Having worked with Kafka for more than two years now, there are two configs whose interaction I've seen be ubiquitously confused. Those two configs are and — and how they interplay with each other. acks min.insync.replicas This piece aims to be a handy reference which clears the confusion through the help of some illustrations. Replication To best understand these configs, it’s useful to remind ourselves of Kafka’s replication protocol. I’m assuming you’re already familiar with Kafka — if you aren’t, feel free to check out my “ ” article. Thorough Introduction to Apache Kafka For each partition, there exists one leader broker and follower brokers. The config which controls how many such brokers exist is . That’s the total amount of times the data inside a single partition is replicated across the cluster. n (1 + N) replication.factor The default and typical recommendation is three. Producer clients only write to the leader broker — the followers asynchronously replicate the data. Now, because of the messy world of distributed systems, we need a way to tell whether these followers are managing to keep up with the leader — do they have the latest data written to the leader? In-sync replicas An (ISR) is a broker that has the latest data for a given partition. in-sync replica A is always an in-sync replica. A is an in-sync replica only if it has fully caught up to the partition it’s following. In other words, it can’t be behind on the latest records for a given partition. leader follower If a follower broker falls behind the latest data for a partition, we no longer count it as an in-sync replica. Broker 3 is behind (out of sync) Note that the way we determine whether a replica is in-sync or not is a bit more nuanced — it’s not as simple as “Does the broker have the latest record?” Discussing that is outside the scope of this article. For now, trust me that red brokers with snails on them are out of sync. Acknowledgements The setting is a client (producer) configuration. It denotes the number of brokers that must receive the record before we consider the write as successful. acks It support three values — , , and . 0 1 all ‘acks=0’ With a value of , the producer won’t even wait for a response from the broker. It immediately considers the write successful the moment the record is sent out. 0 The producer doesn’t even wait for a response. The message is acknowledged! ‘acks=1’ With a setting of , the producer will consider the write successful when the leader receives the record. The leader broker will know to immediately respond the moment it receives the record and not wait any longer. 1 The producer waits for a response. Once it receives it, the message is acknowledged. The broker immediately responds once it receives the record. The followers asynchronously replicate the new record. ‘acks=all’ When set to , the producer will consider the write successful when all of the in-sync replicas receive the record. This is achieved by the leader broker being smart as to when it responds to the request — it’ll send back a response once all the in-sync replicas receive the record themselves. all Not so fast! Broker 3 still hasn’t received the record. Like I said, the leader broker knows when to respond to a producer that uses . acks=all Ah, there we go! Acks’s utility As you can tell, the setting is a good way to configure your preferred trade-off between durability guarantees and performance. acks If you’d like to be sure your records are nice and safe — configure your acks to . all If you value latency and throughput over sleeping well at night, set a low threshold of . You may have a greater chance of losing messages, but you inherently have better latency and throughput. 0 Minimum In-Sync Replica There’s one thing missing with the configuration in isolation. If the leader responds when all the in-sync replicas have received the write, what happens when the leader is the only in-sync replica? Wouldn’t that be equivalent to setting ? acks=all acks=1 This is where comes to shine! min.insync.replicas is a config on the broker that denotes the minimum number of in-sync replicas required to exist for a broker to allow requests. min.insync.replicas acks=all That is, all requests with won’t be processed and receive an error response if the number of in-sync replicas is below the configured minimum amount. It acts as a sort of gatekeeper to ensure scenarios like the one described above can’t happen. acks=all Broker 3 is out of sync here. As shown, allows requests to continue to work when at least replicas of the partition are in sync. Here, we saw an example with two replicas. min.insync.replicas=X acks=all x But if we go below that value of in-sync replicas, the producer will start receiving exceptions. Brokers 2 and 3 are out of sync here. As you can see, producers with can’t write to the partition successfully during such a situation. Note, however, that producers with or continue to work just fine. acks=all acks=0 acks=1 Caveat A common misconception is that denotes how many replicas need to receive the record in order for the leader to respond to the producer. That’s not true — the config is the number of in-sync replicas required to exist in order for the request to be processed. min.insync.replicas minimum That is, if there are three in-sync replicas and , the leader will respond only when all three replicas have the record. min.insync.replicas=2 Here broker 3 is an in-sync replica. The leader can’t respond yet because broker 3 hasn’t received the write. Summary And that’s all there is to it! Simple once visualized — isn’t it? To recap, the and settings are what let you configure the preferred durability requirements for writes in your Kafka cluster. acks min.insync.replicas —the write is considered successful the moment the request is sent out. No need to wait for a response. acks=0 — the leader must receive the record and respond before the write is considered successful. acks=1 — all online in sync replicas must receive the write. If there are less than min.insync.replicas online, then the write won’t be processed. acks=all Further Reading Kafka is a complex distributed system, so there’s a lot more to learn about! Here are some resources I can recommend as a follow-up: — A more in-depth blog of mine that goes over how consumers achieve durability, consistency, and availability. Kafka Consumer Data-Access Semantics — Another in-depth post of mine where we dive into how coordination between brokers works. It explains what makes a replica out of sync (the nuance I alluded to earlier). Kafka Controller Internals ” — An amazing post going over Kafka performance — great tips and explanation on how to configure for low latency and high throughput. “99th Percentile Latency at Scale with Apache Kafka Kafka Summit SF 2020 videos — a wealth of information regarding Apache Kafka Confluent blog — Great, extensive, high-quality documentation Kafka documentation Kafka is actively developed — it’s only growing in features and reliability due to its healthy community. To best follow its development, I’d recommend joining the . mailing lists
