ByteByteGo·3yEP76: Netflix's Tech Stack
In this CEO-approved slide deck, you’ll find simple ways to communicate how your team is increasing engineering efficiency. For too many engineering leaders, the most stressful part of their job isn’t a bug or a system crash. The CTO Board Deck is your secret weapon for owning any boardroom you enter.
freeCodeCamp·3yHow to Build a Real-Time Notification System with Go and Kafka
In this tutorial, you’ll delve into the mechanics of Kafka and then integrate it with Go to develop a real-time notification system. In order to understand this article fully, you should have prior knowledge of Goroutines, the Gin framework, and containerization tools like Docker.
ByteByteGo·3yHow to Choose a Message Queue?
The diagram below shows how it works: When the cashier takes an order, a new order is created in the database-backed queue. For Starbucks’ use case, a simple database queue meets the requirements without needing Kafka. RabbitMQ works like a messaging middleware - it pushes messages to consumers then deletes them upon acknowledgment.
Community Picks·3yRedis vs Kafka vs RabbitMQ
There are two common ways Microservices communicate with each other: Synchronous and Asynchronous. RabbitMQ was released in 2007 and is one of the first common message brokers to be created. RabbitMQ is an open-source that delivers messages through both point-to-point and pub-sub methods.
Earthly·3yKafka vs RabbitMQ: What Are the Differences?
Apache Kafka and RabbitMQ are popular distributed messaging systems with functionalities for real-time data processing, analytics, event-driven architectures, and more. Kafka is a distributed streaming platform with high throughput, low latency, and a publish-subscribe model. RabbitMQ is a message broker with support for multiple protocols and flexible routing capabilities. Kafka excels in real-time data processing, event sourcing, and log aggregation, while RabbitMQ is suitable for asynchronous communication, microservices communication, and IoT data ingestion.
ByteByteGo·3yWhy is Kafka so fast? How does it work?
Kafka has emerged as a popular choice for its speed and scalability - but what exactly makes it so fast? In this issue, we'll explore: Kafka's architecture and its core components like producer, brokers, and consumers. How Kafka optimizes data storage and replication and replication.
JavaScript in Plain English·3yRedis vs Kafka vs RabbitMQ
Redis vs Kafka vs RabbitMQ will compare the three most popular brokers: RabbitMQ, Kafka and Redis. RabbitMQ supports all major languages, including Python, Java,.NET, PHP, Ruby, JavaScript, Go, Swift, and more. Kafka Scale: can send up to a million messages per second.
Amplication·3yBuilding Real-time Applications with Amplication and Kafka
This article discusses the importance of real-time communication in modern applications and introduces Kafka as a tool for building real-time systems. It explains the components of Kafka and how it works on a publish-subscribe model. The article then demonstrates how to build an application with Kafka using Amplication, a platform that generates production-ready backend services. It provides step-by-step instructions for setting up an Amplication project, creating entities, adding a message broker and topics, configuring microservices, and adding custom Kafka communication logic. The article also highlights the benefits of using Amplication to speed up microservice development and ensure seamless event-driven communication.
Towards Dev·3yEvent Streaming v.s Event Sourcing
Event streaming and event sourcing are two related but distinct concepts in the world of event-driven architecture. Event streaming is the process of continuously capturing and storing events as they occur in a system. These events can then be processed and analyzed in real-time or stored for later analysis.
Community Picks·3yKafka Architecture
This post provides an in-depth explanation of Kafka architecture, covering topics such as brokers, producers, consumers, partitions, clusters, topics, offsets, replication factor, leader-follower relationship, consumer groups, Kafka as a streaming technology, and more. It also includes sample code for a Python producer and consumer.
Discover .NET·3yBiggest scam in software dev? Best Practices.
CodeOpinion Sponsor: Do you build complex software systems? See how NServiceBus makes it easier to design, build, and manage software systems that use message queues to achieve loose coupling. Get started for free. What’s the biggest scam in tech that is deemed acceptable?
Cloud Native Daily·3y10 Tools for Scaling Microservices
Helios Helios is an observability tool that plays a crucial role in scaling microservices. It provides real-time visibility and insights into the performance and health of the system. Helios supports distributed tracing to help you understand the flow of requests across different microservices in your application.
Redis·3yFive New Official Redis Clients
Redis now officially supports five open-source client libraries: More are on the way. Choosing one of our official Redis clients means prioritizing your development experience. We will provide a standard interface across all Redis flavors, whether you use Redis Open Source (Redis OSS) or Redis Enterprise Cloud.
Towards Data Science·2ySystem Design Series: The Ultimate Guide for Building High-Performance Data Streaming Systems from Scratch!
This article discusses the process of building high-performance data streaming systems for recommendation services. It explains the need for data streaming pipelines, the challenges of data ingestion, the importance of using Apache Kafka, the steps involved in data processing, the choice of using Cassandra as a data store, and the process of querying recommendations.
Dev Genius·2yKafka Multiple Partitions with Spring Boot and Kubernetes
This article discusses the concepts of Kafka partitions, scalability, fault tolerance, messaging ordering, and consumer parallelism. It also provides instructions on deploying Kafka, Spring Boot applications, and Kafdrop on Kubernetes. Additionally, it demonstrates how to set up a Spring Boot producer and consumer application for Kafka.
Quastor Daily·3yHow PayPal Scaled Kafka to 1.3 Trillion Messages a Day
How PayPal Scaled Kafka to 1.3 Trillion Messages a Day, how Ryan Peterman went from an L3 at Facebook to an L6 in under 3 years (tripling his compensation) How PayPal improved Kafka cluster management with a config service, custom libraries, access control lists and more Collecting metrics on the Kafka cluster.
Trendyol Tech·3yPerformance of Kafka Consumers: 1 Billion messages
Python and Go users can create a consumer in Python and Go. Consumers will search in Topic with the specific key I provided. They will create threads equal to the partition count of the Topic. They will run as parallelly. For instance, a topic has ten partitions, then I will create ten threads.
Quastor Daily·3yHow Airbnb Built a Low Latency, Highly Scalable Key Value Store
Airbnb built a highly scalable key value store that they could use to serve data for their ML applications. The Architecture of Airbnb’s Distributed Key Value Store was sponsored by Spotify. We talk about building a culture around automated testing and strategies Spotify uses for large scale migrations.
