Are you facing data consistency issues with your real-time data streaming application? Do you want to get rid of all your data issues and build a fault-tolerant real-time system? If yes, then you’ve landed at the right place! This article will answer all your queries & relieve you of the stress of finding a truly efficient solution.
Kafka provides high data availability & durability via its data replication process. It allows you to specify the Kafka Replication Factor for the deciding the number of replicas you want. You can also check out our easy step-by-step guide to help you master the skill of efficiently setting up Kafka Replication using in-sync replicas.
This article aims at providing you with in-depth knowledge about how Kafka handles replication, Kafka in-sync replicas and the Kafka Replication Factor to make the data replication process as smooth as possible.
Table of Contents
What is Apache Kafka?
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Apache Kafka is a popular real-time data streaming software that allows users to store, read and analyze streaming data using its open-source framework. Being open-source, it is available free of cost to users. Leveraging its distributed nature, users can achieve high throughput, minimal latency, computation power, etc. and handle large volumes of data with ease.
Written in Scala, Apache Kafka supports bringing in data from a large variety of sources and stores them in the form of “topics” by processing the information stream. It uses two functions, namely Producers, which act as an interface between the data source and Apache Kafka Topics, and Consumers, which allow users to read and transfer the data stored in Kafka.
Key Features of Apache Kafka
- Scalability: Apache Kafka has exceptional scalability and can be scaled easily without downtime.
- Data Transformation: Apache Kafka offers KStream and KSQL (in case of Confluent Kafka) for on the fly data transformation.
- Fault-Tolerant: Apache Kafka uses brokers to replicate data and persists the data to make it a fault-tolerant system.
- Security: Apache Kafka can be combined with various security measures like Kerberos to stream data securely.
- Performance: Apache Kafka is distributed, partitioned, and has very high throughput for publishing and subscribing to the messages.
For further information on Apache Kafka, you can check the official website here.
What is Data Replication in Apache Kafka?
Apache Kafka is a real-time platform distributed across various clusters that allows you to stream events with ease. Apache Kafka uses the concept of data replication to ensure high availability of data at all times via the Replication Factor Kafka. It supports data replication at the partition level, as it stores all data events in the form of topic-based partitions, and hence makes use of the topic partition’s write-ahead log to place partition copies across different brokers.
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- Leaders: It is the first broker that receives the copy of your desired partition and is not only responsible for receiving data but also sending data to other available brokers.
- Followers: Each follower represents a copy of the leader’s data partitions and is also known as an in-sync replica. In-sync replicas act as the subset of numerous data partitions that have the same data messages as the leader.
With the leader-followers concept in place, Apache Kafka ensures that you’re able to access the data from the follower brokers in case a broker goes down. To do this, Apache Kafka will automatically select one of the in-sync replicas as the leader, which will further help send and receive data. It also allows you to configure the number of in-sync replicas you want to create for a particular Apache Kafka Topic of your choice.
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What is Kafka Replication Factor?
Kafka Replication Factor refers to the multiple copies of data stored across several Kafka brokers. Setting the Kafka Replication Factor allows Kafka to provide high availability of data and prevent data loss if the broker goes down or cannot handle the request. To ensure data security it is always recommended that the Kafka Replication Factor value should be more than 1. This indicates that at least one replica of the file is present in another broker and can be accessed by you in the event of a server failure.
You will notice that replication is achievable in Kafka at the partition granularity level. Applying a specialized partition ahead log, Apache Kafka effectively ensures that multiple copies of the data exist in different brokers according to the mentioned Kafka Replication Factor.
Eventually, you will need to extract all of this data from Kafka to your desired destination for further analysis. Manually consolidating data from Kafka Clusters and all your sources can be a time-consuming and resource-intensive job.
Hevo, a No-code Data Pipeline, provides a one-stop solution for all Kafka use cases and provides you with real-time ETL facilities. Hevo initializes a connection with Apache Kafka Bootstrap Servers and seamlessly collects the data stored in their Topics & Clusters. Once the Pipeline is created, Hevo fetches new and updated data every five minutes from your Kafka cluster. Hevo’s end-to-end Data Management service automates the process of not only loading data from Kafka but also transforming and enriching it into an analysis-ready form.
As discussed above, Apache Kafka is built on the concept of Leader-Follower to provide high data availability in all cases. For instance, you can consider that you have a cluster consisting of 3 brokers namely Broker 1, Broker 2 & Broker 3. A Topic-X is created with partition 1 and partition 0. If you set up the Kafka Replication Factor as 2, then you find that now Partition 0 has a replica in Broker 1 & 2 and Partition 1 has a replica in Broker 2 & 3. Kafka chooses one partition as a leader and the rest of the replicas are selected as the followers.
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Data Availability & Durability
A Kafka Producer can always choose to wait for the message to receive confirmation from 0,1 or all (-1) replicas. Though “Acknowledgement by All Replicas” doesn’t provide a complete guarantee that all the known replicas have surely received the message. By default, if acks = all, an acknowledgment occurs when all current asynchronous replicas receive the message. For example, if the topic consists of only two replicas and one fails, the write with acks = all still succeeds for the one synchronized replica left. However, if the remaining replicas also fail, then that specific write can be lost. This maximizes partition availability, but this scenario may not be desirable for some users who want data durability in comparison to availability.
To achieve improved data durability instead of availability, Kafka provides you with the following two configuration options:
- You can deselect the Unclean Leader election. For cases when all the replicas are unavailable, all the partitions will remain unavailable until the most recent leader comes live again. Thereby, prioritizing message loss prevention over availability.
- You can also set a minimum size of ISRs(In-sync replicas). This forces the partitions to accept writes only if the size of the ISR exceeds a certain minimum so that messages written only to a single replica that became unavailable are not lost. You can only configure this if the producer uses acks = all and ensures that the message will be seen by at least that number of asynchronous replicas.
