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Status

Current state: Under Discussion

Discussion thread: here 

JIRA: [KAFKA-19883]( KAFKA-19883 - Getting issue details... STATUS )

Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).

1 Motivation

In the current KIP-932 implementation, Share Group acknowledgments are immediate and irrevocable.

If a processing framework (Flink, Spark, etc.) crashes after acknowledging a record but before committing its own state, that record is lost.

• Current State: ACKNOWLEDGED is a terminal state; there is no way to revert to AVAILABLE for redelivery.

• The Risk: Permanent data loss during processing failures.

The Goal: Atomic "Read-Process-Write"

Enable Exactly-Once Semantics (EOS) by making acknowledgments part of a Kafka transaction. Either the output is produced AND the source record is acknowledged, or neither happens.

1.1 Background: Share Groups

Current State Machine:

• AVAILABLE: Ready for delivery.

• ACQUIRED: Locked by a consumer.

• ACKNOWLEDGED / ARCHIVED: Terminal states; records are never redelivered.

• AVAILABLE (Released): Returned to the pool for redelivery.

Current Acknowledgment Modes:

1. Implicit: Automatic ack on the subsequent poll().

2. Explicit: Manual ack via acknowledge(record, type) followed by commitSync()

1.2 Why Existing Consumer-Group Exactly-Once Doesn't Apply

Traditional Consumer Groups rely on replayability, which Share Groups lack:

• Consumer Groups: Frameworks like Flink save offsets in their own state. On failure, they use seek(offset) to replay data. In this model, Kafka offset commits are "cosmetic" (non-critical) because Flink is the source of truth.

• Share Groups: There is no seek() functionality. The broker manages delivery; once a record is acknowledged, it is removed from the delivery pipeline.

• The Conflict: Because records cannot be replayed, the acknowledgment itself must be the transactional boundary. It must stay "pending" until the entire processing transaction is confirmed.

1.3 Existing Pattern: sendOffsetsToTransaction()

2. Use Cases

2.1 Consume-Transform-Produce (CTP)

Ensures that output records and source acknowledgments are committed as a single atomic unit within a Kafka-to-Kafka pipeline.

• The Flow: beginTransaction() → send(output) → sendShareAcksToTransaction() → commitTransaction().

• Result: Output is visible and source records are finalized only if both operations succeed.

2.2 Source-Only Frameworks (No Kafka Producer)

For applications writing to external systems (Databases, S3) that require transactional acknowledgments coordinated with their own internal checkpoints.

• Tool: KIP-1302 General Transaction Session

• The Flow: Aggregates acks and commits them via a background transaction once the external sink confirms the write.

2.3 End-to-End Exactly-Once (Flink/Spark/OLAP)

Integrates Share Groups into the two-phase commit (2PC) lifecycle of streaming engines.

• Pre-commit: Sink records are flushed and share acks are added to the transaction.

• Snapshot: Transactional metadata is saved to the framework state.

• Commit: On checkpoint completion, the transaction is finalized.

• Recovery: If the framework fails, the transaction is aborted; output is rolled back, and the broker automatically reverts share records to AVAILABLE for redelivery.

3 Public Interfaces

A new method is added to KafkaProducer to mirror traditional offset commits:

• Method: sendShareAcksToTransaction(Map<TopicPartition, ShareAcknowledgements>, ShareGroupMetadata)

• Why a new method? Unlike offsets, share acks target __share_group_state (not __consumer_offsets) and are managed by the ShareCoordinator (not the GroupCoordinator).

KIP-1310: General Transaction Session#4.5Custom2PCCoordinators

// Phase 1: Prepare
kafkaSession.beginTransaction();
producer.send(records);
database.prepareTransaction(dbTxnId); 
kafkaSession.prepareTransaction();    

// Phase 2: Commit (Recovery-friendly)
TransactionSession resumed = TransactionSession.resume(txnId, pid, epoch, configs);
resumed.commitTransaction();          
database.commitTransaction(dbTxnId);

3.5 New Metrics

4 Proposed Changes

4.1 Reuse of Existing 2PC Protocol

This KIP reuses Kafka's existing two-phase commit protocol. No new coordinator type or consensus protocol is introduced.

