THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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Status
Current state: Under discussionAccepted
Discussion thread: here
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Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).
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This KIP presents an overall vision for a scalable post-ZooKeeper Kafka. In order to present the big picture, I have mostly left out details like RPC formats, on-disk formats, and so on. We will want to have follow-on KIPs to describe each step in greater detail. This is similar to KIP-4, which presented an overall vision which subsequent KIPs enlarged upon.
Overview
Currently, a Kafka cluster contains several broker nodes, and an external quorum of ZooKeeper nodes. We have pictured 4 broker nodes and 3 ZooKeeper nodes in this diagram. This is a typical size for a small cluster. The controller (depicted in orange) loads its state from the ZooKeeper quorum after it is elected. The lines extending from the controller to the other nodes in the broker represent the updates which the controller pushes, such as LeaderAndIsr and UpdateMetadata messages.
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In the proposed architecture, three controller nodes substitute for the three ZooKeeper nodes. The controller nodes and the broker nodes run in separate JVMs. The controller nodes elect a single leader for the metadata partition, shown in orange. Instead of the controller pushing out updates to the brokers, the brokers pull metadata updates from this leader. That is why the arrows point towards the controller rather than away.
The Controller Quorum
The controller nodes comprise a Raft quorum which manages the metadata log. This log contains information about each change to the cluster metadata. Everything that is currently stored in ZooKeeper, such as topics, partitions, ISRs, configurations, and so on, will be stored in this log.
Using the Raft algorithm, the controller nodes will elect a leader from amongst themselves, without relying on any external system. The leader of the metadata log is called the active controller. The active controller handles all RPCs made from the brokers. The follower controllers replicate the data which is written to the active controller, and serve as hot standbys if the active controller should fail.
Just like ZooKeeper, Raft requires a majority of nodes to be running in order to continue running. Therefore, a three-node controller cluster can survive one failure. A five-node controller cluster can survive two failures, and so on.
Periodically, the controllers will write out a snapshot of the metadata to disk. While this is conceptually similar to compaction, the code path will be a bit different because we can simply read the state from memory rather than re-reading the log from disk.
Broker Metadata Management
Instead of the controller pushing out updates to the other brokers, those brokers will fetch updates from the active controller via the new MetadataFetch API.
A MetadataFetch is similar to a fetch request. Just like with a fetch request, the broker will track the offset of the last updates it fetched, and only request newer updates from the active controller.
The broker will persist the metadata it fetched to disk. This will allow the broker to start up very quickly, even if there are hundreds of thousands or even millions of partitions. (Note that since this persistence is an optimization, we can leave it out of the first version, if it makes development easier.)
Most of the time, the broker should only need to fetch the deltas, not the full state. However, if the broker is too far behind the active controller, or if the broker has no cached metadata at all, the controller will send a full metadata image rather than a series of deltas.
The broker will periodically ask for metadata updates from the active controller. This request will double as a heartbeat, letting the controller know that the broker is alive.
Note that while this KIP only discusses broker metadata management, client metadata management is important for scalability as well. Once the infrastructure for sending incremental metadata updates exists, we will want to use it for clients as well as for brokers. After all, there are typically a lot more clients than brokers. As the number of partitions grows, it will become more and more important to deliver metadata updates incrementally to clients that are interested in many partitions. We will discuss this further in follow-on KIPs.
The Broker State Machine
Currently, brokers register themselves with ZooKeeper right after they start up. This registration accomplishes two things: it lets the broker know whether it has been elected as the controller, and it lets other nodes know how to contact it.
In the post-ZooKeeper world, brokers will register themselves with the controller quorum, rather than with ZooKeeper. The MetadataFetch API serves as this registration mechanism.
Currently, if a broker loses its ZooKeeper session, the controller removes it from the cluster metadata. In the post-ZooKeeper world, the active controller removes a broker from the cluster metadata if it has not sent a MetadataFetch heartbeat in a long enough time.
In the current world, a broker which can contact ZooKeeper but which is partitioned from the active controller will continue serving user requests, but will not receive any metadata updates. This can lead to some confusing and difficult situations. For example, a producer using acks=1 might continue to produce to a leader that actually was not the leader any more, but which failed to receive the controller's LeaderAndIsrRequest moving the leadership.
In the post-ZK world, cluster membership is integrated with metadata updates. Brokers cannot continue to be members of the cluster if they cannot receive metadata updates. While it is still possible for a broker to be partitioned from a particular client, the broker will be removed from the cluster if it is partitioned from the controller.
Broker States
Offline
When the broker process is in the Offline state, it is either not running at all, or in the process of performing single-node tasks needed to starting up such as initializing the JVM or performing log recovery.
