Current state: Accepted (2.4.0)
Discussion thread: link
JIRA: 
Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).
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.
This KIP is trying to customize the incremental rebalancing approach for Kafka consumer client, which will be beneficial for heavy-stateful consumers such as Kafka Streams applications.
In short, the goals of this KIP are:
As mentioned in motivation, 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 stream threads would
Join group with all currently assigned tasks revoked.
Wait until group assignment finish to get assigned tasks and resume working.
Replay the assigned tasks state.
Once all replay jobs finish, stream thread 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.
Below shows the process of the rebalance protocol with a new member joining the group.
Today Streams embed a full fledged Consumer client, which hard-code a ConsumerCoordinator inside. Streams then injects a StreamsPartitionAssignor to its pluggable PartitionAssignor interface and inside the StreamsPartitionAssignor we also have a TaskAssignor interface whose default implementation is StickyPartitionAssignor. Streams partition assignor logic today sites in the latter two classes. Hence the hierarchy today is:
KafkaConsumer -> ConsumerCoordinator -> StreamsPartitionAssignor -> StickyTaskAssignor. |
StreamsPartitionAssignor uses the subscription / assignment metadata byte array field to encode additional information for sticky partitions. More specifically on subscription:
KafkaConsumer: Subscription => TopicList SubscriptionInfo TopicList => List<String> UserData => Bytes ------------------ StreamsPartitionAssignor: UserData (encoded in version 4) => VersionId LatestSupportVersionId ClientUUID PrevTasks StandbyTasks EndPoint VersionId => Int32 LatestSupportVersionId => Int32 ClientUUID => 128bit PrevTasks => Set<TaskId> StandbyTasks => Set<TaskId> EndPoint => HostInfo |
And on assignment:
KafkaConsumer: Assignment = AssignedPartitions AssignmentInfo AssignedPartitions => List<String, List<Int32>> UserData => Bytes ------------------ StreamsPartitionAssignor: UserData (encoded in version 4) => VersionId, LatestSupportedVersionId, ActiveTasks, StandbyTasks, PartitionsByHost, ErrorCode VersionId => Int32 LatestSupportVersionId => Int32 ActiveTasks => List<TaskId> StandbyTasks => Map<TaskId, Set<TopicPartition>> PartitionsByHost => Map<HostInfo, Set<TopicPartition>> ErrorCode => Int32 |
We also have a StickyAssignor provided out of the box trying to mitigate the cost of unnecessary partition migrations. This assignor only relies on subscription metadata but not modifying assignment metadata, as follows:
KafkaConsumer: Subscription => TopicList SubscriptionInfo TopicList => List<String> UserData => Bytes ------------------ StickyAssignor: UserData (encoded in version 1) => AssignedPartitions AssignedPartitions => List<String, List<Int32>> |
The goal of this incremental protocol, is to fully leverage on the sticky assignors which will try to reassign partitions to its previous owners in best effort, such that we will revoke less partitions as possible since the revocation process is costly.
We will augment the consumer's rebalance protocol as proposed in Incremental Cooperative Rebalancing: Support and Policies with some tweaks compared to KIP-415. The key idea is that, instead of relying on the single rebalance's synchronization barrier to rebalance the group and hence enforce everyone to give up all the assigned partitions before joining the group as the new generation, we use consecutive rebalances where the end of the first rebalance will actually be used as the synchronization barrier.
More specifically, we would first inject more metadata at the consumer-layer, as:
KafkaConsumer: Subscription => TopicList UserData AssignedPartitions TopicList => List<String> UserData => Bytes AssignedPartitions => List<String, List<Int32>> // new field Assignment = AssignedPartitions UserData AssignedPartitions => List<String, List<Int32>> UserData => Bytes |
Note that it is compatible to inject additional fields after the assignor-specific SubscriptionInfo / AssignmentInfo bytes, since on serialization we would first call assignor to encode the info bytes, and then re-allocate larger buffer to append consumer-specific bytes; with the new protocol, we just need to append some fields before, and some fields (a.k.a. those new fields) after the assignor-specific info bytes, and vice-versa on deserialization. So adding fields after the assignor-bytes is still naturally compatible with the plug-in assignor. However there are indeed some compatibility challenges for the consumer protocol upgrade itself, which we will tackle below.
