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  • KIP-91 Provide Intuitive User Timeouts in The Producer

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  • The initial call to send can block up to either waiting on metadata or for available space in the producer's accumulator. After this the record is placed in a (possibly new) batch of records.
  • The batch is eligible to be considered for sending when either or batch.size bytes has been reached, whichever comes first. Although the batch is ready, it does not necessarily mean it can be sent out to the broker.
  • The batch has to wait for a transmission opportunity to the broker. A ready batch can only be sent out if the leader broker is in a sendable state (i.e., if a connection exists, current inflight requests are less than max.inflight.requests, etc.). In KIP-19, we use the configuration to expire requests in the accumulator as well. This was done in order to avoid an additional timeout, especially one that exposes the producer's internals to the user. The clock starts ticking when the batch is ready. However, we added a condition that if the metadata for a partition is known (i.e., it is possible to make progress on the partition) then we do not expire its batches even if they are ready. In other words, it is difficult to precisely determine the duration spent in the accumulator. Note that KIP-19 claims that "The per message timeout is easy to compute - linger ms + (retries + 1) * request". This is false.
  • When the batch gets sent out on the wire, we reset the clock for the actual wire timeout
  • If the request fails for some reason before the timeout and we have retries remaining, we reset the clock again. (i.e., each retry gets a full

The following figure illustrates the above phases. The red circles are the potential points of timeout.


One problem with the implementation of KIP-19 was that it did not check if metadata is stale or not. So for example, if the cluster suddenly becomes unavailable, the producer would never expire batches if it already has metadata available. So in KAFKA-2805 we completely removed the check on availability of metadata and indiscriminately expire batches that are ready and have remained in the accumulator for at least even if the leader broker is available.


  1. Leads to unfair/unnecessary timeouts especially when preceding batches that are inflight encounter retries. (Unfair because those batches are given another to expire.)
  2. Can cause callbacks to fire out of order when strict ordering is required. i.e., when accumulator batches expire, their callbacks fire before callbacks for inflight batches (that are actually preceding batches to the batches in the accumulator). Note that this is really an issue only for max.inflight.requests == 1 since we don't attempt to make any strict ordering guarantees for other inflight settings.
  3. Pessimistically expires batches even though it may be possible to make progress. (KIP-19 takes an optimistic view on the other hand - i.e., do not expire batches if metadata is available since we may be able to make progress.)


One problem with the above incremental change is the way in which we check whether there is any inflight request. Since it only applies to the scenario where max.inflight.requests == 1 we check if the partition is muted or not. (We mute partitions when a batch is inflight for that partition in order to ensure ordering even during leader movements - see KAFKA-3197 for more details on that.) The issue though is that if a metadata request is inflight (say, due to a normal metadata refresh) the partitions on that broker will not be in a muted state (since it is not a batch that is inflight) and can expire if they have been sitting in the accumulator for at least This is an unintuitive side-effect given that they would otherwise have been sent out (had the metadata refresh not occurred).


It is possible to bump up the but that is undesirable as explained in the rejected alternatives section.

Note also that it is insufficient to tweak the above check to see if it is a metadata request that is inflight because the problem is more fundamental than that: we would like to keep the to be relatively small (at most a minute or so). If we continue to reuse for timing out batches in the accumulator it is highly likely for a high volume producer to expire several batches even in routine scenarios such as bouncing the cluster that the producer is sending to. E.g., if a broker is disconnected due to a bounce then metadata will still be available, but there will be no inflight request to that broker and so several batches that have been in the accumulator for more than will get expired. This would be fine if the accumulator timeout is large enough to account for the expected time batches will sit in the accumulator, but this could be high for a high-volume producer. In the absence of an explicit accumulator timeout the only option here is to artificially bump up

Proposed Changes

We propose adding a new timeout The window of enforcement includes batching in the accumulator, retries, and the inflight segments of the batch. With this config, the user has a guaranteed upper bound on when a record will either get sent, fail or expire from the point when send returns. In other words we no longer overload to act as a weak proxy for accumulator timeout and instead introduce an explicit timeout that users can rely on without exposing any internals of the producer such as the accumulator. 


This config enables applications to delegate error handling to Kafka to the maximum possible extent (by setting retries=MAX_INT and deliveryINT and And it enables MirrorMaker to bound the effect of unavailable partitions by setting deliverysetting to ms to be sufficiently low, presumably some function of the expected throughput in the steady state. Specifically, setting to a minimum of requestof ms + +, would allow at least one attempt to send the message when the producer isn't backed up.


  • Add a new producer configuration with default value 120 seconds.
  • Change the default value of retries to of retries to MAX_INT.
  •–no changes in the meaning, but messages are not expired after this time. I.e., is no longer relevant for batch expiry.


