chrislu
6e1b96fb4a
FINAL PHASE - SMQ Native Offset Implementation Complete ✅ - Create comprehensive end-to-end integration tests covering complete offset flow: - TestEndToEndOffsetFlow: Full publish/subscribe workflow with offset tracking - TestOffsetPersistenceAcrossRestarts: Validation of offset persistence behavior - TestConcurrentOffsetOperations: Multi-threaded offset assignment validation - TestOffsetValidationAndErrorHandling: Comprehensive error condition testing - All integration tests pass, validating complete system functionality - Add extensive performance benchmarks for all major operations: - BenchmarkOffsetAssignment: Sequential and parallel offset assignment - BenchmarkBatchOffsetAssignment: Batch operations with various sizes - BenchmarkSQLOffsetStorage: Complete SQL storage operation benchmarks - BenchmarkInMemoryVsSQL: Performance comparison between storage backends - BenchmarkOffsetSubscription: Subscription lifecycle and operations - BenchmarkSMQOffsetIntegration: Full integration layer performance - BenchmarkConcurrentOperations: Multi-threaded performance characteristics - Benchmarks demonstrate production-ready performance and scalability - Validate offset consistency and system reliability: - Database migration system with automatic schema updates - Proper NULL handling in SQL operations and migration management - Comprehensive error handling and validation throughout all components - Thread-safe operations with proper locking and concurrency control - Create comprehensive implementation documentation: - SMQ_NATIVE_OFFSET_IMPLEMENTATION.md: Complete implementation guide - Architecture overview with detailed component descriptions - Usage examples for all major operations and integration patterns - Performance characteristics and optimization recommendations - Deployment considerations and configuration options - Troubleshooting guide with common issues and debugging tools - Future enhancement roadmap and extension points - Update development plan with completion status: - All 6 phases successfully completed with comprehensive testing - 60+ tests covering all components and integration scenarios - Production-ready SQL storage backend with migration system - Complete broker integration with offset-aware operations - Extensive performance validation and optimization - Future-proof architecture supporting extensibility ## Implementation Summary This completes the full implementation of native per-partition sequential offsets in SeaweedMQ, providing: ✅ Sequential offset assignment per partition with thread-safe operations ✅ Persistent SQL storage backend with automatic migrations ✅ Complete broker integration with offset-aware publishing/subscription ✅ Comprehensive subscription management with seeking and lag tracking ✅ Robust error handling and validation throughout the system ✅ Extensive test coverage (60+ tests) and performance benchmarks ✅ Production-ready architecture with monitoring and troubleshooting support ✅ Complete documentation with usage examples and deployment guides The implementation eliminates the need for external offset mapping while maintaining high performance, reliability, and compatibility with existing SeaweedMQ operations. All tests pass and benchmarks demonstrate production-ready scalability.
SeaweedMQ Message Queue on SeaweedFS (WIP, not ready)
What are the use cases it is designed for?
Message queues are like water pipes. Messages flow in the pipes to their destinations.
However, what if a flood comes? Of course, you can increase the number of partitions, add more brokers, restart, and watch the traffic level closely.
Sometimes the flood is expected. For example, backfill some old data in batch, and switch to online messages. You may want to ensure enough brokers to handle the data and reduce them later to cut cost.
SeaweedMQ is designed for use cases that need to:
- Receive and save large number of messages.
- Handle spike traffic automatically.
What is special about SeaweedMQ?
- Separate computation and storage nodes to scale independently.
- Unlimited storage space by adding volume servers.
- Unlimited message brokers to handle incoming messages.
- Offline messages can be operated as normal files.
- Scale up and down with auto split and merge message topics.
- Topics can automatically split into segments when traffic increases, and vice verse.
- Pass messages by reference instead of copying.
- Clients can optionally upload the messages first and just submit the references.
- Drastically reduce the broker load.
- Stateless brokers
- All brokers are equal. One broker is dynamically picked as the leader.
- Add brokers at any time.
- Allow rolling restart brokers or remove brokers at a pace.
Design
How it works?
Brokers are just computation nodes without storage. When a broker starts, it reports itself to masters. Among all the brokers, one of them will be selected as the leader by the masters.
A topic needs to define its partition key on its messages.
Messages for a topic are divided into segments. One segment can cover a range of partitions. A segment can be split into 2 segments, or 2 neighboring segments can be merged back to one segment.
During write time, the client will ask the broker leader for a few brokers to process the segment.
The broker leader will check whether the segment already has assigned the brokers. If not, select a few brokers based on their loads, save the selection into filer, and tell the client.
The client will write the messages for this segment to the selected brokers.
Failover
The broker leader does not contain any state. If it fails, the masters will select a different broker.
For a segment, if any one of the selected brokers is down, the remaining brokers should try to write received messages to the filer, and close the segment to the clients.
Then the clients should start a new segment. The masters should assign other healthy brokers to handle the new segment.
So any brokers can go down without losing data.
Auto Split or Merge
(The idea is learned from Pravega.)
The brokers should report its traffic load to the broker leader periodically.
If any segment has too much load, the broker leader will ask the brokers to tell the client to close current one and create two new segments.
If 2 neighboring segments have the combined load below average load per segment, the broker leader will ask the brokers to tell the client to close this 2 segments and create a new segment.