f67ba35f4a
* Make lock_manager.RenewInterval configurable in LiveLock - Add renewInterval field to LiveLock struct - Modify StartLongLivedLock to accept renewInterval parameter - Update all call sites to pass lock_manager.RenewInterval - Default to lock_manager.RenewInterval if zero is passed * S3 metrics: reduce collection interval to half of bucketSizeMetricsInterval Since S3 metrics collection is not critical, check more frequently but only collect when holding the distributed lock. This allows faster detection of any issues while avoiding overhead on non-leader instances. * Remove unused lock_manager import from bucket_size_metrics.go * Refactor: Make lockTTL the primary parameter, derive renewInterval from it Instead of configurable renew interval, lockTTL is now the input parameter. The renewal interval is automatically derived as lockTTL / 2, ensuring that locks are renewed well before expiration. Changes: - Replace renewInterval parameter with lockTTL - Rename LiveLock.renewInterval field to lockTTL - Calculate renewInterval as lockTTL / 2 inside the goroutine - Update all call sites to pass lockTTL values - Simplify sleep logic to use consistent renewInterval for both states This approach is more intuitive and guarantees safe renewal windows. * When locked, renew more aggressively to actively keep the lock When holding the lock, sleep for renewInterval/2 to renew more frequently. When seeking the lock, sleep for renewInterval to retry with normal frequency. This ensures we actively maintain lock ownership while being less aggressive when competing for the lock. * Simplify: use consistent renewInterval for all lock states Since renewInterval is already lockTTL / 2, there's no need to differentiate between locked and unlocked states. Both use the same interval for consistency. * Adjust sleep intervals for different lock states - Locked instances sleep for renewInterval (lockTTL/2) to renew the lock - Unlocked instances sleep for 5*renewInterval (2.5*lockTTL) to retry acquisition less frequently
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.