Fenced
When the broker is in the Fenced state, it will not respond to RPCs from clients. The broker will be in the fenced state when starting up and attempting to fetch the newest metadata. It will re-enter the fenced state if it can't contact the active controller. Fenced brokers should be omitted from the metadata sent to clients.
Online
When a broker is online, it is ready to respond to requests from clients.
Stopping
Brokers enter the stopping state when they receive a SIGINT. This indicates that the system administrator wants to shut down the broker.
When a broker is stopping, it is still running, but we are trying to migrate the partition leaders off of the broker.
Eventually, the controller will ask the broker to finally go offline, by returning a special result code in the MetadataFetchResponse. Alternately, the broker will shut down if the leaders can't be moved in a predetermined amount of time.
Transitioning some existing APIs to Controller-Only
Many operations that were formerly performed by a direct write to ZooKeeper will become controller operations instead. For example, changing configurations, altering ACLs that are stored with the default Authorizer, and so on.
New versions of the clients should send these operations directly to the controller. This is a backwards compatible change: it will work with both old and new clusters. In order to preserve compatibility with old clients that sent these operations to a random broker, the brokers will forward these requests to the active controller.
New Controller APIs
In some cases, we will need to create a new API to replace an operation that was formerly done via ZooKeeper. One example of this is that when the leader of a partition wants to modify the in-sync replica set, it currently modifies ZooKeeper directly In the post-ZK world, the leader will make an RPC to the controller instead.
Removing Direct ZooKeeper Access from Tools
Currently, some tools and scripts directly contact ZooKeeper. In a post-ZooKeeper world, these tools must use Kafka APIs instead. Fortunately, "KIP-4: Command line and centralized administrative operations" began the task of removing direct ZooKeeper access several years ago, and it is nearly complete.
Compatibility, Deprecation, and Migration Plan
Client Compatibility
We will preserve compatibility with the existing Kafka clients. In some cases, the existing clients will take a less efficient code path. For example, the brokers may need to forward their requests to the controller.
Bridge Release
The overall plan for compatibility is to create a "bridge release" of Kafka where the ZooKeeper dependency is well-isolated. We will be able to upgrade from any version of Kafka to this bridge release, and from the bridge release to a post-ZK release. When upgrading from an earlier release to a post-ZK release, the upgrade must be done in two steps: first, you must upgrade to the bridge release, and then you must upgrade to the post-ZK release.
Rolling Upgrade
The rolling upgrade from the bridge release will take several steps.
Upgrade to the Bridge Release
The cluster must be upgraded to the bridge release, if it isn't already.
Start the Controller Quorum Nodes
We will configure the controller quorum nodes with the address of the ZooKeeper quorum. Once the controller quorum is established, the active controller will enter its node information into /brokers/ids and overwrite the /controller node with its ID. This will prevent any of the un-upgraded broker nodes from becoming the controller at any future point during the rolling upgrade.
Once it has taken over the /controller node, the active controller will proceed to load the full state of ZooKeeper. It will write out this information to the quorum's metadata storage. After this point, the metadata quorum will be the metadata store of record, rather than the data in ZooKeeper.
We do not need to worry about the ZooKeeper state getting concurrently modified during this loading process. In the bridge release, neither the tools nor the non-controller brokers will modify ZooKeeper.
The new controller will monitor ZooKeeper for legacy broker node registrations. It will know how to send the legacy "push" metadata requests to those nodes, during the transition period.
Roll the Broker Nodes
We will roll the broker nodes as usual. The new broker nodes will not contact ZooKeeper. If the configuration for the zookeeper server addresses is left in the configuration, it will be ignored.
Roll the Controller Quorum
Once the last broker node has been rolled, there will be no more need for ZooKeeper. We will remove it from the configuration of the controller quorum nodes, and then roll the controller quorum to fully remove it.
Rejected Alternatives
Combined Controller and Broker Nodes
We could have combined the broker and the controller in the same JVM. This would have the advantage of minimizing the number of JVMs.
However, there are several advantages to keeping them separate. One is that the deployment model is more familiar to Kafka administrators. If they had a certain number of ZooKeeper nodes previously, they can just upgrade to having the same number of controller nodes without rethinking cluster sizing or topology.
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Note that although the controller processes are logically separate from the broker processes, they need not be physically separate. In some cases, it may make sense to deploy some or all of the controller processes on the same node as the broker processes. This is similar to how ZooKeeper processes may be deployed on the same nodes as Kafka brokers today in smaller clusters. As per usual, all sorts of deployment options are possible, including running in the same JVM.
The Controller Quorum
The controller nodes comprise a Raft quorum which manages the metadata log. This log contains information about each change to the cluster metadata. Everything that is currently stored in ZooKeeper, such as topics, partitions, ISRs, configurations, and so on, will be stored in this log.
