Amitesh Bharti★’s Post

🔄 𝐑𝐚𝐛𝐛𝐢𝐭𝐌𝐐 𝐯𝐬 𝐊𝐚𝐟𝐤𝐚 𝐯𝐬 𝐀𝐜𝐭𝐢𝐯𝐞𝐌𝐐: 𝐂𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐌𝐞𝐬𝐬𝐚𝐠𝐞 𝐁𝐫𝐨𝐤𝐞𝐫 🛠️ Choosing the right messaging tool can be crucial for optimizing real-time data processing, asynchronous communication, or event-driven architectures. RabbitMQ, Kafka, and ActiveMQ each offer unique strengths tailored to different needs. 🐰 RabbitMQ: The Versatile Workhorse RabbitMQ is a popular, lightweight message broker that supports multiple protocols like AMQP, MQTT, and STOMP. It’s ideal for scenarios requiring reliability, routing, and complex message workflows. ✅ Pros: Rich Routing Capabilities: Supports complex routing (e.g., fanout, topic, and headers exchanges). Message Acknowledgments: Ensures message delivery with features like acknowledgments and retries. Pluggable Architecture: Easily extendable with plugins for monitoring, logging, etc. ❌ Cons: Not Ideal for High Throughput: Slower than Kafka for handling large volumes of messages. Single Point of Failure: Needs clustering or mirroring for high availability. 💡 Use Case: Ideal for task queues, order processing, and workflows where reliability and flexible message routing are key. ⚡ Kafka: The High-Throughput Beast Apache Kafka is built for high throughput and low-latency message streaming, ideal for handling millions of messages per second. With disk-based message storage, it’s a reliable choice for data pipelines and streaming analytics.✅ Pros: Scalable & Distributed: Built for horizontal scaling with partitioned message logs. Event Streaming: Optimized for real-time data streaming and processing. Data Retention: Retains messages even after they are consumed, ideal for data replay. ❌ Cons: Complex Setup: Requires more configuration and management (e.g., Zookeeper). No Built-in Message Prioritization: Limited support for priority queues. 💡 Use Case: Perfect for real-time analytics, log aggregation, and event sourcing where high throughput and durability are essential. 📡 ActiveMQ: The Legacy Champion ActiveMQ is a mature message broker that supports the JMS (Java Message Service) API. It’s a reliable choice for traditional enterprise applications needing durable messaging. ✅ Pros: JMS Support: Seamless integration with Java-based applications. Flexible Configurations: Supports both point-to-point and publish-subscribe models. Great Tooling: Offers good monitoring, logging, and management capabilities. ❌ Cons: Moderate Performance: Slower than Kafka for handling massive message streams. Memory Intensive: Consumes more memory compared to RabbitMQ and Kafka. 💡 Use Case: Best suited for legacy enterprise systems, internal messaging, and Java-centric applications. ♻ Repost this if you find it valuable. #TechInsights #MessagingBrokers #Microservices #SystemDesign #RabbitMQ #Kafka #ActiveMQ #TechLeadership #SoftwareEngineering #DevOps #EventDrivenArchitecture

Important skill to develop. Understand the basic difference and deep dive into the Topic.

