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.
Table of Contents
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.
In the next sections, you will understand Data Organization in Kafka and also learn about Kafka Replication in detail.
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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.
In Kafka parlance, Kafka Replication means having multiple copies of the data, spread across multiple servers/brokers. This helps in maintaining high availability in case one of the brokers goes down and is unavailable to serve the requests. Before we discuss methods to achieve useful Kafka Replication, let’s familiarize ourselves with some key concepts and terminology.
Kafka Replication is allowed at the partition level, copies of a partition are maintained at multiple broker instances using the partition’s Write-Ahead Log. Amongst all the replicas of a partition, Kafka designates one of them as the “Leader” partition and all other partitions are followers or “in-sync” partitions.
The Leader is responsible for receiving as well as sending data, for that partition. The total number of replicas including the leader constitute the Replication factor. To maintain these clusters and the topics/partitions within, Kafka has a centralized service called the Zookeeper.
Zookeeper takes care of the synchronization between the distributed clusters and manages the configurations, controlling and naming. Zookeeper Atomic Broadcast (ZAB) protocol is the brain of the whole system. Each replica or Node, sends a “Keep-Alive” message to Zookeeper at regular intervals, thereby informing the Zookeeper that it’s alive and functional. If the Zookeeper does not receive this Keep-Alive message ( called heartbeat) within the designated configurable time ( 6000ms, by default), it assumes that the node is dead and if this node was a leader, a new leader election takes place.
The parameter zookeeper.session.timeout.ms milliseconds is set to 6000 by default. Also, the node must not have a substantial backlog of messages that it did not receive from the Leader and did not process, i.e., the difference between the Leader’s Offset and Replica’s Offset must be less than a prescribed limit.
The parameter replica.lag.max.messages, decides the allowed difference between Replica’s Offset and Leader’s Offset. If this difference is more than replica.lag.max.messages-1, then the node is considered lagging behind and is removed from the list of in-sync nodes, by the leader.
Hence, a node is considered alive by Kafka if and only if, it meets the following two conditions:
- A node must be able to maintain its session with the ZooKeeper via ZooKeeper’s heartbeat mechanism.
- If it is a follower it must replicate the writes happening on the leader and not fall “too far” behind.
All nodes that are alive and in-sync, form the In-Sync Replica Set (ISR). Now, if all the in-sync nodes have applied a message to their respective logs, this message is considered committed and is then sent to the consumers. This way, Kafka guarantees that a committed message will not be lost, as long as there is at least one alive and in sync replica, at all times.
An out-of-sync node is allowed to rejoin the ISR if it can re-sync fully again, even if it lost some data due to its crash.
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.
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.VISIT OUR WEBSITE TO EXPLORE HEVO
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