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This document highlights our effort to refactor the threading model of the KafkaConsumer, and the purpose of this refactor is to address issues and shortcomings we’ve we've encountered in the past years, such as increasing code complexity, lock and race conditions, and the coupling between the client poll and the rebalance progress.

• Code complexity: Patches and hotfixes in the past years have heavily impacted the readability of the code.   Firstly, it makes bug fixes mcomplexfixes increasingly difficult, and secondly, it makes the code hard to comprehend.   For example, coordinator communication can happen in both the heartbeat thread and the polling thread, which makes it challenging to diagnose the source of the issue when there's a race condition, for example.   Also, many parts of the coordinator code have been refactored to execute asynchronously; therefore, we can take the opportunity to refactor the existing loops and timers. 

• Complex Coordinator Communication: Coordinator communication happens at both threads in the current design, and it has, caused some race conditions such as KAFKA-13563.   We want to take the opportunity to move all coordinator communication to the background thread and adopt a more linear design.

• Asynchronous Background Thread: One of our goals here is to make rebalance process happen asynchronously to the poll.   Firstly, it simplifies the polling thread pd design, as it essentially only needs to handle fetch requests.   Secondly, because rebalance occurs in the background thread, the polling thread won't be blocked or blocking the rebalance process.

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• Deprecate HeartbeatThread

  • Remove the heartbeat thread.
  • Move the heartbeat logic to the background thread.

• Refactor the Coordinator classes

  • Revisit timers and loops
  • Blocking method methods such as commitOffsetSync will be handled by the poling thread by waiting on the future.  Coordinator 's completion. The coordinator should not be blocking.
  • The coordinator will be polled by the background thread loop will poll the coordinator.
  • Rebalance state modification: We will add a few states to ensure the rebalance callbacks are executed in the correct sequence and timemodify the existing rebalance states, specifically for the callback execution.

• Refactor the KafkaConsumer API

  • It will send Events to the background thread if network communication is required.
  • Remove dependency on the fetcher, coordinator, and networkClient.

• Events and communication channels.

  • We will use two channels the facilitate the two-way communication

• Address issues in these Jira tickets

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Background Thread

Specifically, in the context of the new design context, we use this generic term to indicate the active process running in the background.   It handles network communication, rebalancing, and heartbeat.

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We have two channels in this design.   One channel delivers messages from the polling thread to the background thread.   The other channel delivers messages submitted by the background thread to the polling thread.

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• Polling thread: handles all of the API requests and callback executions.

• Background thread: handles network communication such as heartbeat, coordinator requests, and rebalance flow execution.

• Communication channels: which are responsible for:

  • ServerEventQueue: sending events to the background thread

  • ConsumerEventQueue: sending events to the polling thread

We will discuss the strategy for handling event results in the following section. In short, we will use the CompletableFuture API to handle the asynchronous operation between the two threads.    

A note on the subscriptionState: Its reference will be shared by polling and background threads, i.e., we will refactor it using a the current implementation to be thread-safe data structure or provide explicit locking and synchronization barriers. The reason we are making this data structure an exception are:

• The object is frequently accessed by both the polling thread and background thread.
• The object often requires instaneous response; otherwise, the performance will be heavily impacted
• Also, please review the rejected proposals section below. I talked about a few ideas that we've had.

Important Components

Background thread and its lifecycle

The coordinator discovery process piggybacks on the background thread loop; therefore, any disconnect or connection request is handled by the state machine.  Here we We use a state machine to represent the different states of the coordinator .  Here, the background can be in one of the four statesconnection; these are: down, initialized, coordinator_discovery (coordinator discovery), and stable.  The background thread needs to fulfill certain requirements in each state to maintain its status.  In particular:

• down: The coordinator is uninitialized
• initialized: The background thread completes initialization, and the loop has started.
• coordinator_discovery: The coordinator is requested but hasn't been connected yet. If there's no in-flight FindCoordinatorRequest, then it will send one.   If one exists, check for the request results.
• stable: The coordinator is connected.

The coordinator discovery process piggybacks on the background thread loop; therefore, any disconnect or connection request is handled by the state machine, and the state machine is run by the background thread loop.  If we don't need a coordinator, the background thread will stay in the initialized state.  If an event requires a coordinator, we will then move to the coordinator_discovery state and wait for the response to come back.

Background thread and its lifecycle

1. The background thread has not been constructed, so it is downin the down state.

2. The polling thread starts the background thread; the background thread finishes initialization and then moves to the initialized state.   Usually, this happens after new KafkaConsumer().