 RabbitMQ vs. Kafka vs. ActiveMQ - RabbitMQ: A message broker that implements the Advanced Message Queuing Protocol (AMQP). It excels in use cases that require reliability, message delivery guarantees, and flexibility in message routing.    - Kafka: A distributed event streaming platform designed for high-throughput, real-time data processing. Kafka is optimized for scalable and durable handling of large streams of data, and it’s particularly suited for use cases like data pipelines, log aggregation and real-time analytics. - ActiveMQ: A Java-based message broker that uses the Java Message Service (JMS) API. ActiveMQ is ideal for traditional enterprise message queuing use cases and offers support for various messaging protocols.  1. Performance and Scalability: - Kafka excels at high-throughput and horizontal scaling, ideal for large data streams. - RabbitMQ and ActiveMQ perform well but aren’t as suited for massive real-time data loads. - Kafka is best for big data, while RabbitMQ and ActiveMQ handle smaller, traditional systems well.  2. Message Priority: - RabbitMQ and ActiveMQ support message prioritization, ensuring important tasks are handled first. - Kafka does not support built-in prioritization and focuses on high throughput instead. - Use RabbitMQ or ActiveMQ when priority matters for specific messages.  3. Message Ordering: - RabbitMQ and ActiveMQ guarantee message order within individual queues. - Kafka ensures order within partitions but not across multiple partitions. - For strict ordering needs, RabbitMQ and ActiveMQ provide more control.  4. Message Model: - RabbitMQ uses a queue-based model with AMQP for flexible routing. - Kafka uses a distributed log-based model, ideal for stream processing and distributed systems. - ActiveMQ follows JMS with a queue-based model, suited for enterprise systems.  5. Durability: - Kafka achieves durability through log replication across brokers, ensuring data persistence. - RabbitMQ and ActiveMQ require explicit configuration for durability. - Kafka’s design ensures long-term data retention, even after consumption.  6. Replication: - Kafka features built-in partition replication for fault tolerance and reliability. - RabbitMQ uses mirrored queues for replication, providing redundancy but with manual configuration. - ActiveMQ relies on a primary-replica model for basic replication.  7. Stream Processing: - Kafka offers native stream processing with Kafka Streams for real-time data transformation. - RabbitMQ supports stream processing but is less efficient than Kafka for this use case. - ActiveMQ requires third-party integrations for stream processing, making it less suitable for real-time tasks. Each tool has specific strengths, and the choice depends on your needs—whether you need real-time streaming, traditional messaging, or priority-based processing.

Here's a detailed explanation of how Kafka implements asynchronous communication: 1. Producer-Consumer Decoupling: In Kafka, producers send messages to topics without needing to wait for consumers to process them. Producers can continue producing messages regardless of the consumers' state. This decoupling is a fundamental aspect of asynchronous messaging. 2. Message Queuing: Kafka topics act as distributed logs where messages are appended sequentially. When a producer sends a message to a topic, it's stored in the partition of that topic. Consumers read messages from these partitions at their own pace, which means they do not need to process messages in real-time as they are produced. 3. Broker Architecture: Kafka brokers manage the storage of messages and handle the distribution and replication of data across multiple servers. Producers send messages to brokers, which store them until consumers are ready to read. This broker-centric architecture supports asynchronous communication by managing message persistence and availability. 4. Offset Management: Each consumer group in Kafka keeps track of its own offsets (i.e., the position of the last consumed message in a topic). Consumers can commit their offsets independently, allowing them to read messages at different rates without affecting other consumers or producers. 5. Non-Blocking Operations: Kafka producers and consumers use non-blocking I/O operations, ensuring that producing and consuming messages do not block the application. For instance, a producer can send a message and immediately continue with other tasks without waiting for an acknowledgment from the broker. 6. Parallelism and Partitioning: Kafka's partitioning of topics enables parallel processing. Each partition can be consumed by a separate consumer within a consumer group, enhancing throughput and scalability. 7. Asynchronous Replication: Kafka replicates data across multiple brokers to ensure fault tolerance. This replication process is also asynchronous, meaning data is written to the leader partition first and then replicated to follower partitions in the background. This design choice enhances performance and reliability without requiring synchronous operations. 8. Batch Processing: Kafka allows both producers and consumers to process messages in batches. Producers can send multiple messages in a single request, and consumers can fetch multiple messages in a single pull operation. Batch processing supports high-throughput and asynchronous processing Hence, Kafka's design and architecture inherently support asynchronous communication through decoupling producers and consumers, managing message offsets independently, employing non-blocking I/O, enabling parallel processing through partitioning, and supporting asynchronous data replication and batch processing. #softwaredevelopment #softwareengineering #applicationdevelopment #kafka

Kafka 2/n 🧵 Streaming refers to the continuous flow of data from various sources to destinations in real-time or near real-time. It's a method of data processing where data is handled as it arrives, rather than being collected into batches before processing.   Key aspects of streaming: 1. Continuous: Data is processed as it's generated or received 2. Real-time or near real-time: Minimal delay between data generation and processing 3. Unbounded: The data flow is potentially infinite   Streaming solves several important problems:   1. Latency: It reduces the delay between data generation and action, enabling real-time decision making and responses.   2. Processing large volumes: It allows handling of big data that might be impractical to process in batches.   3. Real-time insights: Enables immediate analysis and reaction to events as they occur.   4. Resource efficiency: Can be more efficient than batch processing for certain types of data and use cases.   5. Continuous updates: Provides the ability to constantly update systems with the latest information.   6. Event-driven architectures: Facilitates building responsive, event-driven systems.   7. Handling perishable insights: Some data loses value quickly; streaming allows immediate processing of time-sensitive information.   8. Scalability: Streaming architectures can often scale more easily to handle growing data volumes.   #kafkaBasu

