chrislu
cd9b39ca50
Implements automatic cleanup of topic partitions with no active publishers or subscribers to prevent memory accumulation from short-lived topics. **Key Features:** 1. Activity Tracking (local_partition.go) - Added lastActivityTime field to LocalPartition - UpdateActivity() called on publish, subscribe, and message reads - IsIdle() checks if partition has no publishers/subscribers - GetIdleDuration() returns time since last activity - ShouldCleanup() determines if partition eligible for cleanup 2. Cleanup Task (local_manager.go) - Background goroutine runs every 1 minute (configurable) - Removes partitions idle for > 5 minutes (configurable) - Automatically removes empty topics after all partitions cleaned - Proper shutdown handling with WaitForCleanupShutdown() 3. Broker Integration (broker_server.go) - StartIdlePartitionCleanup() called on broker startup - Default: check every 1 minute, cleanup after 5 minutes idle - Transparent operation with sensible defaults **Cleanup Process:** - Checks: partition.Publishers.Size() == 0 && partition.Subscribers.Size() == 0 - Calls partition.Shutdown() to: - Flush all data to disk (no data loss) - Stop 3 goroutines (loopFlush, loopInterval, cleanupLoop) - Free in-memory buffers (~100KB-10MB per partition) - Close LogBuffer resources - Removes partition from LocalTopic.Partitions - Removes topic if no partitions remain **Benefits:** - Prevents memory bloat from short-lived topics - Reduces goroutine count (3 per partition cleaned) - Zero configuration required - Data remains on disk, can be recreated on demand - No impact on active partitions **Example Logs:** I Started idle partition cleanup task (check: 1m, timeout: 5m) I Cleaning up idle partition topic-0 (idle for 5m12s, publishers=0, subscribers=0) I Cleaned up 2 idle partition(s) **Memory Freed per Partition:** - In-memory message buffer: ~100KB-10MB - Disk buffer cache - 3 goroutines - Publisher/subscriber tracking maps - Condition variables and mutexes **Related Issue:** Prevents memory accumulation in systems with high topic churn or many short-lived consumer groups, improving long-term stability and resource efficiency. **Testing:** - Compiles cleanly - No linting errors - Ready for integration testing fmt
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.