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Kafka currently provides at-least-once messaging guarantees. Duplicates can arise due to either producer retries or consumer restarts after failure. One way to provide exactly-once messaging semantics is to implement an idempotent producer. This has been covered at length in the proposal for an Idempotent Producer. An alternative and more general approach is to support transactional messaging. This can enable use-cases such as replicated logging for transactional data services in addition to the classic idempotent producer use-cases.

What is transactional messaging?

Producers can explicitly initiate transactional sessions, send (transactional) messages within those sessions and either commit or abort the transaction. The guarantees that we aim to achieve for transactions are perhaps best understood by enumerating the functional requirements.

  1. Atomicity: A consumer's application should not be exposed to messages from uncommitted transactions.
  2. Durability: The broker cannot lose any committed transactions.
  3. Ordering: A transaction-aware consumer should see transactions in the original transaction-order within each partition.
  4. Interleaving: Each partition should be able to accept messages from both transactional and non-transactional producers
  5. There should be no duplicate messages within transactions.

If interleaving of transactional and non-transactional messages is allowed, then the relative ordering of non-transactional and transactional messages will be based on the relative order of append (for non-transactional messages) and final commit (for the transactional messages).


 

So in the above diagram, partitions p0 and p1 receive messages for transactions X1 and X2, and non-transactional messages as well. The time-line is the time of arrival of the messages to the broker. Since X2 is committed first, each partition will expose messages from X2 before X1. Since the non-transactional messages arrived before the commits for X2 and X2, those messages will be exposed before messages from either transaction.

Furthermore, we have the following implementation requirements:

  1. The implementation should be scalable. E.g., a dedicated log per transaction is unacceptable.
  2. Performance:
    1. The throughput of a transactional producer should be comparable to that of a non-transactional producer.
    2. Acceptable latency. E.g., avoid copying the transactional data as much as possible.
    3. Any implementation should not make the partition unavailable (say, due to locking) for an unreasonable period of time.
  3. Client simplicity: Favor a scheme that lend to a simpler client-side implementation (even if it adds more complexity to the broker). For example, it is acceptable (but not ideal) for a consumer implementation to (internally) buffer and subsequently discard messages from uncommitted transactions. i.e., if the chosen implementation allows the broker to materialize messages from uncommitted transactions in the data logs.

Finally, it is worth adding that any implementation should also provide the ability to associate each transaction's input state with the transaction itself. This is necessary to facilitate retries for transactions - i.e., if a transaction needs to be aborted and retried, then the entire input for that transaction needs to be replayed.

Each transaction is associated with a block of input that is processed and results in the output (transaction). When we commit the transaction we would need to also associate the next block of input with that transaction. In the event of a failure the processor would need to query (the downstream Kafka cluster) to determine the next block that needs to be processed. In our case, this would simply be an input offset vector (IOV) for the input partitions that are being processed for each transaction.

Implementation overview

In this implementation proposal, the producer sends transactional control messages that signal the begin/end/abort state of transactions to a highly-available transaction coordinator which manages transactions using a multi-phase protocol. The producer  sends transaction control records (begin/end/abort) to the transaction coordinator, and sends the payload of the transaction directly to the destination data partitions.  Consumers need to be transaction-aware and buffer each pending transaction until they reach its corresponding end (commit/abort) record.

  • Transaction group
  • Producer(s) in that transaction group
  • Transaction coordinator for that transaction group
  • Leader brokers (of the transaction payload's data partitions)
  • Consumers of transactions

Transaction group

The transaction group is used to map to a specific transaction coordinator (say, based on a hash against the number of journal partitions). The producers in the group would need to be configured to use this group. Since all transactions from these producers go through this coordinator, we can achieve strict ordering across these transactional producers.

Producer IDs and state groups

In this section, I will go over the need to introduce two new parameters for transactional producers: producer ID and producer group. These don't necessarily need to be part of the producer configuration but may be specified as a parameter in the producer's transactional API.