𝐃𝐚𝐭𝐚 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐢𝐧 𝐦𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞. A basic principle of microservices is that each service manages its own data. Two services should not share a data store. Instead, each service is responsible for its own private data store, which other services cannot access directly. The reason for this rule is to avoid unintentional coupling between services, which can result if services share the same underlying data schemas. If there is a change to the data schema, the change must be coordinated across every service that relies on that database. By isolating each service's data store, we can limit the scope of change, and preserve the agility of truly independent deployments. Another reason is that each microservice may have its own data models, queries, or read/write patterns. Using a shared data store limits each team's ability to optimize data storage for their particular service. This approach naturally leads to polyglot persistence - the use of multiple data storage technologies within a single application. One service might require the flexible schema capabilities of a document database. Another might need the strong consistency provided by an RDBMS. Dealing with data in a distributed way brings some challenges. One issue is that data might end up repeated in different places. For instance, data could be stored in a transaction and then stored again for other purposes like analysis or saving history. Having data copied or split like this can cause problems with keeping data right and consistent. Also, when data connects with many services, the usual methods for managing these connections can't be used. Traditional data design follows the rule of "one fact in one place." Each piece of information is only in the schema once. Other parts might refer to it, but they don't copy it. The benefit is that changes happen in one spot, which helps avoid issues with data being different. In microservices, you need to think about how updates spread across services, and how to handle when data is in different spots without perfect consistency. #SoftwareArchitecture #MonolithicArchitecture #MicroservicesArchitecture #SaaS #Scalability #Maintainability

𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐞𝐯𝐞𝐧𝐭-𝐝𝐫𝐢𝐯𝐞𝐧 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐰𝐢𝐭𝐡 𝐊𝐚𝐟𝐤𝐚 𝐟𝐨𝐫 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐝𝐚𝐭𝐚 𝐬𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠, 𝐞𝐧𝐚𝐛𝐥𝐢𝐧𝐠 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞, 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞, 𝐚𝐧𝐝 𝐝𝐞𝐜𝐨𝐮𝐩𝐥𝐞𝐝 𝐦𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧. Implementing an event-driven architecture with Apache Kafka is essential for modern system design. Kafka’s scalability, fault tolerance, and high throughput enable real-time data streaming and seamless microservices communication. This approach enhances reliability, scalability, and responsiveness, transforming data infrastructure and supporting advanced data processing and analytics, helping businesses maintain a competitive edge. 𝐓𝐨𝐩 𝟓 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 𝐨𝐟 𝐄𝐯𝐞𝐧𝐭-𝐃𝐫𝐢𝐯𝐞𝐧 𝐊𝐚𝐟𝐤𝐚 𝐢𝐧 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 1. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Apache Kafka handles real-time data streams. It's perfect for applications requiring immediate data processing, such as fraud detection systems, recommendation engines, and real-time analytics dashboards. 2. 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Kafka simplifies communication between microservices by providing a reliable and scalable message broker. This decouples services, making them more independent, resilient, and easier to scale. 3. 𝐄𝐯𝐞𝐧𝐭 𝐒𝐨𝐮𝐫𝐜𝐢𝐧𝐠: Implement event sourcing by using Kafka to record all changes to the state of an application as a sequence of events. This provides a robust audit trail, simplifies debugging, and supports complex data recovery scenarios. 4. 𝐋𝐨𝐠 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧: Centralize logs from various systems and applications using Kafka. This enables efficient storage, analysis, and monitoring of logs in real-time, which is crucial for maintaining system health and debugging issues. 5. 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐄𝐓𝐋: Kafka is a backbone for connecting different data systems. It facilitates efficient ETL (Extract, Transform, Load) processes by streaming data between databases, data lakes, and other storage solutions, ensuring high data integrity and low latency. 𝐀𝐜𝐜𝐞𝐬𝐬 𝐨𝐮𝐫 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐢𝐬 𝐥𝐢𝐧𝐤: https://gigamein.com/ 𝐃𝐢𝐬𝐜𝐨𝐫𝐝: https://lnkd.in/ejTCRn-X #gigame #kafka #eventdriven #microservices #realtimedataprocessing #dataIntegration #backenddevelopment

