
2. **Scaling**: Single ingest API handles many more requests
3. **Resilience**: OpenSearch failure doesn't block ingest
4. **Throughput**: Embeddings computed in parallel
### Why Long-Polling?
Instead of constant polling, long-poll waits up to 20 seconds for messages. This:
- Reduces CPU usage (no tight loop)
- Reduces network overhead
- Achieves near-real-time processing
- Matches SQS/Kafka semantics
### Why Visibility Timeout?
When a message is received, it becomes invisible to other workers for N seconds. This prevents:
- Duplicate processing (if one worker crashes)
- Race conditions (two workers on same message)
- Lost messages (message stays in queue until ack'd)
---
## References
- [M8.2 Dual-Write Indexer](./M8.2-DUAL_WRITE_INDEXER.md)
- [Gateway Queue Adapter](./M8.2-GATEWAY_QUEUE_ADAPTER.md)
- [Queue Adapter Trait](../crates/mem-cli/src/queue_adapter.rs)
- SQS Concepts: https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/