Kafka Topic Replication
What is Kafka topic replication?
One of the main reasons for Kafka's popularity, is the resilience it offers in the face of broker failures. Machines fail, and often we cannot predict when that is going to happen or prevent it. Kafka is designed with replication as a core feature to withstand these failures while maintaining uptime and data accuracy.
Data Replication helps prevent data loss by writing the same data to more than one broker
In Kafka, replication means that data is written down not just to one broker, but many.
The replication factor is a topic setting and is specified at topic creation time.
A replication factor of
1means no replication. It is mostly used for development purposes and should be avoided in test and production Kafka clusters
A replication factor of
3is a commonly used replication factor as it provides the right balance between broker loss and replication overhead.
In the cluster below consisting of three brokers, the replication factor is
2. When a message is written down into Partition 0 of Topic-A in Broker 101, it is also written down into Broker 102 because it has Partition 0 as a replica.
Thanks to a replication factor of 2, we can withstand the failure of one broker. This means that if Broker 102 failed, as you see below, Broker 101 & 103 would still have the data.
For a given topic-partition, one Kafka broker is designated by the cluster to be responsible for sending and receiving data to clients. That broker is known as the leader broker of that topic partition. Any other broker that is storing replicated data for that partition is referred to as a replica.
Therefore, each partition has one leader and multiple replicas.
An ISR is a replica that is up to date with the leader broker for a partition. Any replica that is not up to date with the leader is out of sync.
Here we have Broker 101 as Partition 0 leader and Broker 102 as the leader of Partition 1. Broker 102 is a replica for Partition 0 and Broker 103 is a replica for Partition 1. If the leader broker were to fail, one of the replicas will be elected as the new partition leader by an election.
Kafka producers only write data to the current leader broker for a partition.
Kafka producers must also specify a level of acknowledgment
acks to specify if the message must be written to a minimum number of replicas before being considered a successful write.
The default value of
acks has changed with Kafka v3.0
if using Kafka < v3.0,
if using Kafka >= v3.0,
acks=0 producers consider messages as "written successfully" the moment the message was sent without waiting for the broker to accept it at all.
If the broker goes offline or an exception happens, we won’t know and will lose data. This is useful for data where it’s okay to potentially lose messages, such as metrics collection, and produces the highest throughput setting because the network overhead is minimized.
acks=1 , producers consider messages as "written successfully" when the message was acknowledged by only the leader.
Leader response is requested, but replication is not a guarantee as it happens in the background. If an ack is not received, the producer may retry the request. If the leader broker goes offline unexpectedly but replicas haven’t replicated the data yet, we have a data loss.
acks=all, producers consider messages as "written successfully" when the message is accepted by all in-sync replicas (ISR).
The lead replica for a partition checks to see if there are enough in-sync replicas for safely writing the message (controlled by the broker setting
min.insync.replicas). The request will be stored in a buffer until the leader observes that the follower replicas replicated the message, at which point a successful acknowledgement is sent back to the client.
min.insync.replicas can be configured both at the topic and the broker-level. The data is considered committed when it is written to all in-sync replicas -
min.insync.replicas. A value of 2 implies that at least 2 brokers that are ISR (including leader) must respond that they have the data.
If you would like to be sure that committed data is written to more than one replica, you need to set the minimum number of in-sync replicas to a higher value. If a topic has three replicas and you set
2, then you can only write to a partition in the topic if at least two out of the three replicas are in-sync. When all three replicas are in-sync, everything proceeds normally. This is also true if one of the replicas becomes unavailable. However, if two out of three replicas are not available, the brokers will no longer accept produce requests. Instead, producers that attempt to send data will receive
For a topic replication factor of 3, topic data durability can withstand the loss of 2 brokers. As a general rule, for a replication factor of
N, you can permanently lose up to
N-1 brokers and still recover your data.
Regarding availability, it is a little bit more complicated... To illustrate, let's consider a replication factor of 3:
Reads: As long as one partition is up and considered an ISR, the topic will be available for reads
acks=1: as long as one partition is up and considered an ISR, the topic will be available for writes.
min.insync.replicas=1(default): the topic must have at least 1 partition up as an ISR (that includes the reader) and so we can tolerate two brokers being down
min.insync.replicas=2: the topic must have at least 2 ISR up, and therefore we can tolerate at most one broker being down (in the case of replication factor of 3), and we have the guarantee that for every write, the data will be at least written twice.
min.insync.replicas=3: this wouldn't make much sense for a corresponding replication factor of 3 and we couldn't tolerate any broker going down.
in summary, when
min.insync.replicas=Mwe can tolerate
N-Mbrokers going down for topic availability purposes
min.insync.replicas=2 is the most popular option for data durability and availability and allows you to withstand at most the loss of one Kafka broker
Kafka consumers read by default from the partition leader.
But since Apache Kafka 2.4, it is possible to configure consumers to read from in-sync replicas instead (usually the closest).
Reading from the closest in-sync replicas (ISR) may improve the request latency, and also decrease network costs, because in most cloud environments cross-data centers network requests incur charges.
The preferred leader is the designated leader broker for a partition at topic creation time (as opposed to being a replica).
The process of deciding which broker is a leader at topic creation time is called a preferred leader election.
When the preferred leader goes down, any partition that is an ISR (in-sync replica) is eligible to become a new leader (but not a preferred leader). Upon recovering the preferred leader broker and having its partition data back in sync, the preferred leader will regain leadership for that partition.
Conduktor & Kafka Topic Replication Factor
Conduktor can help you create topics with a defined replication factor and change it if needed.