Support request pipelining in AsyncProducer#2094
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- introduce Broker.AsyncProduce with a callback to have a mostly non blocking way to produce to a single broker - refactor Broker.send and Broker.responseReceiver to support callback - factorize throttleTime metrics into Broker.updateThrottleMetric - add Config.Produce.Pipeline parameter (defaults to false for backward compatibility) - honor MaxOpenRequests when using AsyncProducer and conf.Produce.Pipeline is enabled - add unit and functional tests
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@slaunay thank you for such a fantastic write-up and explanation I had forgotten this PR when I went ahead and reviewed+merged #1686 which was an old PR that was seemingly trying to solve the same problem. Please could you rebase your PR atop the latest main. Feel free to resolve the conflicts by replacing the changes of #1686 with your own. Then I will go ahead and review and merge. |
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@slaunay oh, one other thing I was going to suggest is that we just go ahead and make pipelining the default behaviour. I think it's correct (as you mention) that we should match the Java client and as long as we flag it in the release notes and point out that MaxOpenRequests 1 can be used to restore the old behaviour we should be fine. |
I saw that PR but it looked like it was stalled possibly because it tends to leak the I used commit fba46c9 (includes #1686) to build
That makes sense to me too as long as users read the Changelog before upgrading to a new release 😉. That being said using
I think it is a separate issue that has always been there because the synchronization (using a buffered channel as a semaphore) happens after writing the request to the socket.
Just to be clear, after resolving conflicts do you want me to?
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@slaunay yep exactly right. Thanks again for the great contribution |
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I updated Here are up to date performance results using
The results are consistent with the initial changes. I made sure most of the changes have close to full coverage but I have been running into flaky test from On top of using more than one in flight request now (in case one fails but others succeed), there is no guarantee that the Using a single synchronous call to |
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Note sure I saw this mentioned anywhere, but seeing 16 requests in flight above: note that with idempotency, max requests in flight should not be more than 5. It's not well documented, but Kafka internally uses only a 5-wide idempotent deduplication window per partition. |
The In that particular scenario we write to 128 partitions lead by 5 brokers using the default On the other scenarios, to show the impact of the changes we are relying on a single partition (
FWIU to use the producer idempotency feature you either need in Java land to set at least:
It actually looks like Sarama currently forces you to use 1.:
It is not well documented indeed and quickly looking at the Kafka source code, it seems to me that the restriction is done on the client vs the broker:
I did test more than 5 in flights requests (against one broker) using idempotency and a custom version of But I'm more concerned about setting I believe the The way forward is probably to:
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…2182) * producer: ensure that the management message (fin) is never "leaked" Since async producer now support multiple inflight messages thanks to #1686 and #2094, it now may "leak" the "fin" internal management message to Kafka (and to the client) when broker producer is reconnecting to Kafka broker and retries multiple inflight messages at the same time. * test:async-producer: test broker restart (this fixes #2150) * tests:async-producer: disable logger in TestAsyncProducerBrokerRestart * tests:async-producer: protect leader with a mutex to make race detector happy * test:async-producer: set 5mn default finish timeout * async-producer: do not clear bp.currentRetries when fin message is received just after syn * async-producer: use debug logger when fin message is handled for a healthy brokerProducer * test:async-producer:restart: make emptyValues atomic to avoid races * test:async-producer:restart: rename produceRequestTest to countRecordsWithEmptyValue * test:async-producer:restart: reduce retry backoff timeout to speed up the test * test:async-producer:restart: remove bogus 0
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Enhancement
Although there is a
Config.Net.MaxOpenRequestsparameter, theAsyncProduceruses at most one in-flight produce request when producing to a single Kafka broker.As shown in #1539 a while back, using request pipelining increases throughput when writing to a Kafka cluster over a high latency network link and something that is recommended when implementing the Kafka protocol as described in
their network section:
MaxOpenRequestsdefaults to 5 but if you were to execute the following code:then you would only see a single in-flight
Producerequest.That is, to send the next request you must first wait for the
responseReceiverGo routine to read the current response bytes and return it to calling Go routine.Obviously, if it takes a while to read such response because of network latency or slow replication, the calling Go routine will seat idle instead of sending more records therefore reducing the throughput (more on that in the
Testing donesection).
