Fault Tolerance and Resiliency in Apache Kafka: Safeguarding Data Streams

Apache Kafka is an open-source, distributed event store and stream processing platform. It is a high-throughput, low-latency platform to handle the streams of data in real time. Today, Kafka has become a de facto technology for building real-time data pipelines and real-time data streaming applications.

Being a distributed system, ensuring fault tolerance and resiliency is of the utmost importance to ensure high availability, data integrity, and scalability. In this blog, we will discuss what fault tolerance and resiliency in Apache Kafka are and what mechanisms or configuration changes are required to achieve them. 

What is Fault Tolerance? 

In general, fault tolerance refers to the ability of the system to remain functional without any interruption despite the failure of one or more components. Fault tolerant systems leverage backup components that are replaced with failed components, ensuring that the service continues.

Fault tolerance in Apache Kafka refers to the ability of the system to serve data reliably and continue its operation regardless of some failures. The primary objective of fault tolerance is to make the data as well as the system highly available and prevent disruptions due to a single point of failure. 

What is Resiliency? 

Resiliency is the ability of a system to withstand adversities and bounce back to a normal state. In short, it refers to the ability to adapt to difficulties and recover from unexpected events. 

In the context of Apache Kafka, resiliency refers to its ability to maintain the storage and delivery of messages intact even in case of component failure, unexpected events, and network issues. 

Resilience vs. Fault Tolerance

Now, you might be wondering how both these terms – fault tolerance and resilience – differ from each other. Let us demystify it.

The primary difference between resilience and tolerance is that the former focuses on adapting to different, unusual conditions and recovering from unexpected events and failures, in addition to being operational despite disruptions. On the flip side, fault tolerance primarily concentrates on handling failures. We can think of fault tolerance as one of the resilience strategies in Kafka. 

Kafka’s Approach to Fault Tolerance 

Before we move on to discussing fault tolerance strategies, let us first understand a few terms. 

• Kafka Topic: A topic is a unique name assigned to a category or feed name where producers publish messages. Simply put, a topic is a stream of messages. Producers write data to a topic, and consumers read data from a topic. 
• Kafka Broker: A Kafka broker is nothing but a server or node in a cluster that receives and transmits data. It is responsible for managing data records/messages in Kafka topics. 
• Topic Partition: Each Kafka topic is divided into multiple partitions, where each partition is immutable. 

Let us now learn the approach to fault handling in Apache Kafka. 

• Partition Replication 

Partition replication is the most common strategy for fault tolerance in Apache Kafka. It involves creating the replicas of partition data on multiple brokers (nodes) in a cluster. So, when a specific broker goes down, other brokers in a cluster are available to serve the replica of data. This ensures high availability of data and continuity of service.

Each broker has one or more partitions where one is a leader for a topic and all others are replicas. The leader partition is responsible for managing updates to replicas with new data, as every read/write operation is coordinated by it. 

• Controller Broker 

A controller broker is in charge of electing a broker leader. It is similar to other brokers with an additional responsibility. It leverages Zookeeper to keep track of brokers joining and leaving the cluster. Each Kafka cluster has a single controller broker. 

• Zookeeper: It is a centralized service that stores the metadata of a topic, partition, and broker. It keeps track of every broker and performs health checks from the time it registers itself to Zookeeper. 
• Lead Partition Election 

Whenever a leader broker goes down, the Zookeeper informs the controller. Further, the controller chooses a leader from in-sync replicas (ISR). An in-sync replica is a replica that matches the changes of a leader broker. The leader broker keeps track of in-sync replicas and informs about the same to Zookeeper. Whenever the leader broker comes back, it again becomes the leader. 

• Controller Election

But what if the controller fails? In such a scenario, Zookeeper informs all brokers about the failure of the controller. So, all brokers apply to become the controller. The one who applies first becomes the controller.  

Read More: Understanding Apache Cassandra Architecture

Resilience Strategies in Kafka

As discussed above, fault tolerance is one of the major resilience strategies in Kafka. In addition to it, some other strategies include: 

• Data Retention 

In Apache Kafka, data retention refers to the duration of messages stored in Kafka topics before they are deleted. However, you can change this duration by modifying the data retention policies. 

Messages are stored in log segments on disk. These segments are closed on reaching the size or time limit. Retention policies are responsible for deciding which log segments are ready for deletion. This deletion ensures the efficient management of disk space while making data highly available. 

As a result, data retention creates a perfect balance between storage efficiency and data availability. It ensures reliable operations and recovery capabilities. 

• Consumer Group Coordination 

Consumers in Kafka are combined and called a consumer group, where each consumer has a unique and specific role or purpose. In consumer group coordination, Kafka ensures that each consumer consumes a unique message within a group. The distribution of workload among different consumers facilitates parallel processing, improving performance. 

However, if any consumer fails, Kafka automatically redistributes the workload among other consumers in a group. This capability of dynamically adapting to failures increases resiliency. 

• Monitoring and Alerting 

The monitoring system helps identify potential issues, and the alerting system helps respond to them instantly. 

Administrators keep track of metrics, such as broker lag, consumer lag, and partition health, to monitor the health and performance of the Kafka cluster. 

• Broker Lag: It is the time between the message being produced and consumed. 
• Consumer Lag: It is the time between the latest message being produced and the last message consumed. 

On the other hand, administrators can configure the alert system to send notifications when specific predefined thresholds are exceeded. For instance, administrators receive alerts when the broker lag or consumer lag surpasses their threshold value, resulting in performance degradation. 

• Disaster Recovery

Two major disaster recovery mechanisms in Kafka are cross-datacenter replication (CDCR) and Kafka MirrorMaker. Cross-data center replication (CDCR) involves replicating data across multiple data centers to avoid the unavailability of data in case of data center-level failure. When a data center fails, applications can switch to another data center containing the replica of data. This ensures continuous operations without any disruptions.  

Kafka MirrorMaker involves replicating data between Kafka clusters located at different geographical regions. Like CDCR, when a cluster goes down, applications move to another cluster containing replicated data. 

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Conclusion 

Fault tolerance and resiliency in Apache Kafka are crucial aspects that enable organizations to build and deploy highly available and resilient data pipelines. While fault tolerance primarily focuses on handling failures, resiliency makes the system capable of adapting to several unfavorable conditions and recovering from failures. With replication, partitioning, data retention, CDCR, Kafka MirrorMaker, and monitoring & alerting strategies, Kafka ensures high availability and durability of data even in case of hardware failures, network issues, etc. 

In short, Kafka’s fault tolerance and resiliency strategies make it a reliable platform for organizations to build high-scalable data pipelines and streaming applications.