On Thu, Oct 31, 2019 at 6:11 AM Matthias J. Sax <matth...@confluent.io> wrote:
> Quite a project to test transactions... > > The current system test suite is part of the code base: > https://github.com/apache/kafka/tree/trunk/tests/kafkatest/tests > > There is course also some unit/integration test for transactions. > > There is also a blog post that describes in a high level what testing > was done when EOS was introduced: > > https://www.confluent.io/blog/exactly-once-semantics-are-possible-heres-how-apache-kafka-does-it/ > > And yes, transactions are built on some assumptions and if you configure > your system incorrectly of violate those assumptions, it may break. We > also fixed some bugs since the first release. And there might be more > bugs --- software is always buggy. However, for practical consideration, > transactions should work. > > We would of course love if you could share your test results! If you > discover a bug, please report it, so we can fix it. > > > -Matthias > > On 10/28/19 10:06 AM, Edward Capriolo wrote: > > On Sunday, October 27, 2019, Boyang Chen <reluctanthero...@gmail.com> > wrote: > > > >> Hey Edward, > >> > >> just to summarize and make sure I understood your question, you want to > >> implement some Chaos testing to validate Kafka EOS model, but not sure > how > >> to start or curious about whether there are already works in the > community > >> doing that? > >> > >> For the correctness of Kafka EOS, we have tons of unit tests and system > >> tests to prove its functionality. They could be found inside the repo. > You > >> could check them out and see if we still have gaps (which I believe we > >> definitely have). > >> > >> Boyang > >> > >> On Fri, Oct 25, 2019 at 7:25 PM Edward Capriolo <edlinuxg...@gmail.com> > >> wrote: > >> > >>> Hello all, > >>> > >>> I used to work in adtech. Adtech was great. CPM for ads is 1-$5 per > >>> thousand ad impression. If numbers are 5% off you can blame javascript > >>> click trackers. > >>> > >>> Now, I work in a non addtech industry and they are really, really > serious > >>> about exactly once. > >>> > >>> So there is this blog: > >>> > >>> https://www.confluent.io/blog/transactions-apache-kafka/ > >>> > >>> Great little snippet of code. I think I can copy it and implement it > >>> correctly. > >>> You know, but if you read that section about the zombie fencing, you > >> learn > >>> that you either need to manually assign partitions or use the rebalance > >>> listener and have N producers. Hunting around github is not super > >> helpful, > >>> some code snippets are less complete then even the snipped in the blog. > >>> > >>> I looked at what spring-kafka does. It does get the zombie fencing > >> correct > >>> with respect to fencing id, other bits other bits of the code seem > >>> plausible. > >>> > >>> Notice I said "plausible", because I do not count a few end to end > tests > >>> running single VM as a solid enough evidence that this works in the > face > >> of > >>> failures. > >>> > >>> I have been contemplating how one stress tests this exactly once > concept, > >>> with something Jepsen like or something brute force that I can run for > 5 > >>> hours in a row > >>> > >>> If I faithfully implemented the code in the transactional read-write > loop > >>> and I feed it into my jepsen like black box tester it should: > >>> > >>> Create a topic with 10 partitions, Start launching read-write > transaction > >>> code, start feeding input data, Maybe strings like 1 -1000, now start > >>> randomly killing vms with kill -9 kill graceful exits, maybe even > killing > >>> kafka, and make sure 1-1000, pop out on the other end. > >>> > >>> I thought of some other "crazy" ideas. One such idea: > >>> > >>> If I make a transactional "echo", read x; write x back to the same > topic. > >>> RunN instances of that and kill them randomly. If I am loosing messages > >>> (and not duplicating messages) then the topic would eventually have no > >>> data.. > >>> > >>> Or should I make a program with some math formula like receive x write > >> xx. > >>> If duplication is happening I would start seeing multiple xx's > >>> > >>> Or send 1,000,000,000 messages through and consumer logs them to a > file. > >>> Then use an etl tool to validate messages come out on the other side. > >>> > >>> Or should I use a nosql with increments and count up and ensure no key > >> has > >>> been incremented twice. > >>> > >>> note: I realize I can just use kafka streams or storm, which has its > own > >>> systems to guarantee "at most once" but Iooking for a way to prove what > >> can > >>> be done with pure kafka. (and not just prove it adtech work (good > enough > >> 5% > >>> here or there) ) > >>> > >>> I imagine someone somewhere must be doing this. How where tips? Is it > >> part > >>> of some kafka release stress test? I'm down to write it if it does not > >>> exist. > >>> > >>> Thanks, > >>> Edward, > >>> > >>> Thanks, > >>> Edward > >>> > >> > > > > Boyang, > > > > I just to summarize and make sure I understood your question, you want to > > implement some Chaos testing to validate Kafka EOS model, but not sure > how > > to start or curious about whether there are already works in the > community > > doing that? > > > > Yes. > > > > I am not an expert in this field, but I know what distributed systems can > > mask failures. For example if you have atomic increment you might unit > test > > it and it works fine, but if you ran it for 40 days it might double > count 1 > > time. > > > > of Kafka EOS, we have tons of unit tests and system > > tests to prove its functionality. They could be found inside the repo. > > > > I've been a developer for a while so the phrase "there are tests" never > > tells me everything. Tests reveal the presence of bugs not the absence. > > > > Can you please point me at the tests? My curiosity is if there is a > > systematic in-depth strategy here and how much rigor there is. > > > > In my environment I need to quantify and use rigor to prove out these > > things. Things that you might take for granted. For example, I have to > > prove that zookeeper works as expected when we lose a datacenter. Most > > people 'in the know' take it for granted that kafka and zk do what is > > advertised when configured properly. I have to test that out and document > > my findings. > > > > For kafka transactions. The user space code needs to be written properly > > and configured properly along with