11 KiB
Executor Service Foundation - Session Summary
Date: 2026-01-16 Evening
Phase: Phase 4.1 - Executor Foundation
Status: Core structure complete, needs API refinements
Overview
Implemented the foundational structure for the Executor Service, the core orchestration engine of Attune. The Executor is responsible for processing enforcements from triggered rules, scheduling executions to workers, managing execution lifecycle, and enforcing policies.
This session focused on Session 1: Foundation as outlined in the implementation plan.
Accomplishments
1. Executor Crate Structure Created ✅
Created complete directory structure:
crates/executor/
├── Cargo.toml # Dependencies configured
└── src/
├── main.rs # Service entry point
├── service.rs # Main service orchestration
├── enforcement_processor.rs # Enforcement message processing
├── scheduler.rs # Execution scheduling
└── execution_manager.rs # Execution lifecycle management
2. ExecutorService Implementation ✅
File: crates/executor/src/service.rs
-
Core Features:
- Database pool integration (PostgreSQL via SQLx)
- Message queue connection (RabbitMQ via lapin)
- Publisher and Consumer initialization
- Graceful shutdown handling with broadcast channel
- Cloneable service for sharing across async tasks
-
Architecture:
- Uses
Arc<ExecutorServiceInner>pattern for thread-safe sharing - Spawns multiple processor tasks concurrently
- Handles SIGINT for graceful shutdown
- Uses
3. EnforcementProcessor Module ✅
File: crates/executor/src/enforcement_processor.rs
-
Purpose: Processes
EnforcementCreatedmessages when rules trigger -
Responsibilities:
- Listen for enforcement messages from RabbitMQ
- Fetch enforcement, rule, and event data from database
- Evaluate rule conditions (skeleton implemented)
- Create execution records
- Publish
ExecutionRequestedmessages
-
Key Components:
EnforcementCreatedPayloadstruct for message deserializationExecutionRequestedPayloadstruct for outbound messages- Database repository integration
- Message envelope handling
4. ExecutionScheduler Module ✅
File: crates/executor/src/scheduler.rs
-
Purpose: Routes executions to available workers
-
Responsibilities:
- Listen for
ExecutionRequestedmessages - Fetch action metadata to determine runtime requirements
- Select appropriate worker based on:
- Runtime compatibility
- Worker status (active only)
- Load balancing (placeholder for future enhancement)
- Update execution status to
Scheduled - Queue execution to worker-specific queue
- Listen for
-
Worker Selection Strategy:
- Filters by runtime compatibility
- Filters by worker status (active only)
- Currently selects first available (TODO: implement load balancing)
5. ExecutionManager Module ✅
File: crates/executor/src/execution_manager.rs
-
Purpose: Manages execution lifecycle and status transitions
-
Responsibilities:
- Listen for
ExecutionStatusChangedmessages - Update execution records in database
- Handle workflow orchestration (parent-child executions)
- Trigger child executions on parent completion
- Publish completion notifications
- Listen for
-
Status Handling:
- Supports all execution states: Requested, Scheduling, Scheduled, Running, Completed, Failed, Cancelled, Timeout, Abandoned
- Special handling for terminal states (Completed, Failed, Cancelled)
- Workflow triggering on successful completion
6. Service Entry Point ✅
File: crates/executor/src/main.rs
- Features:
- CLI argument parsing with clap
- Configurable log level
- Enhanced logging with thread IDs and line numbers
- Configuration loading from YAML files
- Connection string masking for security
- Graceful shutdown signal handling
- Comprehensive startup logging
7. Dependencies Configured ✅
File: crates/executor/Cargo.toml
Added dependencies:
attune-common- Shared models, repositories, message queuetokio- Async runtimesqlx- Database accessserde,serde_json- Serializationtracing,tracing-subscriber- Logginganyhow,thiserror- Error handlingconfig- Configuration managementchrono- Timestampsuuid- Message IDsclap- CLI parsinglapin- RabbitMQ clientredis- Redis client (for future use)
Technical Details
Message Flow Architecture
1. Enforcement Created:
sensor → trigger → event → rule → enforcement
↓
EnforcementProcessor listens on "enforcement.created"
↓
Creates Execution record
↓
Publishes "execution.requested" message
2. Execution Scheduling:
ExecutionScheduler listens on "execution.requested"
↓
Selects appropriate worker
↓
Updates execution status to Scheduled
↓
Publishes to worker-specific queue
3. Execution Lifecycle:
ExecutionManager listens on "execution.status.*"
↓
Updates execution state in database
↓
On completion: triggers child executions if needed
↓
Publishes "execution.completed" notification
Message Envelope Pattern
All messages use MessageEnvelope<T> pattern:
message_id: Unique UUIDcorrelation_id: For tracing related messagesmessage_type: Enum for routingtimestamp: Creation timeheaders: Metadata (source, trace_id, retry_count)payload: Typed payload struct
Database Integration
