Status
Current state: [Under Discussion]
Discussion thread: TBD
JIRA: TBD
Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).
Motivation
Recently Kafka community is promoting cooperative rebalancing to mitigate the pain points in the stop-the-world rebalancing protocol and an initiation for Kafka Connect already started as KIP-415. There are already exciting discussions around it, but for Kafka Streams, the delayed rebalance is not the complete solution. This KIP is trying to customize the cooperative rebalancing approach specifically for KStream application context, based on the great design for KConnect.
Currently Kafka Streams uses consumer membership protocol to coordinate the stream task assignment. When we scale up the stream application, KStream group will attempt to revoke active tasks and let the newly spinned hosts take over them. New hosts need to restore assigned tasks' state before transiting to "running". For state heavy application, it is not ideal to give up the tasks immediately once the new player joins the party, instead we should buffer some time to let the new player accept a fair amount of restoring tasks, and finish state reconstruction first before officially taking over the active tasks. Ideally, we could realize no downtime transition during cluster scaling.
In short, the primary goals of this KIP are:
- Reduce unnecessary downtime due to task restoration and global application revocation.
- Better auto scaling experience for KStream applications.
Proposed Changes
Terminology
we shall define several new terms for easy walkthrough of the algorithm.
- Worker (A.K.A stream worker): thread level streaming processor, who actually takes the stream task.
- Instance (A.K.A stream instance): the KStream instance serving as container of stream workers set. This could suggest a physical host or a k8s pod.
- Leaner task: a special task that gets assigned to one stream instance to restore a current active task state from another instance.
Learner Task Essential
Learner task shares the same semantics as standby task, which is taken care by the restore consumer to replicate main task state. When the restoration of learner task is complete, the stream instance will initiate a new JoinGroupRequest to call out rebalance of the new task assignment. The goal of learner task is to delay the task migration when the destination host has not finished or even started replaying the active task.
Stop-The-World Effect
As mentioned in motivation section, we also want to mitigate the stop-the-world effect of current global rebalance protocol. A quick recap of current rebalance semantics on KStream: when rebalance starts, all workers would
Join group with all current assigned tasks revoked.
Wait until group assignment finish to get assigned tasks.
Replay the assigned tasks state
Once all replay jobs finish, worker transits to running mode.
The reason for revoking all ongoing tasks is because we need to guarantee each topic partition is assigned with exactly one consumer at any time. In this way, any topic partition could not be re-assigned before it is revoked.
For Kafka Connect, we choose to keep all current assigned tasks running and trade off with one more rebalance. The behavior becomes:
Join group with all current active tasks running.
Sync the revoked partitions and stop them (first rebalance).
Rejoin group immediately with only active tasks (second rebalance).
Feel free to take a look at KIP-415 example to get a sense of how the algorithm works.
For KStream, we are going to take a trade-off between “revoking all” and “revoking none” solution: we shall only revoke tasks that are being learned since last round. So when we assign learner tasks to new member, we shall also mark active tasks as "being learned" on current owners. Every time when a rebalance begins, the task owners will revoke the being learned tasks and join group without affecting other ongoing tasks. Learned tasks could then immediately transfer ownership without attempting for a second round of rebalance. Compared with KIP-415, we are optimizing for fewer rebalances, but increasing the metadata size and sacrificing partial availability of the learner tasks.
Next we are going to look at several typical scaling scenarios to better understand the algorithm.
Scaling Scenarios
Scale Up Running Application
The newly joined workers will be assigned with learner tasks by the group leader and they will replay the corresponding changelogs on local first. By the end of first round of rebalance, there is no “real task transfer”. When new member finally finishes the replay task, it will re-attempt to join the group to indicate that it is “ready” to take on real active tasks. During second rebalance, the leader will eventually transfer the task ownership.