The preceding overview describes the need to associate the input state of a producer (or in general a processor of some input) along with the last committed transaction. This enables the processor to redo a transaction (by recreating the input state for that transaction - which in our use cases is typically a vector of offsets).

We can utilize the consumer offset management feature to maintain this state.  The consumer offset manager associates each key (consumergroup-topic-partition) to the last checkpointed offset and metadata for that partition. In the case of a transactional processor, we would want to save the offsets of its consumer that are associated with  the commit point of the transaction. This offset commit record (in the __consumer_offsets topic) should be written as part of the transaction.  i.e., the __consumer_offsets topic's partition that stores offsets for the consumer group will need to participate in the transaction. So (for example) suppose a producer fails in the middle of a transaction (which the transaction coordinator subsequently expires); when the producer recovers, it can issue an offset fetch request to recover the input offsets associated with the last committed transaction and resume transactional processing from that point.

There are a few enhancements that we need to make to the offset manager and the compacted __consumer_offsets topic in order to support this. First, the compacted topic will now contain transactional control records as well. We will need to come up with an eviction strategy for these control records. Second, the offset manager needs to become transaction-aware - specifically, an offset fetch request should return an error if the group is associated with a pending transaction.

Transaction coordinator

  • The transaction coordinator is the leader of a specific partition of the __transaction_control topic (i.e., the journal log). It is the point of entry for initiating, committing and aborting transactions. It maintains the following (in-memory) state:
  • A high-watermark (of the journal log) corresponding to the BEGIN record of the first in-flight transaction. The transaction coordinator can periodically checkpoint this high-watermark to ZooKeeper.
  • For all in-flight transactions that follow the HW in the journal log:
    • The list of topic-partitions (of the payload) of the transaction.
    • Timeout of the transaction.
    • The producer ID associated with the transaction and its generation.

We need to ensure that whatever retention policy is in place does not delete a log segment if the transactional HW is within that log segment. This could just be an       additional parameter to the retention policy. i.e., segments including and following that offset should not be deleted.

Transaction flow

(Assuming no failures at each step.)

InitPhase

  1. Producer: Determine which broker is the coordinator for its group.- Producer: Send a BeginTransaction(producerId, generation, partitions... ) request to the coordinator and await response. We could provide a variation of this API that   also includes a time-out. If the producer needs to commit its consumer state as part of the transaction it will need to include the relevant partition of the __consumer_offsets topic in the BeginTransaction request.
  2. Broker: Generate a TxId.
  3. Coordinator: Append a BEGIN(TxId, producerId, generation, partitions...) record to the journal log and send response.
  4. Producer: Read back response (which will include the TxId).
  5. Coordinator (and followers): Update in-memory state of in-flight transactions and data partitions of the transaction.

SendPhase

Producer: Send transaction payloads (i.e., records) to the leader brokers of the data partitions. Each record will contain the TxId and TxCtl fields. The TxCtl is really only required to mark the final commit (or abort). The producer request envelope will include the producer ID and generation as well, but these will not be appended to the data logs.

EndPhase (When the producer is ready to commit a transaction,)

  1. Producer: Send an OffsetCommitRequest to commit the input state associated with the end of that transaction (i.e., input for start of next transaction).
  2. Producer: Send a CommitTransaction(TxId, producerId, generation) request to the coordinator and await response. (A non-error response indicates to the producer that the transaction will be committed.)
  3. Coordinator: Append the PREPARE_COMMIT(TxId) request to the journal log and then send a response to the producer.
  4. Coordinator: Send a CommitTransaction(TxId, partitions...) request to every leader broker (for records in the transaction payload).
  5. Leader brokers:
    1. If this is a leader for a partition of a topic other than __consumer_offsets: Upon receiving a CommitTransaction(TxId, partition1, partition2, ...) request it will    append an empty (null) record (i.e., no key/value) to the respective partition and set the TxId and TxCtl (to COMMITTED) fields of the record. The leader broker will then respond to the coordinator.
    2. If this is a leader for a partition of __consumer_offsets: append a record to the partition with key set to G-LAST-COMMIT, value set to TxId. It should also set the   TxId and TxCtl fields of the record. The broker will then respond to the coordinator.  
  7. Coordinator: Append a COMMITTED(TxId) request to the journal log.
  8. Coordinator (and followers): Advance HW if possible (see above for details of the HW).

