Chris LuandGitHub
86c5e815d2
* fix(kafka): make consumer-group rebalancing work end-to-end
TestConsumerGroups was failing every run since the job was added
(2026-04-17) but the failures were masked by a `|| echo ...` trailer on
the go test invocation, so the CI reported green. Removing the mask
exposes several real bugs in the gateway's group-coordinator code:
1. JoinGroup deduplicated members by ClientID, which collapsed two
Sarama consumers that share the default ClientID ("sarama") into a
single member slot and broke rebalancing. Key dedup off the TCP
ConnectionID instead; keep ClientID on the member for DescribeGroup
fidelity.
2. Every JoinGroup replaced the *GroupMember struct, wiping the
Assignment the leader had just published in its SyncGroup and leaving
non-leader consumers with 0 partitions after a rebalance. Update the
existing member in place on rejoin.
3. Non-leader SyncGroup returned an empty assignment while the leader
was mid-rebalance, so consumers silently came up with no partitions.
Return REBALANCE_IN_PROGRESS when the group is not Stable so Sarama
retries the join/sync cycle (4 retries x 2s backoff by default).
4. Heartbeat returned ILLEGAL_GENERATION on a gen mismatch even when
the group was in PreparingRebalance/CompletingRebalance. Return
REBALANCE_IN_PROGRESS in that case so the heartbeat loop cleanly
cancels the session instead of tearing it down on a fatal error.
5. LeaveGroup parser only handled v0-v2. Sarama at V2_8_0_0 sends v3
(Members array) by default, so the gateway silently rejected the
request as InvalidGroupID and dead consumers stayed in the group as
phantom leaders. Added v3 (Members array) and v4+ (flexible/compact/
tagged-fields) parsing.
The rebalancing integration tests called Consume() once per consumer,
which cannot survive a rebalance (heartbeat RBIP cancels the session
and Consume() returns - this is documented Sarama behaviour; callers
are expected to loop). Added a runConsumeLoop helper and used it in the
four affected sub-tests. RebalanceTestHandler.Setup now overwrites
stale entries in its assignments channel so the test observes the
settled post-rebalance snapshot rather than whatever arrived first.
* fix(kafka): address PR review feedback
- JoinGroup now snapshots existing members before mutating and restores
the snapshot on INCONSISTENT_GROUP_PROTOCOL rollback. Previously the
rollback path always deleted the entry, corrupting group state when
an existing member rejoined with an incompatible protocol.
- handleLeaveGroup iterates request.Members instead of processing only
the first entry, so v3+ batch departures (KIP-345 style) correctly
remove every listed member and build a per-member response. A single
group-state transition runs after the loop, with leader election
only triggered if the actual group leader was among the departures.
- Added buildLeaveGroupFlexibleResponse for v4+ clients. The parser
already decoded flexible versions, but the response still went out in
non-flexible encoding (4-byte array lengths, 2-byte strings, no
tagged fields), which v4+ clients could not parse. Route flexible
versions through the new builder; v1-v3 keep buildLeaveGroupFullResponse.
- BasicFunctionality gives each consumer its own
ConsumerGroupHandler/ready channel. The previous shared handler
closed ready once, so readyCount advanced to numConsumers from a
single signal; the test could proceed without the other consumers
actually reaching Setup.
- RebalanceTestHandler.assignments is now a size-1 channel, so readers
always observe the most recent rebalance snapshot instead of an
intermediate one from an earlier round.
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.