Cluster has 3 stream workers S1(leader), S2, S3, and they each own some tasks 1 ~ 5 Group stable state: S1[T1, T2], S2[T3, T4], S3[T5] #First Rebalance New member S4 joins the group S1 performs task assignments: S1(assigned: [T1, T2], revoked: [], learning: []) S2(assigned: [T3, T4], revoked: [], learning: []) S3(assigned: [T5], revoked: [], learning: []) S4(assigned: [], revoked: [], learning: [T1]) #Second Rebalance New member S5 joins the group. Member S1~S5 join with following metadata: (S4 is not ready yet) S1(assigned: [T2], revoked: [T1], learning: []) // T1 revoked because it's "being learned" S2(assigned: [T3, T4], revoked: [], learning: []) S3(assigned: [T5], revoked: [], learning: []) S4(assigned: [], revoked: [], learning: [T1]) S5(assigned: [], revoked: [], learning: [T3]) S1 performs task assignments: S1(assigned: [T1, T2], revoked: [], learning: []) S2(assigned: [T3, T4], revoked: [], learning: []) S3(assigned: [T5], revoked: [], learning: []) S4(assigned: [], revoked: [], learning: [T1]) S5(assigned: [], revoked: [], learning: [T3]) #Third Rebalance Member S4 finishes its replay and becomes ready, re-attempt to join the group. S5 is not ready yet. Member S1~S5 join with following status:(S5 is not ready yet) S1(assigned: [T2], revoked: [T1], learning: []) S2(assigned: [T4], revoked: [T3], learning: []) // T3 revoked because it's "being learned" S3(assigned: [T5], revoked: [], learning: []) S4(assigned: [], revoked: [], learning: [T1]) S5(assigned: [], revoked: [], learning: [T3]) S1 performs task assignments: S1(assigned: [T2], revoked: [T1], learning: []) S2(assigned: [T3, T4], revoked: [], learning: []) S3(assigned: [T5], revoked: [], learning: []) S4(assigned: [T1], revoked: [], learning: []) S5(assigned: [], revoked: [], learning: [T3]) #Fourth Rebalance Member S5 is ready, re-attempt to join the group. Member S1~S5 join with following status:(S5 is not ready yet) S1(assigned: [T2], revoked: [], learning: []) S2(assigned: [T4], revoked: [T3], learning: []) // T3 revoked because it's "being learned" S3(assigned: [T5], revoked: [], learning: []) S4(assigned: [T1], revoked: [], learning: []) S5(assigned: [], revoked: [], learning: [T3]) S1 performs task assignments: S1(assigned: [T2], revoked: [], learning: []) S2(assigned: [T4], revoked: [T3], learning: []) S3(assigned: [T5], revoked: [], learning: []) S4(assigned: [T1], revoked: [], learning: []) S5(assigned: [T3], revoked: [], learning: []) Now the group reaches balance with 5 members and 5 tasks.
Scale Up from Empty Group
Scaling up from scratch means all workers are new members. There is no need to implement a learner stage because there is nothing to learn: we don’t even have a changelog topic to start with. We should be able to handle this case by identifying whether the given task is in the active task bucket for other members, if not we just transfer the ownership immediately.
After deprecating group.initial.rebalance.delay, we still expect the algorithm to work because every task assignment during rebalance will adhere to the rule "if given task is currently active, reassignment must happen only to workers who are declared ready to serve this task."
Group empty state: unassigned tasks [T1, T2, T3, T4, T5] #First Rebalance New member S1 joins the group S1 performs task assignments: S1(assigned: [T1, T2, T3, T4, T5], revoked: [], learning: []) // T1~5 not previously owned #Second Rebalance New member S2, S3 joins the group S1 performs task assignments: S1(assigned: [T1, T2, T3, T4, T5], revoked: [], learning: []) S2(assigned: [], revoked: [], learning: [T3, T4]) S3(assigned: [], revoked: [], learning: [T5]) #Third Rebalance S2 and S3 are ready immediately after the assignment. Member S1~S3 join with following status: S1(assigned: [T1, T2], revoked: [T3, T4, T5], learning: []) S2(assigned: [], revoked: [], learning: [T3, T4]) S3(assigned: [], revoked: [], learning: [T5]) S1 performs task assignments: S1(assigned: [T1, T2], revoked: [T3, T4, T5], learning: []) S2(assigned: [T3, T4], revoked: [], learning: []) S3(assigned: [T5], revoked: [], learning: [])
Scale Down Running Application
As we have already discussed around the “learner” logic, when we perform the scale down of stream group, it is also favorable to initiate learner tasks before actually shutting down the instances. Although standby tasks could help in this case, it requires user to pre-set which may not be available when admin performs scaling down. The plan is to use command line tool to tell certain stream members that a shutdown is on the way to be executed. These informed members will send join group request with join reason indicating they are “leaving soon”. During rebalance assignment, leader will perform the learner assignment among members without intention of leaving. And the leaving member will shut down itself once received the instruction to revoke all its active tasks.