Unfortunately this is how the
AsyncProducercurrently works, at least how thebrokerProducer"bridge" Go routine processes batches (i.e.*produceSet):https://github.com/Shopify/sarama/blob/635bcf350a7b8a92b4da0aacd288ee09311e673d/async_producer.go#L693-L707
By adding a new
Broker.AyncProducereceiver and using it in theAsyncProducer, we can achieve proper pipelining ofProducerequests.The new receiver relies on a callback to eventually receive the response:
The same could technically be done with
Broker.Produceand N go routines:but it gets tricky to get the back-pressure behavior from the internal
Brokers.responseschannel and avoid creating too many Go routines.With the callback the back-pressure is propagated up and we can still use a channel to handle the responses asynchronously if necessary:
Returning or else passing a channel to
Broker.AsyncProduceis another option but I believe it leads to more complex and less flexible code.As this is a non trivial behavior change in the
AsyncProducer, I introduced a new configuration property to make that feature opt-in.I do believe that
Producerequest pipelining should be the default (like in the Java client) but an application that cares about ordering might see records persisted out of order depending on how it is configured which can be considered a breaking change.Changes
Broker.AsyncProducewith a callback to have a mostly non blocking way to produce to a single brokerBroker.sendandBroker.responseReceiverto support callbackBroker.updateThrottleMetricConfig.Produce.Pipelineparameter (defaults tofalsefor backward compatibility)MaxOpenRequestswhen usingAsyncProducerandconf.Produce.Pipelineis enabled-enable-pipelineflag tokafka-producer-performancefor benchmarkingTesting done
The new unit and functional tests should provide good coverage for the new changes as they touch a critical part of the
Brokerand theAsyncProducerlogic.But for a real end to end and performance test I am using the
kafka-producer-performancetool with a new-enable-pipelineto show the impact of request pipelining when it is the most useful.For that, I am running the tool from an EC2 instance in AWS
us-west-2and producing to:~1msus-east-1with a RTT of~70mseu-central-1with a RTT of~140msHere are some details about the EC2 instance and the command line used as the baseline:
Here are the performance results for each remote target cluster with the respective extra flags:
us-west-2-partitioner manual-partition 0155,417.735.871us-west-2-partitioner manual-partition 0-enable-pipeline163,645.841.706us-east-1-partitioner manual-partition 07010,758.99.741us-east-1-partitioner manual-partition 0-enable-pipeline7064,800.036.126eu-central-1-partitioner manual-partition 01405,494.05.141eu-central-1-partitioner manual-partition 0-enable-pipeline-max-open-requests 114011,371.910.102eu-central-1-partitioner manual-partition 0-enable-pipeline14032,999.425.476eu-central-1roundrobinpartitioneron 5 Brokers/128 partitions
140119,782.459.195eu-central-1roundrobinpartitioneron 5 Brokers/128 partitions
plus
-enable-pipeline140133,124.765.8816Here is the detailed output of
kafka-producer-performancewhen producing toeu-central-1over a single topic partition (-partitioner manual -partition 0).Request pipelining disabled (current behavior) details
Request pipelining enabled (-enable-pipeline -max-open-requests 1) details
Request pipelining enabled (-enable-pipeline [-max-open-requests 5]) details
There is an oddity with using
-max-open-requestsas it is reported as2when set to1and6when set to5(default).I thought it was because the
requests-in-flightmetric is increased before writing to the socket but the effective rate limiting is done later when writing to theBrokers.responseschannel:https://github.com/Shopify/sarama/blob/635bcf350a7b8a92b4da0aacd288ee09311e673d/broker.go#L830-L848
I think there might actually be room for one more in-flight request even if the channel is buffered with a
N-1capacity. That would explain the 2x throughput improvement when using-max-open-requests 1as we would be able topipeline up to 2
Producerequests.Anyway, based on those numbers the throughput performance improvement ranges from
11%to395%.When writing to multiple topic partitions like when using the
roundrobinpartitioner (i.e. writing to more than one broker) the throughput increases because multiplebrokerProducers are active.If you have Kafka producers distributed around the world and a few centralized Kafka clusters like we do, writing to multiple brokers and using request pipeline can greatly improve the throughput.
The same applies if you want to mirror records between two Kafka clusters located in remote data centers.
Having a constant stream of bytes written to a TCP connection is key to increase the TCP window and therefore the throughput especially on high latency network links.
We have been using this specific enhancement in our log shipper successfully for a few years in production.
With other improvements, we are able to use Sarama to push up to 1M records/s per instance (131 Bytes records in average) and produce overall ~6M records/s during peak hours across our 37 data centers.
So thanks a lot for creating and maintaining this Go library and merry Christmas 🎄!