the server being setup properly. It is > > not enough for me to check out kafka run 'sbt test' and declare victory > > after the unit tests pass. > > > > What I am effectively looking for is the anti jepsen blog that says...We > > threw the kitchen sink at this and these transactions are bullet proof. > > Here is our methodology, here is some charts, here is xyz. Here is how we > > run it every minor release > > > > I'm not trying to be a pita, educate me on how bullet proof this is and > how > > I can reproduce the results. > > > > > > > > > > > > > > > > All, After a few weeks of hacking at this I have found a few interesting things. First things first, introducing Kafos https://github.com/edwardcapriolo/kafos Kafos (think Kafka + Chaos) . Kafos is a project that attempts to prove out Kafka transactions do exacty-once by launching multiple servers and consume/producers, killing them off and counting how many messages land on the other end. Note: The project is a little raw at this point, (it has hard code to my home folders and some long Thread.sleep() calls in there. I will clean it up as I go,) but I have some early findings. >From the blog here, I want to point out the following flaws: https://www.confluent.io/blog/transactions-apache-kafka/ KafkaProducer producer = createKafkaProducer( “bootstrap.servers”, “localhost:9092”, “transactional.id”, “my-transactional-id”); 1) a fixed transaction.id effectively means the first worker fences all the other ones.While being exactly-once, there is exactly one worker and no parallelism. Spring has two schemes that seem to implement random information in the fencing ids, which I think defeats the purpose. But I need to dive in on that more. https://docs.spring.io/spring-kafka/reference/html/ Transactions are enabled by providing the DefaultKafkaProducerFactory with a transactionIdPrefix. In that case, instead of managing a single shared Producer, the factory maintains a cache of transactional producers. When the user calls close() on a producer, it is returned to the cache for reuse instead of actually being closed. The transactional.id property of each producer is transactionIdPrefix + n, where n starts with 0 and is incremented for each new producer, unless the transaction is started by a listener container with a record-based listener. In that case, the transactional.id is <transactionIdPrefix>.<group.id>.<topic>.<partition>. This is to properly support fencing zombies, as described here <https://www.confluent.io/blog/transactions-apache-kafka/>. This new behavior was added in versions 1.3.7, 2.0.6, 2.1.10, and 2.2.0. If you wish to revert to the previous behavior, you can set the producerPerConsumerPartition property on the DefaultKafkaProducerFactory to false. While transactions are supported with batch listeners, by default, zombie fencing is not supported because a batch may contain records from multiple topics or partitions. However, starting with version 2.3.2, zombie fencing is supported if you set the container property subBatchPerPartition to true. In that case, the batch listener is invoked once per partition received from the last poll, as if each poll only returned records for a single partition. How to fix this? Here be the dragons: "Practically, one would either have to store the mapping between input partitions and transactional.ids in an external store, or have some static encoding of it. Kafka Streams opts for the latter approach to solve this problem." Well then practically, the rest of this code is not useful. while (true) { ConsumerRecords records = consumer.poll(Long.MAX_VALUE); producer.beginTransaction(); for (ConsumerRecord record : records) producer.send(producerRecord(“outputTopic”, record)); producer.sendOffsetsToTransaction(currentOffsets(consumer), group); producer.commitTransaction(); } When you call consumer.poll() (unless you manually assign to a single partiion) you are going to receive records from one or more partitions. As consumers join and leave the the group workers are going to move. There are two ways I can find to do this: 1) use a consumerRebalanceListener and a concurrent map of producers https://github.com/edwardcapriolo/kafos/blob/68782a5420840377dcb3ef94fb65132028804340/pure8/src/main/java/pure8/multipartitionrpw/MultiPartitionRPWBuffered.java#L123 2) as you are polling create/reuse producers as needed. With scalable correctly fenced producers the loop looks more like this: ConsumerRecords<K,V> records = null; try { records = consumer.poll(2000); } catch (KafkaException e){ return; } for(ConsumerRecord<K,V> record: records) { int partition = record.partition(); BufferedTrackingProducer<K,V> producer = producers.get(partition); List<ProducerRecord<K, V>> results = processor.process(record); producer.send(results); } for (Entry<Integer, BufferedTrackingProducer<K, V>> entry: producers.entrySet()) { entry.getValue().commitAndClear(records, groupId); } Implementation here: https://github.com/edwardcapriolo/kafos/blob/68782a5420840377dcb3ef94fb65132028804340/pure8/src/main/java/pure8/multipartitionrpw/MultiPartitionRPWBuffered.java#L1 BTW maybe I am wrong about this and there is a better approach. With the multiple producer threads for single consumer.poll figuring out exactly how to layer the try/catch and exception handling becomes a bit harder than the example in the blog. Anyway, here is what a Kafos chaos "test" looks like. https://github.com/edwardcapriolo/kafos/blob/68782a5420840377dcb3ef94fb65132028804340/pure8/src/main/java/pure8/multipartitionrpw/MutliRPWWithConsumerRestartingTest.java#L1 It is a bit silly that we are forking off processes like this, but I did not want processes running in the same VM.I am not ready to run testcontainers/docker at this point. But hey we launch kafka, we launch zk, we start workers, we kill them off we count things. Results look like this. Verification stats ------------------------------------------------------------------ notFound: 0 foundCorrect: 4000 foundDuplications: 0 ------------------------------------------------------------------ not found list[] serverid: 0 out line Stream closed I am going to do more work killing off servers and making more test scenarios, but so far off to a good start. When you get all the code right, (by cobbling together all the sources out there with half the code) you get things that seem to do exactly once, Help wanted in acedemic review of my assertions and code or PRS Thanks Edward