For ease of operation, a new tool for scaling down the stream app shall be built. It will have access to the application instances, and ideally could compute targeting scaled down members while end user just needs to provide a % of scale down. For example, if the current cluster size is 40 and we choose to scale down to 80%, then the script will attempt to inform 8 of 40 hosts to “prepare leaving” the group.
Group stable state: S1[T1, T2], S2[T3, T4], S3[T5] Scaling down the application, S2 will be leaving. #First Rebalance Member S2 joins the group and claim that it is leaving S1 performs task assignments: S1(assigned: [T1, T2], revoked: [], learning: [T3]) S2(assigned: [T3, T4], revoked: [], learning: []) S3(assigned: [T5], revoked: [], learning: [T4]) #Second Rebalance S3 finishes replay first and trigger another rebalance Member S1~S3 join with following status:(S1 is not ready yet) S1(assigned: [T1, T2], revoked: [], learning: [T3]) S2(assigned: [T3], revoked: [T4], learning: []) S3(assigned: [T5], revoked: [], learning: [T4]) S1 performs task assignments: S1(assigned: [T1, T2], revoked: [], learning: [T3]) S2(assigned: [T3], revoked: [T4], learning: []) S3(assigned: [T4, T5], revoked: [], learning: []) #Third Rebalance S1 finishes replay and trigger rebalance. Member S1~S3 join with following status: S1(assigned: [T1, T2], revoked: [], learning: [T3]) S2(assigned: [], revoked: [T3], learning: []) S3(assigned: [T4, T5], revoked: [], learning: []) S1 performs task assignments: S1(assigned: [T1, T2, T3], revoked: [], learning: []) S2(assigned: [], revoked: [T3], learning: []) S3(assigned: [T4, T5], revoked: [], learning: []) S2 will shutdown itself upon new assignment since there is no assigned task left.
Online Host Swapping
This is a typical use case where user wants to replace entire application's host type. Normally administrator will choose to do host swap one by one, which could cause endless KStream resource shuffling. The recommended approach under cooperative rebalancing is like:
- Increase the capacity of the current stream job to 2
- Mark existing stream instances as leaving
- Learner tasks finished on new hosts, shutting down old ones.
Leader Transfer During Scaling
Leader crash could cause a missing of historical assignment information. For the learner assignment, however, each worker maintains its own assignment status, so when the learner task's id has no active task running, the transfer will happen immediately. Leader switch in this case is not a big concern. The essence is that we don't rely on leader information to do the assignment.
Cluster has 3 stream workers S1(leader), S2, S3, and they own tasks 1 ~ 5 Group stable state: S1[T1, T2], S2[T3, T4], S3[T5] #First Rebalance New member S4 joins the group S1 performs task assignments: S1(assigned: [T1, T2], revoked: [], learning: []) S2(assigned: [T3, T4], revoked: [], learning: []) S3(assigned: [T5], revoked: [], learning: []) S4(assigned: [], revoked: [], learning: [T1]) #Second Rebalance S1 crashes/gets killed before S4 is ready, S2 takes over the leader. Member S2~S4 join with following status: S2(assigned: [T3, T4], revoked: [], learning: []) S3(assigned: [T5], revoked: [], learning: []) S4(assigned: [], revoked: [], learning: [T1]) Note that T2 is unassigned, and S4 is learning T1 which has no current active task. We could rebalance T1, T2 immediately. S2 performs task assignments: S2(assigned: [T3, T4], revoked: [], learning: []) S3(assigned: [T5, T2], revoked: [], learning: []) S4(assigned: [T1], revoked: [], learning: []) Now the group reaches balance.
Optimizations
Stateful vs Stateless Tasks
For stateless tasks the ownership transfer should happen immediately without the need of a learning stage, because there is nothing to restore. We should fallback the algorithm towards KIP-415 where the stateless tasks will only be revoked during second rebalance. This feature requires us to add a new tag towards a stream task: "isStateful".
Eager Rebalance
Sometimes the restoration time of learner tasks are not equivalent. When assigned with 1+ tasks to replay, the stream worker could require immediate rebalance as a subset of learning tasks are finished in order to speed up the load balance and resource waste of double task processing, with the sacrifice of global efficiency by introducing many more rebalances. We could supply user with a config to decide whether they want to take eager approach or stable approach eventually, with some follow-up benchmark tools of the rebalance efficiency.