Uses repository pattern for all database access:
EnforcementRepository(from event module)EventRepositoryRuleRepositoryExecutionRepositoryActionRepositoryRuntimeRepositoryWorkerRepository
Known Issues & TODOs
Compilation Issues (To be resolved in Session 2)
-
Consumer API Pattern Mismatch:
- Current code attempts to use
consumer.consume()method - Actual API uses
consumer.consume_with_handler()pattern - Need to refactor processors to use handler-based approach
- Current code attempts to use
-
Missing Trait Implementations:
WorkerRepositoryneedsListtrait implementationUpdateExecutionInputneedsFrom<Execution>conversion
-
Unused Imports:
- Several modules have unused imports to clean up
- Minor warnings from dead code
Future Enhancements (Later Sessions)
-
Policy Enforcement:
- Rate limiting logic
- Concurrency control
- Queue management for policy violations
-
Workflow Orchestration:
- Parse workflow definitions from action metadata
- Extract child actions from execution results
- Implement DAG-based workflow execution
-
Inquiry Handling:
- Pause executions waiting for human input
- Handle inquiry timeouts
- Resume execution with inquiry response
-
Worker Selection:
- Implement intelligent load balancing
- Consider worker affinity (same pack, same runtime)
- Geographic locality
- Round-robin or least-connections strategy
-
Error Handling:
- Retry logic with exponential backoff
- Dead letter queue for failed messages
- Circuit breaker pattern for service protection
-
Monitoring:
- Metrics collection
- Health checks
- Performance tracking
Testing Strategy
Unit Tests (To be implemented)
- Enforcement condition evaluation
- Worker selection logic
- Status transition validation
- Message serialization/deserialization
Integration Tests (To be implemented)
- End-to-end enforcement → execution flow
- Database transaction handling
- Message queue reliability
- Multiple processors running concurrently
Manual Testing (To be implemented)
- Service startup and shutdown
- Configuration loading
- Graceful shutdown handling
- Message processing throughput
Documentation Updates
Updated Files
work-summary/TODO.md- Added Session 1 completion, updated priorities- This session summary document
Technical Documentation (To be created)
- Architecture diagrams for message flow
- Sequence diagrams for enforcement processing
- Worker selection algorithm documentation
- Configuration examples for production deployment
Metrics
- Files Created: 5 new files
- Lines of Code: ~1,200 lines
- Modules Implemented: 4 core modules
- Dependencies Added: 12 crates
- Compilation Status: Core structure complete, API refinements needed
- Test Coverage: 0% (to be implemented in Session 2)
Next Steps (Session 2: Enforcement Processing)
Immediate Priorities
-
Fix Consumer API Usage:
- Refactor all processors to use
consume_with_handlerpattern - Update message handling to be callback-based
- Test message consumption
- Refactor all processors to use
-
Implement Missing Traits:
- Add
Listimplementation toWorkerRepository - Add conversion trait for
UpdateExecutionInput
- Add
-
Clean Up Warnings:
- Remove unused imports
- Fix dead code warnings
-
End-to-End Testing:
- Create test enforcement in database
- Verify message flow through all processors
- Validate execution creation and scheduling
Medium-Term Goals
-
Policy Enforcement:
- Implement rate limiting checks
- Add concurrency control
- Queue management
-
Workflow Support:
- Parse workflow definitions
- Implement child execution triggering
- Handle complex execution graphs
-
Production Readiness:
- Add comprehensive error handling
- Implement retry logic
- Add monitoring and metrics
Lessons Learned
-
Message Queue Patterns: The RabbitMQ library (lapin) uses a handler-based consumption pattern rather than polling. Future code should follow this pattern from the start.
-
Type Safety: Using strongly-typed payload structs with
MessageEnvelope<T>provides excellent type safety and makes message handling more reliable. -
Configuration Complexity: Multiple configuration types (
ConfigvsMessageQueueConfig) can cause confusion. Need clear documentation on which to use where. -
Repository Pattern: The repository abstraction works well for database access but needs consistent trait implementations across all repositories.
-
Service Architecture: The cloneable service pattern with
Arc<ServiceInner>works well for sharing state across async tasks while maintaining thread safety.
Conclusion
Session 1 successfully established the foundation for the Executor Service. The core structure is in place with proper separation of concerns across three main processors (Enforcement, Scheduling, Management). While compilation issues need to be resolved, the architectural decisions are sound and the codebase is well-positioned for rapid iteration in Session 2.
The Executor is now ready to move from foundation to functional implementation, with the message consumption pattern being the primary blocker to overcome.
Session 1 Status: ✅ COMPLETE (with known issues to address in Session 2)