Managing Replicas
After you have configured the Kafka Replication Factor, Kafka employs various strategies for effectively managing its replicas to maintain a smooth, swift, and error free performance:
- The partitions are balanced in a round-robin pattern to prevent clustering all partitions for high-volume topics on fewer nodes.
- Efficiently balancing leadership, Kafka ensures that each node is a leader for the right amount of partitions.
- Kafka takes care of optimizing the uncertain period of unavailability of a leader by choosing one of the brokers as a controller. The controller ensures detecting broker-level failures and replacing the leader of all affected partitions in the failed broker. As a result, several necessary leadership change notifications can be batched, making the process of selecting a large number of partitions much economic and faster. In a case where the controller itself fails, Kafka selects one of the functioning brokers as the new controller.
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Prerequistes
- Working knowledge of Apache Kafka.
- Apache Kafka installed at the host workstation.
How does the Replication Factor in Kafka works?
When configuring the Kafka Replication Factor, Apache Kafka makes use of the in-sync replicas to implement the leader-follower concept to carry out data replication and hence ensures availability of data even in the times of a broker failure.
You can learn about how you can enable replication in Apache Kafka and configure the Kafka Replication Factor to match your business needs from the following sections:
Kafka Replication Factor: Setting up Replication
With Apache Kafka in place, you can configure the Kafka Replication Factor as per your data and business requirements. It allows you to set up replication with ease, by assigning an integer value to the parameter “min.insync.replicas”. To do this, you can either use the Apache Kafka UI to configure it or configure at the time of Apache Kafka Topic creation. You can also alter your existing Apache Kafka Topic and modify it.
Using the Apache Kafka UI to Configure the min.insync.replicas Parameter
- Step 1: To configure the “min.insync.replicas” parameter using the Apache Kafka UI, launch your Apache Kafka Server and choose a cluster of your choice from the navigation bar on the left.
- Step 2: Once you selected it, select the Apache Kafka Topic that you want to configure and click on the edit settings option, found under the configurations section.
A new window will now open up, where you will be able to modify the Kafka Replication Factor settings for your Apache Kafka Topic.
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- Step 3: To modify the “min.insync.replicas” parameter, you will have to switch to the expert mode. You can do this by clicking on the button found at the bottom of your screen. You can now modify it as per your requirement.
- Step 4: Once you’ve made the necessary changes, click on the save changes option found at the bottom of your screen and restart your Apache Kafka Server to bring the changes into effect.
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This is how you can use the Apache Kafka UI to configure the “min.insync.replicas” parameter to set up Replication Factor in Kafka.
Altering Apache Kafka Topics to Configure the min.insync.replicas Parameter
- Step 1: Apache Kafka allows users to alter or edit their existing Apache Kafka Topics, to modify the “min.insync.replicas” parameter. You can do this by executing the following command:
/usr/bin/kafka-topics --alter --zookeeper zk01.example.com:2181 --topic topicname --config min.insync.replicas=Integer_value
- Step 2: For example, if you want to set the parameter to two, you can do so as follows:
/usr/bin/kafka-topics --alter --zookeeper zk01.example.com:2181 --topic topicname --config min.insync.replicas=2
This is how you can alter your existing Apache Kafka Topics and modify the “min.insync.replicas” parameter to set up Kafka Replication.
Kafka Replication Factor: How Kafka Acknowledges Replication
Whenever a new event comes into the Apache Kafka Topic, Apache Kafka automatically creates a single/multiple min in-sync replicas based on the Apache Kafka Topic Replication Factor configurations.
- To ensure a smooth process, Apache Kafka makes use of an acknowledge-based mechanism and hence acknowledges the in-sync replicas before sending any new records to the Apache Kafka Topic.
- Each Apache Kafka Producer thus has an “acks” parameter, that lets you configure whether you want to acknowledge the replica or not.
- You can set the “acks” parameter to 0/1/all depending upon your application needs.
| Acks | Description | Notes |
| 0 | Don’t wait for any acknowledgment | Possible data loss |
| 1 | Wait for leader’s acknowledgement | Partial data loss |
| all | Wait for leader’s and all the in-sync replicas’ acknowledgement | No data loss |
For example, if you’re working with an application that handles critical data, you can set the “acks” parameter to “all“, to ensure data availability at all times. However, configuring the “acks” parameter to “all” can result in slower performance as it can add some latency to the process.
This is how Apache Kafka acknowledges data replication via the Kafka Replication Factor.
Kafka Replication Factor: Why Followers Lag Behind a Leader
While working with Kafka Replication Factor, numerous aspects can cause the follower replica to lag behind the leader:
- The rate at which the leader receives data messages is usually faster than the rate at which a follower replica can copy the data messages. This often results in an IO bottleneck, as the Apache Kafka replica finds it challenging to cope up with the pace.
- Issues such as garbage collection can prevent the Apache Kafka replica from requesting data from the leader. In such situations, the Apache Kafka replica is either in a dead state or a blocked state and hence is not able to get the new data.
Conclusion
This article teaches you how to set up Kafka Replication Factor with ease and answers all your queries regarding it. It provides a brief introduction of Kafka Replication Factors, various concepts related to it, etc. to help users understand them better and use them to perform data replication & recovery in the most efficient way possible. The manual replication methods, however, can be challenging as they require a deep understanding of Java programming language and other backend tools. This is where Hevo saves the day!
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Vishal has a passion towards the data realm and applies analytical thinking and a problem-solving approach to untangle the intricacies of data integration and analysis. He delivers in-depth researched content ideal for solving problems pertaining to modern data stack.