Example:
A stream worker S1 takes two learner tasks T1, T2, where restoring time time(T1) < time(T2). Under eager rebalance approach, the worker will call out rebalance immediately when T1 finishes replaying. While under conservative approach, worker will rejoin the group until it finishes replaying of both T1 and T2.
Standby Task Utilization
Don’t forget the original purpose of standby task is to mitigate the issue during scaling down. When performing learner assignment, we shall prioritize workers which currently have standby tasks that match learner assignment. Therefore the group should rebalance pretty soon and let the leaving member shutdown themselves fairly quickly.
Scale Down Timeout
Sometimes end user wants to reach a sweet spot between ongoing task transfer and streaming resource free-up. So we want to take a similar approach as KIP-415, where we shall introduce a client config to make sure the scale down is time-bounded. If the time takes to migrate tasks outperforms this config, the leaving member will shut down itself immediately instead of waiting for the final confirmation. And we could simply transfer learner tasks to active because they are now the best shot to own new tasks.
Task Tagging
Note that to make sure the above resource shuffling could happen as expected, we need to have the following task status indicators to be provided:
| Tag Name | Task Type | Explanation | prerequisites |
|---|---|---|---|
| isStateful | both | Indicate whether given task has a state to restore. | N/A |
| isLearner | standby | Indicate whether standby task is a learner task. | isStateful = True |
| beingLearned | active | Indicate whether active task is being learned by some other stream worker. | isStateful = True |
| isReady | standby | Indicate whether standby task is ready to serve as active task. | isLearner = True and isStateful = True |
| isLeaving | active | Indicate whether active task will be leaving the group soon. | isStateful = True |
Algorithm Details
The above examples are focusing more on demonstrating expected behaviors with KStream incremental rebalancing "end picture". However, we also want to have a holistic view of the new learner algorithm.
We shall assign tasks in the order of: active, learner and standby. The assignment will be broken down into following steps:
Algorithm incremental-rebalancing
Input Set of Tasks,
Set of Instances,
Set of Workers,
Where each worker contains:
Set of active Tasks,
Set of standby Tasks,
owned by which instance
Main Function
Separate out Tasks into stateful bucket and stateless bucket
Assign Stateful active tasks:
To instances with learner tasks that indicates "ready"
To previous owners
To unready learner tasks owners
To instances with standby tasks
To resource available instances
Assign Stateless tasks:
To previous owners
To instances with standby tasks
To resource available instances
Assign learner tasks:
To previous owners (no half way bounce at least in the first version)
To new coming instances with abundant resource (first version)
Move tasks out of heaviest loaded instances first
Assign standby tasks:
To instances without matching active tasks
To previous active task owners
To resource available instances
Based on num.standby.task config, this could take multiple rounds
Output Finalized Task Assignment
As long as the group members/ number of tasks are not changing, there should be a defined balanced stage instead of forever rebalancing.
Instances with standby tasks have higher priority to be chosen as learner task assignor. The standby task will convert to learner task immediately.
Also for the smooth delivery of all the features we have discussed so far, an iteration plan of algorithm is as below:
Version 1.0
Delivery goal: Scale up support, conservative rebalance
The goal of first version is to realize the foundation of learner algorithm by solving the following questions:
- Which task is qualified to be learned ? Require us to implement the tagging for stateful tasks.
- Which worker/instance should take learner task? We shall only apply the learner tasks to newly spinned hosts.
A bit explanation of 1.0 goal:
The reason we are hoping to only implement the learner task logic on new hosts is because there is a potential edge case that could break the existing naive learner assignment. When the number of tasks are much smaller than total cluster capacity, we could fall in endless resource shuffling.
We will care more about smooth transition over resource balance for stage one. This is because we do have some historical discussion on marking weight for different types of tasks. If we go ahead to aim for task balance too early, we are potentially in the position of over-optimization.
So we only gonna assign learner task only to "new comers", which means every stream worker will denote itself as "new member" when they don't have local information. The assignment, however, will be on the task .