Transaction lifecycle (identical to existing):

EMPTY → ONGOING → PREPARE_COMMIT → COMPLETE_COMMIT
                → PREPARE_ABORT  → COMPLETE_ABORT

What changes is which partitions are added to the transaction:

The WriteTxnMarkers request dispatches commit/abort markers to all partitions in the transaction set. If only sendShareAcksToTransaction() was called, markers go only to __share_group_state. If both sendOffsetsToTransaction() and sendShareAcksToTransaction() were called in the same transaction, markers go to both. The transaction coordinator does not distinguish between these — it just tracks partition sets.

4.2 Wire Protocol Changes

New request: AddShareAcksToTxnRequest (mirrors AddOffsetsToTxnRequest)

{
  "apiKey": TBD,
  "type": "request",
  "name": "AddShareAcksToTxnRequest",
  "validVersions": "0",
  "fields": [
    { "name": "TransactionalId", "type": "string", "versions": "0+" },
    { "name": "ProducerId", "type": "int64", "versions": "0+" },
    { "name": "ProducerEpoch", "type": "int16", "versions": "0+" },
    { "name": "GroupId", "type": "string", "versions": "0+" },
    { "name": "Topics", "type": "[]AddShareAcksToTxnTopic", "versions": "0+",
      "fields": [
        { "name": "Name", "type": "string", "versions": "0+" },
        { "name": "Partitions", "type": "[]int32", "versions": "0+" }
      ]
    }
  ]
}

Purpose: Tells the TransactionCoordinator to add the __share_group_state partition(s) for the given {groupId, topicPartition} pair(s) to the producer's ongoing transaction. The partition is determined by ShareCoordinator.partitionFor(groupId, topicPartition), where topicPartition refers to the original data topic partition being acknowledged, and the function maps it to the corresponding __share_group_state internal partition that stores the state for that share-group + data-partition combination.

New request: TxnShareAcknowledgeRequest (mirrors TxnOffsetCommitRequest)

{
  "apiKey": TBD,
  "type": "request",
  "name": "TxnShareAcknowledgeRequest",
  "validVersions": "0",
  "fields": [
    { "name": "GroupId", "type": "string", "versions": "0+" },
    { "name": "TransactionalId", "type": "string", "versions": "0+" },
    { "name": "ProducerId", "type": "int64", "versions": "0+" },
    { "name": "ProducerEpoch", "type": "int16", "versions": "0+" },
    { "name": "Topics", "type": "[]TxnShareAcknowledgeTopic", "versions": "0+",
      "fields": [
        { "name": "TopicId", "type": "uuid", "versions": "0+" },
        { "name": "Partitions", "type": "[]TxnShareAcknowledgePartition", "versions": "0+",
          "fields": [
            { "name": "Partition", "type": "int32", "versions": "0+" },
            { "name": "AcknowledgementBatches", "type": "[]AcknowledgementBatch", "versions": "0+",
              "fields": [
                { "name": "FirstOffset", "type": "int64", "versions": "0+" },
                { "name": "LastOffset", "type": "int64", "versions": "0+" },
                { "name": "AcknowledgeType", "type": "int8", "versions": "0+" }
              ]
            }
          ]
        }
      ]
    }
  ]
}

Purpose: Sent to the ShareCoordinator to write acknowledgments as pending (uncommitted) within the transaction. The acks become visible only when the transaction commits.