This configuration is backwards compatible. Throw ConfigException for timeouts that don't make sense. (E.g., < ms + +


  • Bumping up request timeout does not work well because that is an artificial way of dealing with the lack of an accumulator timeout. Setting it high will increase the time to detect broker failures.
  • In KAFKA-4089 we also considered looking at whether metadata is stale or not to determine whether to expire. This may work to address the problem raised in KAFKA-4089 but is still hard for users to understand without understanding internals of the producer and makes it difficult to put an upper bound on the overall timeout.
  • We cannot repurpose since there are use-cases for non-blocking calls to send.
  • We also discussed the ideal of providing precise per-record timeouts or at least per-batch timeouts. These are very difficult to implement correctly and we believe it is sufficient to provide users with the ability to determine an upper bound on delivery time (and not specify it on a per-record level). Supporting per-record timeouts precisely is problematic because we would then need the ability to extract records from compressed batches which is horribly inefficient. The difficulty is even more pronounced when requests are inflight since batches need to be extracted out of inflight requests. If we are to honor the retry backoff settings then this would mean that we have to split an inflight request with an expiring record or batch into smaller requests which is again horribly inefficient. Given the enormous complexity of implementing such semantics correctly and efficiently, and the limited value to users we have decided against pursuing this path. The addition of an explicit timeout as summarized in this proposal will at least give the users the ability to come up with a tight bound on the maximum delay before a record is actually sent out.
  • Allow to span the inflight phase as well. This won't work because a request would contain batches from multiple partitions. One expiring batch should not cause the other batches to expire, and it is too inefficient to surgically remove the expired batch for the subsequent retry. 

  • An end-to-end timeout Model: A single end-to-end timeout for the entire send operation is a very easy to use and therefore, very compelling alternative. It's, however, not without pitfalls. The model of end-to-end timeout we considered is exclusive to segment-wise timeouts. In other words, you specify either of them but not both.

    1. An end-to-end timeout does not subsume because the latter is a bound on how long the application threads may block. An end-to-end timeout may only subsume time spent in accumulator (including and on the wire.

    2. In applications such as mirror-maker where consumed records are immediately produced on the other side, "catch-up mode" is a frequent scenario that creates additional challenges to end-to-end timeout model. A record may spent 99.95% of it's end-to-end budget in the accumulator and may not leave much budget at all for retrying over the wire. The producer may expire batches without any retries at all. An end-to-end delay of MAX_INT may be sufficient for mirror-makers but the same could not be said about a general application that has a few seconds of end-to-end timeout. It's unclear if a send failure due to timeout is due to an unavailable partition or just acccumulator wait. Exhaustion of nRetries combined with pretty much guarantees that failure's due to service unavailability.

    3. An end-to-end timeout may be partially emulated using the future.get(timeout). The timeout must be greater than ( + nRetries * ( + Note that when future times out, Sender may continue to send the records in the background. To avoid that implementing a cancellable future is a possibility.

  • An additional configuration called "" for producing applications that don't care about end-to-end bound on message delivery (and hence don't want to configure but do care about partitions that never make progress. A notional partition-unavailability-budget is useful for kafla-mirror-maker-like apps. It could be a function of retries, backoff period, and request timeout. Deemed too complicated for the benefit received. Specifically, a new mechanism is needed at the producer side that keeps track of unavailability of partitions over time.  Partition unknown and partition unavailable have different nuances.
  • The original proposal in this KIP from LinkedIn was to add a new timeout called The window of enforcement would be from the time send returns until the produce request is sent on the wire. With this change, the user has a guaranteed upper bound on when a record will either get sent, fail or expire: ms + batch ms + nRetries * ( ms + retry In other words, is no longer overloaded to act as a weak proxy for accumulator timeout. This proposal introduced an explicit timeout that users can rely on without exposing any internals of the producer such as the accumulator. In the following figure, the possible timeout points are colored red. The new global timeout can occur at any point after the batch is ready.

  • Gliffy Diagram
  • The proposal is preferred over for the following reasons.
    • It's clearer for users to ocnfigure one number that encompases batching, await-send, and inflight segments together as opposed to having to configure multiple segments via separate configs.
    • In the approach, clock for a batch starts when a batch is created. Starting the clock at the beginning of a batch avoids the pitfalls of starting the clock at close. As of now, closing of batch may be arbitrarily delayed because a batch is closed only when the
      batch is sendable (i.e., broker is available, inflight request limit is not exceeded, etc). The possibility of unbounded delay in closing a batch is incompatible with the goal of this kip.