🔍 WTF (What’s That For?) – Episode 4 WTF 4: Message Queues (Kafka) 📩 In complex systems, services often need to communicate with each other to complete tasks. But what if one service is overwhelmed, slow, or temporarily unavailable? That’s where Message Queues come in. A Message Queue allows asynchronous communication between services. Instead of directly calling another service and waiting for a response, a service sends a message to a queue, where it waits until the receiving service is ready to process it. This makes systems more resilient and scalable, as services aren’t tightly coupled. 🔑 How Message Queues Work: 1️⃣ Producer: A service that sends a message (event/data) to the queue. 2️⃣ Queue:Holds the messages until a consumer is ready to process them. 3️⃣ Consumer: A service that pulls messages from the queue and processes them when it’s ready. 🔄 Enter Kafka: One of the most popular Message Queues is Apache Kafka. Kafka excels at handling large volumes of real-time data, making it a top choice for event-driven architectures and distributed systems. 🔑 How Kafka Works: 1️⃣ Producer: Sends messages (data/events) to Kafka. 2️⃣ Broker: The Kafka cluster stores these messages, organized into topics. 3️⃣ Consumer: Reads messages from Kafka at its own pace, ensuring no loss of data and efficient processing. 🔄 Key Kafka Concepts: - Topics: A category where messages are published. Think of it as a folder for events. - Partitions: Kafka splits topics into partitions for parallel processing, boosting performance. - Offset: Each message in a partition has an offset, acting as its unique identifier, allowing consumers to track what they’ve processed. 🔧 Why Kafka is Popular: - Scalability: Kafka handles huge volumes of messages, making it perfect for high-throughput environments. - Fault Tolerance: It ensures data reliability through replication across brokers. - Event-Driven Architecture: Perfect for building microservices that respond to real-time data changes. 📊 Real-World Example:  In e-commerce systems, Kafka is used to process orders asynchronously. When an order is placed, Kafka handles it as an event, updating inventory, processing payments, and notifying users without overwhelming the system. 🔧 Why Message Queues Matter: - Decoupling Services: Allows services to work independently and asynchronously. - Increased Resilience: Systems stay responsive even during heavy loads. - Guaranteed Delivery: Messages aren’t lost even if a service is down temporarily. Stay tuned for the next WTF deep dive! 💡 #TechTalk #Kafka #MessageQueues #DistributedSystems #WTF #wtfwithpushkar