Version 2.0
Delivery goal: Scale down support
We will focus on the delivery of scaling down support upon the success of version 1.0
- Tag for leaving members
- Create new tooling for marking instances as ready to scale down
- Scale down timeout implementation
Version 3.0
Delivery goal: eager rebalance analysis
A detailed analysis and benchmark support need to be built before fully devoting effort to this feature. Intuitively most applications should be able to tolerate minor discrepancy of task replaying time, while the cost of extra rebalances and increased debugging complexity are definitely things we are not in favor of.
The success of version 3.0 is upon version 1.0 success. This work could be done concurrently with version 2.0.
Version 4.0
Delivery goal: eventual balance
The 4.0 and the final version will take application eventual load balance into consideration. If we define a balancing factor x, the total number of tasks each instance owns should be within the range of +-x% of the expected number of tasks, which buffers some capacity in order to avoid imbalance.
We could even provide a stream.balancing.factor for the user to configure. The smaller this number sets to, the more strict the assignment will behave. If the factor is set to r, the number of tasks a host could own is (w/total tasks)
As we could see, there should be only exactly one learner task after each round of rebalance, and there should be exactly one corresponding active task at the same time.
Algorithm Trade-offs
We open a special section to discuss the trade-offs of the new algorithm, because it's important to understand the change motivation and make the proposal more robust.
More rebalances
The new algorithm will invoke many more rebalances than the current protocol as one could perceive. As we have discussed in the overall incremental rebalancing design, it is not always bad to have multiple rebalances when we do it wisely, and after KIP-345 we have a future proposal to avoid scale up rebalances for static members. The goal is to pre-register the members that are planning to be added. The broker coordinator will augment the member list and wait for all the new members to join the group before rebalancing, since by default stream application’s rebalance timeout is infinity. The conclusion is that: it is server’s responsibility to avoid excessive rebalance, and client’s responsibility to make each rebalance more efficient.
Metadata size increase
Since we are carrying over more information during rebalance, we should be alerted on the metadata size increase. So far the hard limit is 1MB per metadata response, which means if we carry over too much information, the new protocol could hit hard failure. This is a common pain point for finding better encoding scheme for metadata if we are promoting incremental rebalancing KIPs like 415 and 429. Some thoughts from Guozhang have been started in this JIRA and we will be planning to have a separate KIP discussing different encoding technologies and see which one could work.
Public Interfaces
We are going to add a new type of protocol called "streams" for the protocol type.
We will be adding following new configs:
| stream.rebalancing.mode Default: incremental Version 1.0 | The setting to help ensure no downtime upgrade of online application. Options : upgrading, incremental |
scale.down.timeout.ms Default: infinity Version 2.0 | Timeout in milliseconds to force terminate the stream worker when informed to be scaled down. |
learner.partial.rebalance Default : true Version 3.0 | If this config is set to true, new member will proactively trigger rebalance when it finishes restoring one task state each time, until it eventually finishes all the task replaying. Otherwise, new worker will batch the ready stage to ask for single round of rebalance. |
| stream.worker.balancing.factor Default: 2 Version 4.0 | The tolerance of task imbalance factor between hosts to trigger rebalance. |
Implementation Plan
We want to callout this portion because the algorithm we are gonna design is fairly complicated so far. To make sure the delivery is smooth with fundamental changes of KStream internals, I build a separate Google Doc here that could be sharable to outline the step of changes. Feel free to give your feedback on this plan while reviewing the algorithm, because some of the changes are highly coupled with internal changes. Without these details, the algorithm is not making sense.
Compatibility, Deprecation, and Migration Plan
Minimum Version Requirement
This change requires Kafka broker version >= 0.9, where broker will react with a rebalance when a normal consumer changes the encoded metadata. Client application needs to update to the earliest version which includes KIP-429 version 1.0 change.
Switching Protocol Type
As we have mentioned above, a new protocol type shall be created. To ensure smooth transition, we need to make sure the existing job doesn't fail. The workflow for upgrading is like below:
- Set the `stream.rebalancing.mode` to `upgrading`, where we will set the protocol type remaining to "consumer".
- Rolling restart the stream application, the change should be safe because we are not changing protocol type anyway.
In long term we are proposing a more smooth upgrade approach than the current one. However it requires broker upgrade which may not be trivial effort for the end user.
FAQ
Why do we call stream workers?
Rejected Alternatives
If there are alternative ways of accomplishing the same thing, what were they? The purpose of this section is to motivate why the design is the way it is and not some other way.