Distributed systems today form the backbone of scalable and fault-tolerant applications. These systems rely heavily on replication to ensure high availability and fault tolerance. However, with replication comes the challenge of maintaining consistency across nodes, especially in the presence of network partitions and failures. One commonly used method to handle this is the Quorum Algorithm. … Read more
In computing, scaling means increasing the capacity of your system so it can handle more load — more users, more requests, more data. You do this either by upgrading your existing server (vertical scaling) or by adding more servers (horizontal scaling). 1. Vertical Scaling (Scale-Up) What is Vertical Scaling? Vertical scaling means increasing the power … Read more
In the realm of distributed systems, data replication is a foundational concept that ensures availability, fault tolerance, and performance by maintaining copies of data across multiple nodes. As systems scale across data centers and geographies, replication becomes essential to ensure uninterrupted service and disaster recovery. However, replication also introduces complexity in maintaining consistency, synchronization, and … Read more
When your data is stored on many computers (servers) instead of one, we need to make sure that everyone sees the same data — even if they’re asking different servers. A consistency model is like a rulebook. It says: “If one person changes something, when and how should others see it?” Let’s look at the … Read more
If you’re building or using distributed systems like databases or microservices, you’ve probably come across the term CAP Theorem. But what does it really mean, and why should you care? CAP theorem, also known as Brewer’s Theorem, states that in any distributed data system, it is impossible to simultaneously provide all three of the following … Read more
A beginner-friendly Q&A guide that breaks down how Kafka works behind the scenes — from brokers and partitions to group coordinators and offsets. Does the group coordinator assign a broker to a partition? No. The group coordinator assigns partitions to consumers, not brokers. Brokers are already assigned to partitions (as leaders) by the Kafka Controller. … Read more
Kafka Consumers are essential components in any data pipeline that reads messages from Kafka topics and processes them for analytics, storage, or downstream services. In this article, we’ll explore: What a Kafka Consumer is How Kafka Consumer Groups work Important consumer configurations How to implement a Kafka consumer using Spring Boot 🔸 What is a … Read more
In a distributed system, data producers are responsible for pushing messages into Kafka topics. Apache Kafka’s producer API is built to be high-throughput, fault-tolerant, and configurable to suit different delivery guarantees. In this article, we’ll explore: What a Kafka Producer is Key producer configurations How to send messages A working Spring Boot example to implement … Read more
Apache Kafka provides several configuration options for topics, brokers, producers, and consumers. Knowing the default values and purpose of these settings is crucial when tuning Kafka for production or development environments. Below is a table of the most commonly used Kafka configuration properties, their default values, and a brief description. 🔧 Main Kafka Configuration Table … Read more