Chris Lu
902a12fd6f
* wdclient: bound the wait for a master leader by the caller's context WithClient waited on GetMaster with context.Background(), so a caller that arrived while no master leader was known parked in a 200ms poll loop until one appeared, whatever deadline it had already set on the RPC. Each retry above it then left another goroutine in the same wait. Take the context in WithClient and WithClientCustomGetMaster and hand it to GetMaster, and stop the retry loop once it is done. The dial keeps context.Background(): fn brings its own RPC context, so a cancellation seen here cannot be attributed to the shared connection. Call sites pass whatever they hold: the request context in the filer's CollectionList, DeleteCollection and Statistics handlers and in the credential store's propagation, the operation context in the shell's s3.bucket.delete and the kafka gateway's broker and filer discovery, and context.Background() where there is none - the shell commands, the admin dashboard wrapper, and the exclusive locker's initial lease. The locker's release keeps its own uncancelled context so a slow unlock cannot turn into a ghost lock. Claude-Session: https://claude.ai/code/session_01BjDWtZsCoZY6x4pdDmGWxU * wdclient: test that WithClient gives up with the caller's context Claude-Session: https://claude.ai/code/session_01BjDWtZsCoZY6x4pdDmGWxU * wdclient: cut the master retry backoff short when the caller gives up util.Retry sleeps unconditionally between attempts, so a transient error arriving just before the caller's deadline still cost it a full backoff step. Use the context-aware util.RetryWithBackoff, the same helper the volume lookup in this file already uses. Two call sites went with it: the shell's lock-holder lookup builds its three second bound before WithClient so it also covers finding the leader, as its comment already promised, and the filer's post-delete collection cleanup goes back to an uncancelled context - the entry is already gone, so a caller that hung up must not leave the collection behind. Claude-Session: https://claude.ai/code/session_01BjDWtZsCoZY6x4pdDmGWxU * wdclient: test that a cancel during backoff ends the retry Claude-Session: https://claude.ai/code/session_01BjDWtZsCoZY6x4pdDmGWxU
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.