In addition, we want to resolve a long-lasting issue that when consumer's being kicked out of the group, since it no longer owns the partitions the `commit` call would doom to fail. To distinguish this case with the normal case that consumers are likely still within the group but just try to re-join, we introduce a new API into the consumer rebalance listener:
public interface ConsumerRebalanceListener {
void onPartitionsRevoked(Collection<TopicPartition> partitions);
void onPartitionsAssigned(Collection<TopicPartition> partitions);
// new API
default void onPartitionsLost(Collection<TopicPartition> partitions) {
onPartitionsRevoked(partitions);
}
} |
For users implementing this rebalance listener, they would not need to make code changes necessarily if they do not need to instantiate different logic; but they'd still need to recompile their implementation class. The semantics of these callbacks do differ in the new cooperative protocol however, so you should review your implementation to make sure there are no logical changes needed. For details, see ConsumerRebalanceListener and ConsumerPartitionAssignor Semantics below.
Note that adding new fields would increase the size of the request, especially in cases like Streams where user metadata has been heavily encoded wither assignor-specific metadata. We are working on with compression / reformation to reduce the user metadata footprint, and with that we believe adding this new field would not be pushing the size exceeding the message size limit.
Rebalance behavior of the consumer (captured in the consumer coordinator layer) would be changed as follows.
Below is a more illustrative of the different set of partitions and their assignment logic:
No changes required from the broker side, since this logic change is completely wrapped inside the consumer protocol / coordinator implementation itself, and to brokers it is just the same as previous version's rebalances.
Note that one minor difference compared with KIP-415 is that we do not introduce the scheduledDelay in the protocol, but instead the consumer will trigger rebalance immediately. This is because the consumer protocol would applies to all consumers (including streams) and hence should be kept simple, and also because KIP-345 is being developed in parallel which is aimed for tackling the scaling out / rolling bounce scenarios already.
We would omit the common scenarios description here since it is already covered in KIP-415, which is very similar to this KIP with the difference of the scheduledDelay above.
NOTE that for this new algorithm to be effective in reducing rebalance costs, it is really expecting the plug-in assignor to be "sticky" in some way, such that the diff of the newly-assigned-partitions and the existing-assigned-partitions can be small, and hence only a few subset of the total number of partitions need to be revoked / migrated at each rebalance in practice – otherwise, we are just paying more rebalance for little benefits. We will talk about how sticky StreamsPartitionAssignor would be updated accordingly in Part II.
Now the new process for the same new-member-joining example becomes the following under the new protocol (note that on the existing member we will now only call revoke on b-0 once, and no longer revoking both a-0 and b-0 and then later assigning a-0 again:
Today, when a user customized rebalance listener callback throws an exception, as long as it is not WakeupException / InterruptedException it will be swallowed and logged with an error. This has been complained by our users that it was not an efficient way for notifying them when it happened ().
In this KIP, we propose to slightly modify the semantics of the listener, such that the callback is used more like a "notification" to users about what gets assigned and what gets revoked, etc, and even exceptions gets thrown, it would not cause any of these assignment / revocation results to be changed. Also, if the ConsumerCoordinator found out that there's no need to notify since no "new" partitions have been assigned or revoked, the corresponding callback would not be triggered either.
More specifically, we will change this behavior as well:
Upon capturing the error, users can do the following depend on their exception handling logic:
To give a concrete example, suppose your current assigned partitions are {1,2}, and the newly assigned partitions are {2,3}. The consumer will call onPartitionsRevoked(1) and then onPartitionsAssigned(3). Suppose the former failed with an exception, ConsumerCoordinator would still proceed to complete the latter callback (and assume the latter callback succeeds). If users decide to retry, it is still considered as having successfully changed to {2, 3} – i.e. we consider all of the effects user indicated in the callback have taken place.
