This page is meant as a template for writing a KIP. To create a KIP choose Tools->Copy on this page and modify with your content and replace the heading with the next KIP number and a description of your issue. Replace anything in italics with your own description.

Status

Current state: Under Discussion

Discussion thread: here 

JIRA: [KAFKA-19883]()

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

1 Motivation

A record's side effects (output writes) and its source acknowledgment must be committed atomically. Either both succeed, or neither does.

1. Share consumer polls records -> broker sets them to ACQUIRED.
2. Framework processes and acknowledges records (implicit or explicit).
3. Checkpoint fails before sink outputs are committed.
4. Records are permanently ACKNOWLEDGED (not redelivered) -> data loss.
5. Note:
   1. Share records become terminal when acked: RecordState
   2. Ack path today is irreversible once ACKNOWLEDGED: SharePartition.acknowledge()

Goal: Enable exactly-once read semantics via transactional acknowledgements.

We will be following the similar existing pattern we have in Kafka Producer Transactions.

Use Cases

• Consume‑Transform‑Produce (CTP): bind acks to Kafka output transaction.
• Consume‑only frameworks: transactional acks independent of producer.

Frameworks:

- Apache Flink: Exactly-once checkpointing with Share Group sources
- Apache Spark: Structured Streaming with Share Group consumers
- Any coordinator-worker streaming framework requiring atomic acknowledgements

1.1 Background: Share Groups

Share groups (KIP-932) allow multiple consumers to read from the same partition concurrently, with the broker controlling per-record delivery via an acquisition-lock mechanism. Each record passes through a state machine on the broker:

AVAILABLE → ACQUIRED → ACKNOWLEDGED (terminal)
                    → ARCHIVED (terminal, rejected or max delivery exceeded)
                    → AVAILABLE (released for redelivery)

Today, share groups support two acknowledgment modes:

• Implicit: Records from a previous poll() are automatically acknowledged on the next poll().
• Explicit: The application calls acknowledge(record, AcknowledgeType) for each record, then commitSync().

In both modes, acknowledgments are committed immediately and irrevocably. Once a record enters ACKNOWLEDGED state in the SharePartition (managed on the broker), it is never redelivered.

Relevant existing code:

• RecordState enum: kafka/server/src/main/java/org/apache/kafka/server/share/fetch/RecordState.java
• SharePartition.acknowledge(): kafka/core/src/main/java/kafka/server/share/SharePartition.java
• ShareConsumer interface: kafka/clients/src/main/java/org/apache/kafka/clients/consumer/ShareConsumer.java
• AcknowledgeType enum (ACCEPT/RELEASE/REJECT/RENEW): kafka/clients/src/main/java/org/apache/kafka/clients/consumer/AcknowledgeType.java

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

With traditional consumer groups, Flink avoids this problem by replaying from a saved offset:

1. KafkaSourceReader.snapshotState() saves offsets in Flink's state.
2. On failure recovery, consumer.seek(savedOffset) replays from the checkpoint.
3. Kafka offset commits (via sendOffsetsToTransaction()) are cosmetic — Flink state is the source of truth.

Share groups have no seek(). The broker controls which records are delivered. Once acknowledged, records are gone. Therefore, acknowledgment itself must become the transactional boundary, not just a cosmetic side-effect.

1.3 Existing Pattern: sendOffsetsToTransaction()

Kafka already solves the identical problem for consumer-group offsets via KafkaProducer.sendOffsetsToTransaction():

1. The producer includes consumer-group offsets in its ongoing transaction.
2. When the transaction commits, both output records and offset commits become visible atomically.
3. On abort, neither is visible — the consumer re-reads from the old offset.

This KIP applies the same pattern to share-group acknowledgments. Instead of committing to __consumer_offsets, we commit to __share_group_state.

Existing code this KIP mirrors:

• KafkaProducer.sendOffsetsToTransaction(): kafka/clients/src/main/java/org/apache/kafka/clients/producer/KafkaProducer.java
• AddOffsetsToTxnRequest.json: kafka/clients/src/main/resources/common/message/AddOffsetsToTxnRequest.json
• GroupCoordinator.completeTransaction(): handles WriteTxnMarkers for __consumer_offsets
• ShareCoordinatorShard.replayEndTransactionMarker(): already exists, handles transaction markers for __share_group_state

2. Use Cases

2.1 Consume-Transform-Produce (CTP)

An application reads from a share group, transforms records, and produces output to another Kafka topic. Both output and acknowledgments must commit atomically.