Aborting a transaction

A transaction may be aborted by a producer due to failures while sending the transaction's payload records. A transaction may also be aborted by the coordinator when it has not been committed within a configurable timeout period.

  • Producer: Send an AbortTransaction(TxId) request to the coordinator and await response. (A non-error response indicates to the producer that the transaction will be aborted.)
  • Coordinator: Append a PREPARE_ABORT(TxId) record to the journal log and then send a response to the producer.
  • Coordinator: Send a AbortTransaction(TxId, partitions...) request to every leader broker (for records in the transaction payload). (The abort-action on the receiving brokers is similar to the above for commits.)

Failure cases (in basic transaction flow)

  • Timeout or error response when producer sends BeginTransaction(TxId): producer simply retries (with same TxId).
  • Broker-side error while the producer is sending the data: The producer should abort (and subsequently redo) the transaction (with a new TxId). If the producer does not  abort the transaction, the coordinator will abort the transaction after the transaction's timeout period. Redoing the transaction is required only in the case of an error for which the request data may have been appended and replicated to the follower. For example, a producer request timeout would require a redo while a NotLeaderForPartitionException does not require a redo.
  • Timeout or error response when producer sends CommitTransaction(TxId): producer simply retries (with same TxId).
  • Producer failure while a transaction is pending: if the coordinator is in a position to detect a closed socket (when it sends the response to the BeginTransactionRequest) then it can proactively abort the transaction.  Otherwise the transaction will be aborted after its timeout period.
  • Coordinator failure: i.e., when the coordinator moves to another broker (i.e., leadership of a journal partition moves). The coordinator will scan the log from the last checkpointed HW. If there are any transactions that were in PREPARE_COMMIT or PREPARE_ABORT, the new coordinator will redo the COMMIT and ABORT. Note that transactions that are in-flight when a coordinator goes down don't necessarily need to be aborted - i.e., the producer can just send its CommitTransactionRequest to the new coordinator.

Do transactions make sense for compacted topics?

Compacted topics discard earlier records with the same key during the compaction process. Is this legal if those records are part of a transaction? It is perhaps a bit weird but may not be too harmful since the rationale for using the compaction policy within a topic is to retain the latest update for keyed data.

Still, some messages from within a transaction can get compacted out while others remain. i.e., it is possible that a consumer (that is lagging behind) at an offset within a compacted segment will only see some messages from a transaction. So if that application was (say) updating some table and the messages in the transaction correspond to different keys then this scenario could result in an inconsistent view of the database. i.e., this is a caveat to keep in mind when using transactions within compacted topics.

Compacting topics with transactional messages

The transactional HW is the offset of the earliest pending transaction. The issue is, do we allow messages from committed transactions after the transactional HW (or non- transactional messages) to participate in a round of compaction. I think the answer is yes. There are two cases to consider:

  • The transactional HW is in the active segment: In this case, there is no issue since only segments that have rolled over participate in the compaction process.
  • The transactional HW is in a dirty portion that has rolled over from the active segment and is thus eligible for compaction: In this case we have two options:
    • Do compaction as usual except that messages that are part of a pending transaction need to be copied over to the "cleaned" log. The cleaner checkpoint should not be moved beyond the transactional HW. i.e., even if compaction can be done past the transactional HW that portion should still be considered "dirty" for the next round.
    • Don't compact past the transactional HW.
    • Both of these approaches require that the transactional HW be maintained on the followers as well. This is not as bad as it sounds since this information can be made  available from the transaction coordinator module. The transactional HW only needs to be approximate. i.e., it could be slightly stale at the expense of achieving slightly less compaction than possible.

 

 

 

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