4.3 ShareCoordinator Changes

The ShareCoordinator must handle transaction completion, mirroring GroupCoordinator.completeTransaction():

// In ShareCoordinatorShard (NEW method):
public CoordinatorResult<Void, CoordinatorRecord> completeTransaction(
    long producerId,
    short producerEpoch,
    TransactionResult result    // COMMIT or ABORT
) {
    if (result == TransactionResult.COMMIT) {
        // Materialize pending transactional acks into share-group state
        return applyPendingAcknowledgements(producerId, producerEpoch);
    } else {
        // Discard pending transactional acks
        return discardPendingAcknowledgements(producerId, producerEpoch);
    }
}

The existing hook ShareCoordinatorShard.replayEndTransactionMarker() already exists for replaying transaction markers during log recovery. This KIP extends it to also handle live transaction completion.

4.4 State Storage

Transactional acks are written to __share_group_state using CoordinatorRuntime.scheduleTransactionalWriteOperation(). This follows the same pattern used by GroupCoordinator for transactional offset commits to __consumer_offsets.

Records in __share_group_state are written with the producer's producerId and producerEpoch, making them part of the transaction. They become readable by other consumers only after the transaction commits and the WriteTxnMarkers COMMIT marker is written.

5 Flow diagram 

CLIENT --> BROKER communication (over the Kafka wire protocol):

  Share Consumer -----> Share Group Coordinator
    ShareFetch, ShareAcknowledge, ShareGroupHeartbeat

  TransactionManager --> Transaction Coordinator
    InitProducerId, AddPartitionsToTxn, AddShareAcksToTxn(NEW), EndTxn

  TransactionManager --> Share Group Coordinator
    TxnShareAcknowledgeRequest (NEW)

  Producer ------------> Data Partition Leaders
    ProduceRequest


BROKER --> BROKER communication (internal, not client-visible):

  Transaction Coordinator --> Data Partition Leaders
    WriteTxnMarkers (COMMIT/ABORT control record)

  Transaction Coordinator --> Group Coordinator (via __consumer_offsets leader)
    WriteTxnMarkers (materializes transactional offset commits)

  Transaction Coordinator --> Share Group Coordinator (via __share_group_state leader) [NEW]
    WriteTxnMarkers (materializes transactional share acks)

Abort case: 

6. Corner Cases

Case 1: Crash BEFORE beginTransaction()

poll() -> records [0..9] ACQUIRED
<CRASH>

What happens:

• No transaction was started, no output produced, no acks sent
• ACQUIRED locks on records 0-9 expire (controlled by share.record.lock.duration.ms)
• Broker re-delivers records 0-9 to another member of the share group
• No data loss, no duplicates

Case 2: Crash AFTER send() but BEFORE commitTransaction()

poll() -> records [0..9] ACQUIRED
beginTransaction()
send(output for 0-9)       ← records written to broker, but transactional
<CRASH>

What happens:

• Output records are in the partition log but invisible (read_committed consumers skip uncommitted)
• No EndTxnRequest was sent
• TC detects transaction timeout (transaction.timeout.ms), auto-aborts
• TC sends WriteTxnMarkers(ABORT) -> output records get ABORT marker, permanently invisible
• Share group: no acks were sent, ACQUIRED locks expire, records 0-9 re-delivered
• No data loss, no duplicates

Case 3: Crash AFTER sendShareAcksToTransaction() but BEFORE commitTransaction()

poll() -> records [0..9] ACQUIRED
beginTransaction()
send(output)
sendShareAcksToTransaction()  ← acks written to SGC as PENDING
<CRASH>

What happens:

• Output records: in log, invisible (uncommitted)
• Share acks: stored in SGC as PENDING (not materialized)
• TC detects timeout, sends WriteTxnMarkers(ABORT) to both data partitions AND __share_group_state
• Output: ABORT marker written, records permanently invisible
• Share acks: SGC discards pending acks, locks expire, records 0-9 re-delivered
• No data loss, no duplicates -- this is the critical case that KIP-1289 solves