producer.beginTransaction();

ConsumerRecords<K,V> records = shareConsumer.poll(Duration.ofMillis(100));
for (ConsumerRecord<K,V> record : records) {
    ProducerRecord<K,V> output = transform(record);
    producer.send(output);
}

// Bind share acks to this transaction (NEW API)
producer.sendShareAcksToTransaction(
    ShareAcknowledgements.fromRecords(records, AcknowledgeType.ACCEPT),
    shareConsumer.groupMetadata()
);

producer.commitTransaction();
// Output records AND share acks commit atomically

2.2 Flink / Spark Source (No Producer)

A streaming framework reads from a share group as a source. There is no Kafka producer in the pipeline — the output may go to a database, filesystem, or another system. The framework needs to commit share acks transactionally, coordinated with its own checkpointing.

TransactionalShareAcknowledger acknowledger = new TransactionalShareAcknowledger(props);
acknowledger.initTransactions();

// On checkpoint complete:
acknowledger.commitAcknowledgements(bufferedAcks, shareGroupId);
// Internally: beginTransaction → sendShareAcksToTransaction → commitTransaction

2.3 Flink End-to-End Exactly-Once (Source + Sink)

When a Flink pipeline reads from a Kafka share group and writes to a Kafka sink topic, we achieve end-to-end exactly-once by binding both sink output and source acknowledgments to the same Kafka transaction:

Checkpoint lifecycle:
  prepareCommit()              → flush sink records, pre-commit Kafka txn
                               → include share acks in the same transaction
  snapshotState()              → save txn metadata + buffered acks
  notifyCheckpointComplete()   → commitTransaction() (acks + output atomically)
  On failure                   → abortTransaction() (acks + output both rolled back)

3 Public Interfaces

3.1 KafkaProducer API Addition

// In org.apache.kafka.clients.producer.KafkaProducer:

/**
 * Sends share-group acknowledgments as part of the current transaction.
 * Mirrors sendOffsetsToTransaction() but writes to __share_group_state
 * instead of __consumer_offsets.
 *
 * @param acknowledgements Map of TopicPartition to list of acknowledgment batches
 * @param groupMetadata    The share group metadata (group ID, member ID, generation)
 * @throws IllegalStateException if no transaction is in progress
 * @throws ProducerFencedException if the producer is fenced
 */
public void sendShareAcksToTransaction(
    Map<TopicPartition, ShareAcknowledgements> acknowledgements,
    ShareGroupMetadata groupMetadata
) throws ProducerFencedException;

This mirrors sendOffsetsToTransaction(). The reason a new method is needed (instead of reusing the existing one) is that:

• Different storage topic: acks go to __share_group_state, not __consumer_offsets.
• Different coordinator: the ShareCoordinator handles ack persistence, not GroupCoordinator.
• Different semantics: acks are per-record state transitions, not per-partition offsets.

3.2 TransactionalShareAcknowledger (Standalone)

For frameworks that do not use a KafkaProducer in the pipeline:

public class TransactionalShareAcknowledger implements Closeable {

    public TransactionalShareAcknowledger(Properties config);

    /** Initialize the internal transactional producer. Call once. */
    public void initTransactions();

    /**
     * Atomically commit share acknowledgments.
     * Internally executes: beginTransaction → sendShareAcksToTransaction → commitTransaction.
     * This is NOT a single RPC. It orchestrates the standard 2PC protocol.
     */
    public void commitAcknowledgements(
        Map<TopicPartition, ShareAcknowledgements> acks,
        String groupId
    );

    /** Abort any in-progress transactional acknowledgment. */
    public void abortAcknowledgements();

    public void close();

 }

Clarification: commitAcknowledgements() is a convenience wrapper. It internally calls three operations in sequence:

1. beginTransaction()
2. sendShareAcksToTransaction(acks, groupMetadata)
3. commitTransaction()

It does NOT introduce a new single-RPC path. It uses the standard 2PC protocol.

3.3 ShareGroupMetadata

public class ShareGroupMetadata {
    private final String groupId;
    private final String memberId;
    private final int generationId;
    // constructor, getters

 }

3.4 ShareAcknowledgements

public class ShareAcknowledgements {
    private final List<ShareAcknowledgementBatch> batches;

    public static ShareAcknowledgements fromRecords(
        ConsumerRecords<?, ?> records, AcknowledgeType type);

    // Each batch: firstOffset, lastOffset, acknowledgeType

 }

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.