3. The background thread loop is running.  Here are the things that are happening:performs the following things:

   1. Check if there is an in-flight event. If not, poll Poll for the new events (for example, a commit event) from the channel.

   2. Check Run the background thread state by running the state machine.  Ensure state requirements are fulfilled.state machine, and here are the following scenario:

      • The event does not require a coordinator.  Execute the event and move on.
      • The If the event requires a coordinator, but it is currently disconnected.  Moves the background thread to the Move the state to coordinator_discovery (coordinator discovery) state.

        • If not, stays initialized and execute the event.

        .
      • The background thread is currently in coorinator_discovery state; continue to loop and wait for the FindCoordinator response.
      • The background thread is in

        Check the coordinator connection. 

      • If the FindCoordinator request hasn’t been completed, stay in the discovery state. 

      • If the request fails, transition to the initialized state. 

      Otherwise, the coordinator is found. Transition to the stable state.
      
      • Poll
   ConsumerCoordinator

4. Poll networkClient

5. Loop back to 3 if not closed.

6. If close() is received:

   1. set close to true to that the loop and exit

   2. poll coordinator and network client

   3. Commit

Consumer Poll Changes

Currently, the consumer.poll() does two things: poll coordinator for assignment update, auto-commit and rebalances, and fetching.

Moving forward, the consumer will only be responsible for sending and returning the fetch data and polling the channel for events such as rebalancing and error:

1. Poll the ConsumerQueue for events

   1. Handle exceptions

   2. Handle callback execution

2. Send out fetch requests

3. Returning fetch results

ConsumerCoordinator and AbstractCoordinator

We will use the existing implementation but with a few changes

• Refactor the HeartbeatThread out of the existing implementation.  Therefore, we won’t be starting the heartbeat thread but having the send heartbeat mechanism piggyback onto the background thread.

• Right now, the coordinator states Unjoin, Prepare_Rebalancing, Complete_Rebalancing, and Stable.  Because of the complexity of the rebalance callback execution in the new implementation, we will:

  • Add new states for partition assignment and revocation:

    • Add the “REVOKE_PARTITION” state to handle onPartitionRevoke

    • Add “ASSIGN_PARTITION” state to take onPartitionAssigned

• These “loop until done or timeout” mechanisms will piggyback on the background thread loop and take advantage of the state machine to advance the progress:

  • Background thread will automatically re-discover the coordinator if disconnected, using the state machine

  • We are already doing networkclinet.poll() in the background thread loop

Rebalance States

The existing coordinator implementation already has a state machine in place.  We will add four states to facilitate the callback execution.

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After onJoinPrepare, transition to the REVOKING_PARTITIONS state.  It will do a few things:

1. Send an event to the polling thread to execute the event

2. Wait for the partition revoked event, and advance the state to PARTITIONS_REVOKED

In the onJoinComplete:

1. 1. • • the Coordinator.
        • Execute the event.

2. Poll the networkClient.

3. Backoff for retryBackoffMs milliseconds.

Rebalance States

We modify the existing rebalance states to aid the callback execution.

1. We split the PREPARING_REBALANCE into PREPARE_REVOCATION, and PARTITION_REVOKED, PREPARING_REBALANCE

   1. PREPARE_REVOCATION: Pause partitions that will be reovked

   2. REVOKING_PARTITION: Send a PARTITION_REVOKE event to the polling thread.

   3. PARTITION_REVOKED: Update the subscription.  Autocommit.

2. STABLE will become ASSIGNING_PARTITIONS and STABLE 

   1. ASSIGNING_PARTITIONS: Send an ASSIGN_PARTITIONS event

   2. STABLE: Upon receiving PARTITIONS_ASSIGNED event, transition to STABLE

After the proposed state transition is as such:

Starting from UNJOINED: UNJOINED → PREPARING_REBALANCE → COMPLETING_REBALANCE → ASSIGNING_PARTITIONS → STABLE

(EAGER) Starting from STABLE: STABLE → PREPARE_REVOCATION → REVOKING_PARTITION → PARTITION_REVOKED → PREPARING_REBALANCE → COMPLETING_REBALANCE → ASSIGNING_PARTITIONS → STABLE

(COOPERATIVE) STABLE → PREPARE_REVOCATION → REVOKING_PARTITION → PARTITION_REVOKED → COMPLETING_REBALANCE → ASSIGNING_PARTITIONS → STABLE → //rejoin

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Enter ASSIGNING_PARTITIONS, and send a partition assignment event.  Return.