Using the Raft algorithm, the controller nodes will elect a leader from amongst themselves, without relying on any external system. The leader of the metadata log is called the active controller. The active controller handles all RPCs made from the brokers. The follower controllers replicate the data which is written to the active controller, and serve as hot standbys if the active controller should fail. Because the controllers will now all track the latest state, controller failover will not require a lengthy reloading period where we transfer all the state to the new controller.
Just like ZooKeeper, Raft requires a majority of nodes to be running in order to continue running. Therefore, a three-node controller cluster can survive one failure. A five-node controller cluster can survive two failures, and so on.
Periodically, the controllers will write out a snapshot of the metadata to disk. While this is conceptually similar to compaction, the code path will be a bit different because we can simply read the state from memory rather than re-reading the log from disk.
Broker Metadata Management
Instead of the controller pushing out updates to the other brokers, those brokers will fetch updates from the active controller via the new MetadataFetch API.
A MetadataFetch is similar to a fetch request. Just like with a fetch request, the broker will track the offset of the last updates it fetched, and only request newer updates from the active controller.
The broker will persist the metadata it fetched to disk. This will allow the broker to start up very quickly, even if there are hundreds of thousands or even millions of partitions. (Note that since this persistence is an optimization, we can leave it out of the first version, if it makes development easier.)
Most of the time, the broker should only need to fetch the deltas, not the full state. However, if the broker is too far behind the active controller, or if the broker has no cached metadata at all, the controller will send a full metadata image rather than a series of deltas.
The broker will periodically ask for metadata updates from the active controller. This request will double as a heartbeat, letting the controller know that the broker is alive.
Note that while this KIP only discusses broker metadata management, client metadata management is important for scalability as well. Once the infrastructure for sending incremental metadata updates exists, we will want to use it for clients as well as for brokers. After all, there are typically a lot more clients than brokers. As the number of partitions grows, it will become more and more important to deliver metadata updates incrementally to clients that are interested in many partitions. We will discuss this further in follow-on KIPs.
The Broker State Machine
Currently, brokers register themselves with ZooKeeper right after they start up. This registration accomplishes two things: it lets the broker know whether it has been elected as the controller, and it lets other nodes know how to contact it.
In the post-ZooKeeper world, brokers will register themselves with the controller quorum, rather than with ZooKeeper.
Currently, if a broker loses its ZooKeeper session, the controller removes it from the cluster metadata. In the post-ZooKeeper world, the active controller removes a broker from the cluster metadata if it has not sent a MetadataFetch heartbeat in a long enough time.
In the current world, a broker which can contact ZooKeeper but which is partitioned from the controller will continue serving user requests, but will not receive any metadata updates. This can lead to some confusing and difficult situations. For example, a producer using acks=1 might continue to produce to a leader that actually was not the leader any more, but which failed to receive the controller's LeaderAndIsrRequest moving the leadership.
In the post-ZK world, cluster membership is integrated with metadata updates. Brokers cannot continue to be members of the cluster if they cannot receive metadata updates. While it is still possible for a broker to be partitioned from a particular client, the broker will be removed from the cluster if it is partitioned from the controller.
Broker States
Offline
When the broker process is in the Offline state, it is either not running at all, or in the process of performing single-node tasks needed to starting up such as initializing the JVM or performing log recovery.
Fenced
When the broker is in the Fenced state, it will not respond to RPCs from clients. The broker will be in the fenced state when starting up and attempting to fetch the newest metadata. It will re-enter the fenced state if it can't contact the active controller. Fenced brokers should be omitted from the metadata sent to clients.
Online
When a broker is online, it is ready to respond to requests from clients.
Stopping
Brokers enter the stopping state when they receive a SIGINT. This indicates that the system administrator wants to shut down the broker.
When a broker is stopping, it is still running, but we are trying to migrate the partition leaders off of the broker.
Eventually, the active controller will ask the broker to finally go offline, by returning a special result code in the MetadataFetchResponse. Alternately, the broker will shut down if the leaders can't be moved in a predetermined amount of time.
Transitioning some existing APIs to Controller-Only
Many operations that were formerly performed by a direct write to ZooKeeper will become controller operations instead. For example, changing configurations, altering ACLs that are stored with the default Authorizer, and so on.
New versions of the clients should send these operations directly to the active controller. This is a backwards compatible change: it will work with both old and new clusters. In order to preserve compatibility with old clients that sent these operations to a random broker, the brokers will forward these requests to the active controller.
New Controller APIs
In some cases, we will need to create a new API to replace an operation that was formerly done via ZooKeeper. One example of this is that when the leader of a partition wants to modify the in-sync replica set, it currently modifies ZooKeeper directly In the post-ZK world, the leader will make an RPC to the active controller instead.