🌟 𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐄𝐯𝐞𝐧𝐭-𝐃𝐫𝐢𝐯𝐞𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐰𝐢𝐭𝐡 𝐀𝐩𝐚𝐜𝐡𝐞 𝐊𝐚𝐟𝐤𝐚❗ 🚀 🌟 . . . 🔍 𝑼𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅𝒊𝒏𝒈 𝑬𝒗𝒆𝒏𝒕-𝑫𝒓𝒊𝒗𝒆𝒏 𝑨𝒓𝒄𝒉𝒊𝒕𝒆𝒄𝒕𝒖𝒓𝒆 (𝑬𝑫𝑨): Ever wondered how systems handle vast amounts of data in real-time, seamlessly reacting to changes without missing a beat? That's the magic of Event-Driven Architecture (EDA)! It's a paradigm where the flow of information is based on the occurrence of events, triggering actions and responses across systems. But when did this revolutionary approach emerge, and why? 🕰 𝑾𝒉𝒆𝒏 𝒂𝒏𝒅 𝑾𝒉𝒚 𝑬𝑫𝑨 𝑺𝒕𝒂𝒓𝒕𝒆𝒅: Back in the digital dark ages, traditional architectures struggled to keep pace with the explosive growth of data and the need for instant responses. That's when the need for EDA arose, offering a solution where systems react to events as they happen, ensuring agility, scalability, and responsiveness like never before. 🚀 𝑰𝒏𝒕𝒓𝒐𝒅𝒖𝒄𝒊𝒏𝒈 𝑨𝒑𝒂𝒄𝒉𝒆 𝑲𝒂𝒇𝒌𝒂: Enter Apache Kafka, the powerhouse behind many cutting-edge event-driven systems! Kafka acts as a high-throughput, fault-tolerant, and scalable event streaming platform, designed to handle real-time data feeds with ease. But why do we use Kafka, you ask? 🔑 𝑾𝒉𝒚 𝑾𝒆 𝑼𝒔𝒆 𝑨𝒑𝒂𝒄𝒉𝒆 𝑲𝒂𝒇𝒌𝒂: Think of Kafka as the central nervous system of your data infrastructure. It enables seamless communication between disparate systems, ensures fault tolerance, and provides horizontal scalability, making it ideal for scenarios like real-time analytics, log aggregation, and stream processing. 💼 𝑳𝒆𝒕'𝒔 𝑩𝒓𝒆𝒂𝒌 𝑰𝒕 𝑫𝒐𝒘𝒏: Now, let's dive into the components of Kafka's ecosystem and understand their roles through relatable daily life examples: 🎬 Producers: Imagine you're a movie producer, crafting compelling stories (events) that you want to share with the world. In Kafka, producers are akin to content creators, generating and publishing events to Kafka topics. 🛣 Brokers: Picture brokers as diligent mail carriers, responsible for routing and storing messages (events) within Kafka clusters. They ensure messages are delivered reliably and efficiently to their intended recipients. 📚 Topics: Topics serve as virtual channels or categories where related events are organized. It's like different shelves in a library, each dedicated to a specific genre or topic, making it easier for consumers to find what they're interested in. 🧩 Partitions: Think of partitions as slices of a pizza. Each partition holds a subset of the topic's data, allowing for parallel processing and scalability. Just like how you divide a pizza to share with friends, partitions distribute the workload across multiple consumers. 👥 Consumers: Finally, consumers are the eager readers who subscribe to specific topics (book genres) to receive and process events of interest. They extract insights, trigger actions, or simply enjoy the stream of events flowing through Kafka.

While microservices are supposed to work autonomously, they often surprisingly end up exhibiting functionality and private state dependencies amongst each other This research provides a few insights https://lnkd.in/et2GmhaT Each microservice and respective DB are usually bundled in separate containers, ensuring that each can be scaled independently and faults are limited to the container boundary A trend (15%) of use of the following stack was observed: RDBMS + Redis + MongoDB + Elasticsearch Use of relational or document DBMSs for the underlying microservice DBs, Redis as a caching layer for fast data access to recurring requests, and replication of data through an event-driven approach to Elasticsearch for fast online analytical queries Event-based workflows imply that updates and operations affecting other microservices are queued for asynchronous processing The main difference between the orchestration and the choreography is the type of communication. The former is synchronous and HTTP based, whereas the latter is mostly event-based and asynchronous Most microservice-based applications often perform operations that span multiple microservices ⭕Queries aggregating data from different microservices. A consumer service contacts (HTTP requests) a set of microservices through their APIs. After receiving all responses, it aggregates the data in-memory and serves the client ⏹Use of composition of service calls, a microservice performs synchronous requests to get data from other microservices ⏹Use of the BFF pattern ⏹Use of the API Gateway pattern ⭕Replication  ⏹Replication across microservices. A microservice is generating events related to its own updated data items. These changes are then communicated asynchronously, via a broker that supports persistent messaging ⏹Replication to a DB 🔷Daemon workers, one for each microservice and its respective generated events, or a central service, are responsible for subscribing to data item updates and replicating these to a special-purpose DB used for querying 🔷Use of batch workers to extract data from microservices periodically (pull) and replicate it in a neutral repository for fast querying (e.g., Elasticsearch) ⏹Use of data stream processing systems to handle streams generated by microservices to build materialised views ⭕Views. When microservices share the same DB, app may rely on views across multiple schemas to serve cross-microservice queries In the context of microservices, data replication across various functional silos is common. This approach avoids synchronous requests that span multiple microservices for data retrieval and subsequent application-level aggregation. This practice lacks ordering guarantees for updates to different objects. When data arrives from different microservices, it is often aggregated in queries without any consistency guarantee, meaning it does not reflect a snapshot of the entire system at a single point in time