As part of this KIP we will also add some metrics on the consumer side related to rebalancing. These include:
Since we've already encoded the assigned partitions at the consumer protocol layer, for consumer's sticky partitioner we are effectively duplicating this data at both consumer protocol and assignor's user data. Similarly we have a StreamsPartitionAssignor which is sticky as well but relying on its own user data to do it. We have added a new out-of-the-box assignor for users that leverages the Subscription's built-in ownedPartitions. Consumer groups plugging in the new "cooperative-sticky" assignor will follow the incremental cooperative rebalancing protocol. A specific upgrade path is required for users wishing to do a rolling upgrade to the new cooperative assignor, as described in the compatibility section below.
Users may also wish to implement their own custom assignor, or are already doing so, and want to use the new cooperative protocol. Any assignor that returns COOPERATIVE among the list in #supportedProtocols indicates to the ConsumerCoordinator that it should use the cooperative protocol, and must follow specific assignment logic. First, the assignor should try and be as "sticky" as possible, meaning it should assign partitions back to their previous owner as much as possible. The assignor can leverage the new ownedPartitions field that the Subscription has been augmented with in order to determine the previous assignment. Note that "stickiness" is important for the cooperative protocol to be effective, as in the limit that the new assignment is totally different than the previous one then the cooperative protocol just reduces to the old eager protocol as each member will have to completely revoke all partitions and get a whole new assignment. In addition, any time a partition has to be revoked it will trigger a follow up rebalance, so the assignor should seek to minimize partition movement. Second, in order to ensure safe resource management and clear ownership, the assignor must make sure a partition is revoked by its previous owner before it can be assigned to a new one. Practically speaking, this means that the assignor should generate its "intended" assignment and then check against the previous assignment to see if any partitions are being revoked (that is, in the ownedPartitions but not in the new assignment for a given consumer). If that is the case, that partition should be removed from the new assignment for that round, and wait until it has been revoked so that it can be assigned to its final owner in the second rebalance. See the CooperativeStickyAssignor implementation for an example.
Note that the CooperativeStickyAssignor is for use by plain consumer clients – the existing StreamsPartitionAssignor has simply been modified to support cooperative so users should not try to plug in the CooperativeStickyAssignor (or any other). The upgrade path for Streams differs slightly from that of the clients CooperativeStickyAssignor as well. See the section on Streams below for details.
If you do choose to plug in a cooperative assignor and have also implemented a custom ConsumerRebalanceListener, you should be aware of how the semantics and ordering of these callbacks has changed. In the eager protocol, the timeline of a rebalance is always exactly as follows:
In the cooperative protocol, the timeline is less exact as some methods may or may not be called, at different times, and on different sets of partitions. This will instead look something like the following:
The italics indicate a callback that may not be called at all during a rebalance. Take note in particular that it is possible for #onPartitionsRevoked to never be invoked at all during a rebalance, and should not be relied on to signal that a rebalance has started. The #onPartitionsAssigned callback will however always be called, and can therefore be used to signal to your app that a rebalance has just completed.
Since we are modifying the consumer protocol as above, we need to design the upgrade path to enable consumers upgrade to the new rebalance protocol in an online manner. In fact, the most tricky thing for this KIP is actually how to support safe online upgrade path, such that even if users made mistakes and not strictly following the instructions, we can pause the consumer from proceeding and hence eventually users will realized it by seeing e.g. consumer lags and investigating logs, rather than letting them to fall into an undefined behavior or even worse, having some partitions to be owned by more than one member.
Note that since we are injecting additional fields at the end of the consumer protocol, the new protocol would still be compatible with the old version. That means, an old-versioned consumer would still be able to deserialize a newer-versioned protocol data (as long as we only append new fields at the end, this would be the case).
However, when consumers with V1 is joining the group, there's a key behavioral difference that they would NOT revoke their partitions, and hence it is not safe to re-assign any of their partitions as we did in the current (V0) assignment logic. That means, the leader can only proceed the assignment when it knew that all the members are either on V0, or V1 versions.