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Channel and Events

We use a blocking queue to send API events from the polling thread to the background thread. 

ServerEventQueue

// Channel used to send events to the background thread

private BlockingQueue<ServerEvent> queue;

abstract public class ServerEvent {
   private final ServerEventType eventType;
}

enum ServerEventType {
   FETCH,
   COMMIT,
   METADATA_UPDATE,
   CLOSE,
   ...
}

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// Channel used to send events to the polling thread for client side execution/notification

private BlockingQueue<ConsumerEvent> queue;

abstract public class ConsumerEvent {
   private final ConsumerEventType eventType;
}

enum ConsumerEventType {
   ERROR,
   REVOKE_PARTITIONPARTITIONS,
   ...
}

Wakeup() and WakeupException

TBD.  I’m not sure how WakeupException plays out in the new design.

ASSIGN_PARTITIONS,
}

Consumer.poll() 

consumer.poll() onward will be responsible for a the following tasks:

1. Poll ConsumerEventQueue, execute the events
2. Build FetchEvent, and send it to the background thread
3. Return fetched data

Major Changes

Consumer Poll Changes

Currently, the consumer.poll() does two things: poll coordinator for assignment update, auto-commit and rebalances, and fetching.

Moving forward, the consumer will only be responsible for sending and returning the fetch data and polling the channel for events such as rebalancing and error:

1. Check wakeup flag.  Throw WakeupException and interrupt the loop if a wakeup call was invoked.

2. Poll the ConsumerQueue for events

   1. Handle exceptions

   2. Handle callback execution

3. Send out fetch requests

4. Returning fetch results

ConsumerCoordinator and AbstractCoordinator

• New states will be introduced (see Rebalance States section above).  The main purpose is to make the background thread drive the poll, and letting the polling thread to invoke the callbacks.

• Remove HeartbeatThread. Therefore, we won't be starting the heartbeat thread.

  • It will still require a fetch event to poll heartbeat.  As only polling thread issues fetch events, and we want to respect the existing implementation.
• Timeouts and timers will be reevaluated and possibly removed.
• while loops will be reevaluated and possibly thrown out.  In the new implementation the coordinator will be non-blocking, and its states are managed by the background thread loop.

Wakeup() and WakeupException

We will have to reimplement wakeup() and WakeupException.

wakeup()

In the new design, wakeup will interrupt the blocking polling loop in the polling thread.

WakeupException

The exception will be thrown by the polling thread when the loop is interrupted.

Timeout Policy

Consumer.poll() - user provide timeout

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Coordinator discovery timeout: Currently uses the user-provided timeout in the consumer.poll().   Maybe we should use request.timeout.ms.   And re-attempt in the next loop if failed

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Is there a better way to configure session interval and heartbeat interval?

Event Data Models

Test Plan

We will first write and use the existing unit tests and integration tests.

We need to ensure the timing of the critical events are happening in the correct sequence.   For example: We need to first discover the coordinator, second, commit the offset while pausing the partition for being fetched, revoke the partition, and then continue onto rest of the rebalance process.

We will also ensure the heartbeat interval, and poll interval are respected.

We also need to ensure there’s there's no event lost, and the event should happen in the correct sequence.

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The new consumer should be backward compatible.

Alternative

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Proposals

Fetcher

• Should we consider a larger or configurable prefetch buffer?  Ideally, we can tune this param to improve the performance.

SubscriptionState

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• some of the fetcher configuration be dynamic

• Configurable prefetch buffer

SubscriptionState

• Non-shared: We can make a copy of the subscriptionState in the background thread, and 1.  update the state, and 2.  check for consistency during the poll

Others

• use event to drive the synchronization.

  • There could be out of sync issues, which can subsequently causes in correct fetching, etc..

API Changes

• Poll returns CompletableFuture<ConsumerRecord<K,V>>Should poll(), internally, return a lazy list/stream, ConsumerRecordStream, so that the API would just execute stream.take().  Each take() performs similar action as poll().

User Adoption

The refactor should have (almost) no regression or breaking changes upon merge.   So user should be able to continue using the new client.

Release Plan

• Support code will live a long side with exist in parallel from the current code.  The support code are:
  • Background thread
  • A new coordinator implementation, AsyncConsumerCoordinator, for example.
  • Events and event executors
• We will create a new KafkaConsumer class first, then have it override the existing one once reach stability