Case 4: Crash AFTER EndTxn(COMMIT) sent but BEFORE WriteTxnMarkers completes

poll() -> records [0..9] ACQUIRED
beginTransaction()
send(output)
sendShareAcksToTransaction()
commitTransaction()            ← EndTxn sent, PREPARE_COMMIT logged
<TC crashes or broker restart>

What happens:

• PREPARE_COMMIT is durable in __transaction_state log
• When TC recovers (or new leader elected for __transaction_state partition), it replays the log
• TC sees PREPARE_COMMIT, resumes sending WriteTxnMarkers(COMMIT) to all partitions
• Output records become visible, share acks materialized
• No data loss, no duplicates -- the two-phase commit guarantees completion

Case 5: ProducerFencedException (zombie detection)

Instance A: beginTransaction(), send(), ...
Instance B: initTransactions() with same transactional.id
            ← TC bumps epoch, A is now a zombie
Instance A: commitTransaction()
            ← TC rejects: ProducerFencedException

What happens:

• Instance A's transaction is aborted by the TC (epoch fenced)
• Any pending output and share acks from A are discarded
• Instance B takes over, re-processes the records
• No data loss, no duplicates -- fencing prevents split-brain

Case 6: abortTransaction() called explicitly

poll() -> records [0..9] ACQUIRED
beginTransaction()
send(output for 0-4)
record 5 fails validation
abortTransaction()             ← explicit abort

What happens:

• TC writes PREPARE_ABORT, sends WriteTxnMarkers(ABORT)
• Output records 0-4: ABORT marker, permanently invisible
• Share acks (if any sent): discarded
• ACQUIRED locks expire, all records 0-9 re-delivered
• Application gets fresh batch, can retry
• No data loss

Case 7: Partial produce failure (network error to one partition)

beginTransaction()
send("enriched-orders-0", rec1)   ← success
send("enriched-orders-1", rec2)   ← network error, KafkaException

What happens:

• Application catches KafkaException, calls abortTransaction()
• Same as Case 6: everything rolled back, records re-delivered
• No data loss

The Guarantee Matrix

The fundamental property: at no point can output records be visible while share acks are uncommitted, or vice versa. They are in the same transaction and share the same COMMIT/ABORT fate.

7. Compatibility, Deprecation, and Migration Plan

Impact on Existing Users

• If older broker doesn’t support TxnShareAcknowledgeRequest, fallback to current explicit/implicit ack with warning.
• Clients must fail fast on unsupported brokers
• No breaking changes for existing Share Group users
• `transactional` mode is opt-in via `share.acknowledgement.mode` config
• Existing `implicit` and `explicit` modes continue to work unchanged

Migration Path

1. Phase 1: Add `transactional` acknowledgement mode (backward compatible)
2. Phase 2: Streaming frameworks (Flink, Spark) implement transactional sources
3. Phase 3: Documentation and best practices for exactly-once semantics

Deprecation

- No deprecation of existing modes planned
- `transactional` mode recommended for exactly-once use cases

8. Test Plan

Unit Tests

- Transaction state machine transitions
- Idempotency of all transaction operations
- Timeout handling and auto-abort
- Record state transitions (ACQUIRED → ACKNOWLEDGED on commit, ACQUIRED → AVAILABLE on abort/timeout)

Integration Tests

- End-to-end transaction commit/abort flows
- Multi-consumer transactions within same group
- Coordinator failure and recovery
- Network partition scenarios

System Tests

- Flink checkpoint integration with transactional Share Groups
- Exactly-once delivery verification under failures
- Performance benchmarks comparing transactional vs non-transactional modes
- Stress testing with concurrent transactions

Chaos Tests

- Broker failure during PREPARE state
- Coordinator crash before/after prepare
- Consumer crash during transaction
- Network partitions between coordinator and brokers

Include exactly‑once recovery tests similar to Flink e2e:

• • Kill after sendShareAcksToTransaction but before commit.
  • Kill after commit but before notifyCheckpointComplete.
  • Ensure acks are either replayed or visible, never lost.

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.