Another thing to keep in mind that, if the leader itself is still on older version, it would still be able to deserialize the V1 subscription protocol as V0, by ignoring the additional fields, and hence it may "think" everyone is still on V0, while some of them may actually be on the newer version.
The key idea to resolve this, is to let Assignor implementors themselves to indicate the ConsumerCoordinator whether they are compatible with the protocol, and then relying ton multi-assignor protocols for a safe upgrade path: users need to keep two assignors when switching the rebalance protocol, and after that they can use another rolling bounce to remove the old versioned protocol.
More specifically, we will introduce the new public API ConsumerPartitionAssignor class and its #Subscription / #Assignment (the old classes are actually in `internal` package mistakenly, so we use this KIP to deprecate that class with this new one, along with augmented methods) as follows:
ConsumerGroupMetadata {
public String groupId();
public int generationId();
public String memberId();
public Optional<String> groupInstanceId();
}
public interface ConsumerPartitionAssignor {
/**
* Return serialized data that will be included in the {@link Subscription} sent to the leader
* and can be leveraged in {@link #assign(Cluster, GroupSubscription)} ((e.g. local host/rack information)
*
* @param topics Topics subscribed to through {@link org.apache.kafka.clients.consumer.KafkaConsumer#subscribe(java.util.Collection)}
* and variants
* @return nullable subscription user data
*/
default ByteBuffer subscriptionUserData(Set<String> topics) {
return null;
}
/**
* Perform the group assignment given the member subscriptions and current cluster metadata.
* @param metadata Current topic/broker metadata known by consumer
* @param groupSubscription Subscriptions from all members including metadata provided through {@link #subscriptionUserData(Set)}
* @return A map from the members to their respective assignments. This should have one entry
* for each member in the input subscription map.
*/
GroupAssignment assign(Cluster metadata, GroupSubscription groupSubscription);
/**
* Callback which is invoked when a group member receives its assignment from the leader.
* @param assignment The local member's assignment as provided by the leader in {@link #assign(Cluster, GroupSubscription)}
* @param metadata Additional metadata on the consumer (optional)
*/
default void onAssignment(Assignment assignment, ConsumerGroupMetadata metadata) {
}
/**
* Indicate which rebalance protocol this assignor works with;
* By default it should always work with {@link RebalanceProtocol#EAGER}.
*/
default List<RebalanceProtocol> supportedProtocols() {
return Collections.singletonList(RebalanceProtocol.EAGER);
}
/**
* Return the version of the assignor which indicates how the user metadata encodings
* and the assignment algorithm gets evolved.
*/
default short version() {
return (short) 0;
}
/**
* Unique name for this assignor (e.g. "range" or "roundrobin" or "sticky"). Note, this is not required
* to be the same as the class name specified in {@link ConsumerConfig#PARTITION_ASSIGNMENT_STRATEGY_CONFIG}
* @return non-null unique name
*/
String name();
final class Subscription {
public List<String> topics();
public ByteBuffer userData();
public List<TopicPartition> ownedPartitions();
public void setGroupInstanceId(Optional<String> groupInstanceId);
public Optional<String> groupInstanceId();
}
final class Assignment {
public List<TopicPartition> partitions();
public ByteBuffer userData();
}
final class GroupSubscription {
public GroupSubscription(Map<String, Subscription> subscriptions);
public Map<String, Subscription> groupSubscription();
}
final class GroupAssignment {
public GroupAssignment(Map<String, Assignment> assignments);
public Map<String, Assignment> groupAssignment();
}
/**
* The rebalance protocol defines partition assignment and revocation semantics. The purpose is to establish a
* consistent set of rules that all consumers in a group follow in order to transfer ownership of a partition.
* {@link ConsumerPartitionAssignor} implementors can claim supporting one or more rebalance protocols via the
* {@link ConsumerPartitionAssignor#supportedProtocols()}, and it is their responsibility to respect the rules
* of those protocols in their {@link ConsumerPartitionAssignor#assign(Cluster, GroupSubscription)} implementations.