Removing Direct ZooKeeper Access from Tools
Currently, some tools and scripts directly contact ZooKeeper. In a post-ZooKeeper world, these tools must use Kafka APIs instead. Fortunately, "KIP-4: Command line and centralized administrative operations" began the task of removing direct ZooKeeper access several years ago, and it is nearly complete.
Compatibility, Deprecation, and Migration Plan
Client Compatibility
We will preserve compatibility with the existing Kafka clients. In some cases, the existing clients will take a less efficient code path. For example, the brokers may need to forward their requests to the active controller.
Bridge Release
The overall plan for compatibility is to create a "bridge release" of Kafka where the ZooKeeper dependency is well-isolated.
While this release will not remove ZooKeeper, it will eliminate most of the touch points where the rest of the system communicates with it. As much as possible, we will perform all access to ZooKeeper in the controller, rather than in other brokers, clients, or tools. Therefore, although ZooKeeper will still be required for the bridge release, it will be a well-isolated dependency.
We will be able to upgrade from any version of Kafka to this bridge release, and from the bridge release to a post-ZK release. When upgrading from an earlier release to a post-ZK release, the upgrade must be done in two steps: first, you must upgrade to the bridge release, and then you must upgrade to the post-ZK release.
Rolling Upgrade
The rolling upgrade from the bridge release will take several steps.
Upgrade to the Bridge Release
The cluster must be upgraded to the bridge release, if it isn't already.
Start the Controller Quorum Nodes
We will configure the controller quorum nodes with the address of the ZooKeeper quorum. Once the controller quorum is established, the active controller will enter its node information into /brokers/ids and overwrite the /controller node with its ID. This will prevent any of the un-upgraded broker nodes from becoming the controller at any future point during the rolling upgrade.
Once it has taken over the /controller node, the active controller will proceed to load the full state of ZooKeeper. It will write out this information to the quorum's metadata storage. After this point, the metadata quorum will be the metadata store of record, rather than the data in ZooKeeper.
We do not need to worry about the ZooKeeper state getting concurrently modified during this loading process. In the bridge release, neither the tools nor the non-controller brokers will modify ZooKeeper.
The new active controller will monitor ZooKeeper for legacy broker node registrations. It will know how to send the legacy "push" metadata requests to those nodes, during the transition period.
Roll the Broker Nodes
We will roll the broker nodes as usual. The new broker nodes will not contact ZooKeeper. If the configuration for the zookeeper server addresses is left in the configuration, it will be ignored.
Roll the Controller Quorum
Once the last broker node has been rolled, there will be no more need for ZooKeeper. We will remove it from the configuration of the controller quorum nodes, and then roll the controller quorum to fully remove it.
Rejected Alternatives
Pluggable Consensus
Rather than managing metadata ourselves, we could make the metadata storage layer pluggable so that it could work with systems other than ZooKeeper. For example, we could make it possible to store metadata in etcd, Consul, or similar systems.
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Supporting multiple metadata storage options would inevitably decrease the amount of testing we could give to each configuration. Our system tests would have to either run with every possible configuration storage mechanism, which would greatly increase the resources needed, or choose to leave some user under-tested. Increasing the size of test matrix in this fashion would really hurt the project.Additionally, if we supported multiple metadata storage options, we would have to use "least common denominator" APIs. In other words, we could not use any API unless all possible metadata storage options supported it. In practice, this would make it difficult to optimize the system.-tested. Increasing the size of test matrix in this fashion would really hurt the project.
Additionally, if we supported multiple metadata storage options, we would have to use "least common denominator" APIs. In other words, we could not use any API unless all possible metadata storage options supported it. In practice, this would make it difficult to optimize the system.
Follow-on Work
This KIP expresses a vision of how we would like to evolve Kafka in the future. We will create follow-on KIPs to hash out the concrete details of each change.
- KIP-455: Create an Administrative API for Replica Reassignment
- KIP-497: Add inter-broker API to alter ISR
- KIP-543: Expand ConfigCommand's non-ZK functionality
- KIP-555: Deprecate Direct Zookeeper access in Kafka Administrative Tools
- KIP-589 Add API to update Replica state in Controller
- KIP-590: Redirect Zookeeper Mutation Protocols to The Controller
- KIP-595: A Raft Protocol for the Metadata Quorum
- KIP-631: The Quorum-based Kafka Controller
References
The Raft consensus algorithm
- Ongaro, D., Ousterhout, J. In Search of an Understandable Consensus Algorithm
Handling Metadata via Write-Ahead Logging
- Shvachko, K., Kuang, H., Radia, S. Chansler, R. The Hadoop Distributed Filesystem
- Balakrishnan, M., Malkhi, D., Wobber, T. Tango: Distributed Data Structures over a Shared Log