Replications in Kafka: Kafka Replication Factor Included

Organizations employ data-driven ways to operate their business operations efficiently in order to stay ahead of their competitors. Lagging applications or websites can stifle business expansion. Advanced-Data Pipelining and Data Streaming microservices are required to provide high performance, scalability, and flexibility to business applications.

Apache Kafka is an event streaming platform and a pub-sub system that allows users to read and publish data more easily. It is used by companies to distribute events at high throughput. Developers can use Kafka Data Pipeline to stream data in real-time from source to target with high throughput.

Are you looking to set up Kafka Replication? Don’t worry, we have you covered. This blog will act as your guide in understanding how Kafka Replication works and how you can configure it easily.

What is Kafka?

Kafka is a stream-based, distributed message broker software that receives messages from publishers and distributes them to subscribers. Kafka stores messages in physically distributed Locations, Processes, Streams, and Response to events. 

To reduce the overhead of network round trips, Kafka groups messages together forming the “Message Set” abstraction, which leads to larger Network Packets, larger Sequential Disk operations, contiguous Memory Blocks, etc., allowing Kafka to turn a bursty stream of random message writes into linear writes. Kafka is used for Event Processing, Real-Time Monitoring, Log Aggregation, and Queuing. 

Key Features of Kafka

Apache Kafka is widely popular because of its capabilities that maintain availability, simplify scaling, and allow it to handle massive volumes, among other things. Take a look at some of the powerful features it provides:

• Extensibility: Since Kafka’s recent popularity, various other software programs have developed connectors. This facilitates the creation of new features, such as integration with other programs. See how you can use Kafka to interface with Redshift and Salesforce.
• Log Aggregation: Data recording from multiple system components must be centralized to a single area because a modern system is typically distributed. Kafka frequently acts as a single source of truth by centralizing data from all sources, regardless of shape or volume.
• Stream Processing: It is Kafka’s main skill. It allows him to perform real-time calculations on Event Streams. From real-time data processing to dataflow programming, Kafka ingests, stores, and analyses stream of data as they are created.

In the next sections, you will understand Data Organization in Kafka and also learn about Kafka Replication in detail.

Data Organization in Kafka 

Kafka manages data in logically separate Topic. A Topic is a collection of semantically similar records. e.g. Locational data of all parcels in transit can form a Topic. 

The records within a Topic are stored in partitions where each partition can be stored in a separate machine, easing parallel reads and availability. The number of partitions in a Topic must be declared at the time of Topic creation. A low number of partitions eases Distributed Clustering, and a higher number of partitions per topic will lead to improved Throughput but a higher risk of Unavailability and higher end-to-end Latency. 

Each message in a partition is assigned a unique integer value called Offset. Kafka assures that Offset i will always be processed before offset i+1. Within a partition, all messages are stored in a sorted manner, based on each message’s Offset. This arrangement creates what is called a “Write-Ahead Log“.

Now, that you have understood how the data is organized in Kafka, let’s discuss what is Kafka Replication in the next section.

What is Kafka Replication?

In this section, you will understand Kafka Replication. In addition, you will learn about how Zookeeper helps in Kafka Replication.

• Kafka Replication Overview: Kafka Replication ensures high availability by having multiple copies of data (partitions) distributed across multiple brokers. If one broker goes down, other replicas can serve the requests.

• Partition-Level Replication: Kafka Replication occurs at the partition level. Each partition has one leader replica and several follower replicas (in-sync replicas). The leader handles all read and write operations for the partition, while followers replicate data from the leader.

• Kafka Replication Factor: Kafka replication factor refers to the total number of replicas (including the leader) for each partition. This determines the fault tolerance and availability of the partition.

• ZooKeeper’s Role: ZooKeeper is responsible for managing Kafka clusters by synchronizing distributed brokers, maintaining configurations, and handling leader elections. It ensures that nodes stay synchronized and in communication through a heartbeat mechanism.

• ZooKeeper’s Heartbeat Mechanism: Kafka nodes send regular “Keep-Alive” (heartbeat) messages to ZooKeeper. If ZooKeeper doesn’t receive this message within a configurable timeout (zookeeper.session.timeout.ms), the node is considered dead, and a new leader election occurs.

Some other important parameters to be configured are:

• min.insync.replicas: Specifies the minimum number of replicas that must acknowledge a write for the write to be considered successful. 
• offsets.retention.check.interval.ms: Frequency at which to check for stale Offsets.
• offsets.topic.segment.bytes: This should be kept relatively small in order to facilitate faster Log Compaction and Cache Loads.
• replica.lag.time.max.ms: If the follower has not consumed the Leaders log OR sent fetch requests, for at least this much time, it is removed from the ISR.
• replica.fetch.wait.max.ms: Max wait time for each fetcher request issued by follower replicas, must be less than the replica.lag.time.max.ms to avoid shrinking of ISR.
• transaction.max.timeout.ms: In case a client requests a timeout greater than this value, it’s not allowed so as to not stall other consumers. 
• zookeeper.session.timeout.ms: Zookeeper session timeout.
• zookeeper.sync.time.ms: How far a follower can be behind a Leader, setting this too high can result in an ISR that has potentially many out-of-sync nodes.

Kafka Replication can be a tiresome task without the right set of tools. Hevo’s Data Replication & Integration platform empowers you with everything you need to have a smooth Data Collection, Processing, and Replication experience. It helps you transfer data from a source of your choice without writing any code.

Why do Replicas Lag Behind?

For a variety of reasons, a follower clone may lag behind the leader.

• Slow Replica: A replica may be unable to keep up with the rate at which the leader receives new messages if the rate at which the leader receives messages is greater than the rate at which the replica copies messages, resulting in an IO bottleneck.
• Stuck Replica: If a replica has ceased requesting fresh messages from the leader owing to factors such as a dead replica or GC blocking the replica (Garbage collector).
• Bootstrapping Replica: When the user increases the topic’s replication factor, the new follower replicas are out of sync until they catch up to the leader’s log.

How do you determine that a replica is lagging?

In all circumstances, this model for detecting out-of-sync stuck clones works well. It keeps track of how long a follower duplicate has been alive without sending a fetch request to the leader. The model for detecting out-of-sync slow replicas using the number of messages, on the other hand, only works well if these parameters are set for a single topic or multiple topics with similar traffic patterns, but it doesn’t scale to the variety of workloads across all topics in a production cluster.

For example, if foo receives data at a rate of 2 msg/sec and a single batch received on the leader rarely exceeds 3 messages, replica.lag.max.messages for that topic can be set to 4. Since the follower logs will be no more than three messages behind the leader’s logs after the largest batch is appended to the leader and before the follower replicas duplicate those messages. At the same time, you want the leader to remove the sluggish follower replica and prevent the message write latency from growing if the follower replicas for topic foo start lagging behind the leader by more than 3 messages.

Conclusion

To summarize, we have discussed the importance of Kafka and how to use it to its optimal efficiency.  If you’re looking to enhance the scalability, fault-tolerance, and other features for an optimized Kafka Replication, this is a combination that you must implement. If you’re comfortable with manually configuring the Kafka Replication, you can follow the above-mentioned steps for Kafka Replication.

Hevo Data, a No-code Data Pipeline, helps you transfer data from a source of your choice in a fully automated and secure manner without having to write the code repeatedly. Hevo, with its strong integration with 150+ sources & BI tools, such as Kafka allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiff.

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