* Failures to follow the rules of the supported protocols would lead to runtime error or undefined behavior.
*
* The {@link RebalanceProtocol#EAGER} rebalance protocol requires a consumer to always revoke all its owned
* partitions before participating in a rebalance event. It therefore allows a complete reshuffling of the assignment.
*
* {@link RebalanceProtocol#COOPERATIVE} rebalance protocol allows a consumer to retain its currently owned
* partitions before participating in a rebalance event. The assignor should not reassign any owned partitions
* immediately, but instead may indicate consumers the need for partition revocation so that the revoked
* partitions can be reassigned to other consumers in the next rebalance event. This is designed for sticky assignment
* logic which attempts to minimize partition reassignment with cooperative adjustments.
*/
enum RebalanceProtocol {
EAGER((byte) 0), COOPERATIVE((byte) 1);
private final byte id;
RebalanceProtocol(byte id) {
this.id = id;
}
public byte id() {
return id;
}
public static RebalanceProtocol forId(byte id) {
switch (id) {
case 0:
return EAGER;
case 1:
return COOPERATIVE;
default:
throw new IllegalArgumentException("Unknown rebalance protocol id: " + id);
}
}
}
} |
Note the semantical difference between the above added fields:
With the existing built-in Assignor implementations, they will be updated accordingly:
| Highest Version | Supported Strategy | Notes | |
|---|---|---|---|
| RangeAssignor | 0 | Eager | Current default value. |
| RoundRobinAssignor | 0 | Eager | |
| StickyAssignor | 0 | Eager | |
| CooperativeStickyAssignor | 0 | Eager, Cooperative | To be default value in 3.0 |
| StreamsAssignor | 4 | Eager, Cooperative |
The reason we make "range" and "round-robin" to not support cooperative rebalance is that, this protocol implicitly relies on the assignor to be somewhat sticky to make benefits by trading an extra rebalance. However, for these two assignors, they would not be sticky (although sometimes range may luckily reassign partitions back to old owners, it is not best-effort) and hence we've decided to not make them be selected for cooperative protocol. The existing StickyAssignor was not made to support Cooperative to ensure users follow the smooth upgrade path outlined below, and avoid running into trouble if they already use the StickyAssignor and blindly upgrade.
The ConsumerCoordinator layer, on the other hand, will select which protocol to use based on the first assignor specified in its configs, as the following:
The specific upgrade path is described below. Note that this will be different depending on whether you have a plain consumer app or a Streams app, so make sure to follow the appropriate one.
From the user's perspective, the upgrade path of leveraging new protocols is similar to switching to a new assignor. For example, assuming the current version of Kafka consumer is 2.2 and "range" assignor is specified in the config (or no assignor is configured, which is identical as the RangeAssignor is the default below 3.0). The upgrade path would be:
The key point behind this two rolling bounce is that, we want to avoid the situation where leader is on old byte-code and only recognize "eager", but due to compatibility would still be able to deserialize the new protocol data from newer versioned members, and hence just go ahead and do the assignment while new versioned members did not revoke their partitions before joining the group. Note the difference with KIP-415 here: since on consumer we do not have the luxury to leverage on list of built-in assignors since it is user-customizable and hence would be black box to the consumer coordinator, we'd need two rolling bounces instead of one rolling bounce to complete the upgrade, whereas Connect only need one rolling bounce.
Streams embeds its own assignor which will determine the supported protocol. In 2.4 it will enable cooperative rebalancing by default, so a specific upgrade path must be followed in order to safely upgrade to 2.4+ since assignors on the earlier versions do not know how to handle a cooperative rebalance safely (or even what it is). To do so you must perform two rolling bounces as follows:
Note that as long as the UPGRADE_FROM parameter is set to 2.3 or lower, Streams will stay on EAGER. If for some reason you decide you would like to stay on eager, or return to it after switching to cooperative, you can simply set/leave the UPGRADE_FROM parameter in place. If you intend to use this config to stay on eager even after upgrading, it is recommended that you set it to a version in the range 2.0 - 2.3 as setting it to earlier than 2.0 will force Streams to remain stuck on an earlier metadata version.
These changes should all be fairly transparent to Streams apps, as there are no semantics only improved rebalancing performance. However, users using Interactive Queries (IQ) or implementing a StateListener will notice that Streams spends less time in the REBALANCING state, as we will not transition to that until the end of the rebalance. This means all owned stores will remain open for IQ while the rebalance is in progress, and Streams will continue to restore active tasks if there are any that are not yet running, and will process standbys if there aren't.
As summarized in , a further optimization would be to allow consumers to still return messages belong to its owned partitions even when it is within a rebalance.
In order to do this, we'd need to allow the consumer#commit API to throw RebalanceInProgressException if it is committing offset while a rebalance is undergoing.
/**
* ...
*
* @throws org.apache.kafka.common.errors.RebalanceInProgressException if the consumer instance is in the middle of a rebalance
* so it is not yet determined which partitions would be assigned to the consumer. In such cases you can first
* complete the rebalance by calling {@link #poll(Duration)} and commit can be reconsidered afterwards.
* NOTE when you reconsider committing after the rebalance, the assigned partitions may have changed,
* and also for those partitions that are still assigned their fetch positions may have changed too
* if more records are returned from the {@link #poll(Duration)} call.
* ...
*/
@Override
public void commitSync() {
commitSync(Duration.ofMillis(defaultApiTimeoutMs));
} |
With this optimization (implemented in 2.5.0) consumer groups can continue to process some records even while a rebalance is in progress. This means that in addition to processing standby and restoring tasks during a rebalance, Streams apps will be able to make progress on running active tasks.
Note that the above upgrade path relies on the fact that COOPERATIVE and EAGER members can work together within the same generation as long as the leader recognize both; this however may not be true moving forward if we add a third rebalance protocol. One idea to resolve this in the future is that, instead of letting the members to decide which protocol to use "locally" before sending the join-group request, we will use Heartbeat request / response to piggy-back the communication of the group's supported protocols and let members to rely on that "global" information to make decisions. More specifically:
The broker, upon receiving the heartbeat request, if the indicated supported protocols does not contain the one it has decided to use for the up-coming rebalance, then reply with an fatal error.
One semi-orthogonal issue with the current assignor implementation is that, the same assignor may evolve its encoded user metadata byte format; and when that happens, if the leader does not recognize the new format it will cause it to crash. For example today StreamsAssignor has evolved to version 4 of its user metadata bytes, and to cope with old versioned leader we have introduced the "version probing" feature which will require additional rebalances.
To resolve this issue, I'd propose to modify the JoinGroup protocol in this KIP as well to take the read `protocol version` from the PartitionAssignor.
JoinGroupRequest (v5) => groupId SessionTimeout RebalanceTimeout memberId ProtocolType Protocols ProtocolVersion GroupId => String SessionTimeout => Int32 RebalanceTimeout => Int32 MemberId => String ProtocolType => String Protocols => List<Protocol> Protocol (v0) => ProtocolName ProtocolMetadata ProtocolName => String ProtocolMetadata => Bytes ProtocolVersion => Int32 // new field |
And then on the broker side, when choosing the leader it will pick the one with the highest protocol version instead of picking it "first come first serve".
Although this change will not benefit the upgrade path at this time, in the future if we need to upgrade the assignor again, as long as they would not change the rebalance semantics (e.g. like we did in this KIP, from "eager" to "cooperative") we can actually use one rolling bounce instead since as long as there's one member on the newer version, that consumer will be picked.
For example, this can also help saving "version probing" cost on Streams as well: suppose we augment the join group schema with `protocol version` in Kafka version 2.3, and then with both brokers and clients being in version 2.3+, on the first rolling bounce where subscription and assignment schema and / or user metadata has changed, this protocol version will be bumped. On the broker side, when receiving all member's join-group request, it will choose the one that has the highest protocol version (also it assumes higher versioned protocol is always backward compatible, i.e. the coordinator can recognize lower versioned protocol as well) and select it as the leader. Then the leader can decide, based on its received and deserialized subscription information, how to assign partitions and how to encode the assignment accordingly so that everyone can understand it. With this, in Streams for example, no version probing would be needed since we are guaranteed the leader knows everyone's version -- again it is assuming that higher versioned protocol is always backward compatible -- and hence can successfully do the assignment at that round.
This proposal depends on user's correct behavior that when upgrading to the new version everyone is still using "eager" protocol. But if user mades mistakes, it is still not going to fall into undefined behavior, as the assignor mechanism will guarantee that we must form a consensus on the protocol names, and whoever does not support the chosen one will be kicked out of the group and hence users would be notified about the mis-configure.
There's a few edge cases to illustrate this:
In some assignors like StreamsPartitionAssignor, there are secondary type of resources (e.g. standby-tasks) being assigned in addition to the active partition assignment which may not obey the cooperative manner since the assignor may not know the "currently owned" list of such resources from the subscription, and therefore it could immediately reassign such resources from one owner to another even if they are not revoked at all from the current owner yet.
In this case, as long as the secondary resources does not require any synchronization between its owners like the active partition (e.g. reading previously committed offsets) it should be fine. Take StreamsPartitionAssignor for example, its standby-task are owned separately and independently between instances and are only depend on local state directories (including the data as well as the checkpoint file corresponding to the offset). When a standby task is assigned to a host:
So more generally, an assignor supporting COOPERATIVE protocol and is assigning other types of resources than the partition as well would need to make sure that these resource assignment is also respecting the COOPERATIVE rules, or their assignment does not require any synchronization between instances as well.
If a consumer is downgraded incorrectly after the above upgrade path is complete: i.e. it just replaced with the old jar without changing any configs, it is treated as first leaving the group, and then rejoining the group as an new member with "eager". This situation can also be reflected when a new member with "eager" is joining a group (probably mistakenly) whereas everyone else have been switched to "cooperative".
At the moment, no consensus protocol can be chosen with this member joining, and hence this member or everyone else will be kicked out of the group with a fatal error.
The right way to downgrade is to perform a rolling bounce to first add back the RangeAssignor (or whichever assignor you wish to use), and then perform a second rolling bounce in which you remove the CooperativeStickyAssignor and also downgrade the consumers to the old byte code. It's essentially the same as the upgrade path, but in reverse.
This is to quickly summarize what we would change on the public interface exposed to the user.
This change requires Kafka broker version >= 0.9, where broker will react with a rebalance when a normal consumer rejoin the encoded metadata. Client application needs to update to the earliest version which includes KIP-429 version 1.0 change.
As we have mentioned above, a new protocol type shall be created. To ensure smooth upgrade, we need to make sure the existing job won't fail. The procedure is described above.
Another solution that we have discussed about, is to make each assignor only supports one protocol, i.e.:
interface PartitionAssignor {
// existing interfaces
short version(); // new API, the version of the assignor which indicate the user metadata / algorithmic difference.
String name();
PartitionAssignorProtocol supportedProtocol(); // new API, indicate the ConsumerCoordinator the rebalance strategy it would work with.
} |
The existing built-in Assignor implementations will then be updated to:
| Highest Version | Supported Strategy | Notes | |
|---|---|---|---|
| RangeAssignor | 0 | Eager | Current default. |
| RoundRobinAssignor | 0 | Eager | |
| StickAssignor (old) | 0 | Eager | |
| StickAssignor (new) | 0 | Cooperative | Will be new default in 3.0 |
| StreamsAssignor (old) | 4 | Eager | |
| StreamsAssignor (new) | 4 | Cooperative |
Although it makes the upgrade path simpler since we would no longer need the "rebalance.protocol" config on consumer anymore, while just encoding multiple assignors during the first rolling bounce of the upgrade path, it requires duplicated assignor class (of course, the new class could just be extending from the old one and there's not much LOC duplicated) which is a bit cumbersome.