Apache Storm | BestHub

Tencent Cloud Developer

Sep 6, 2018 · Big Data

Real-Time Stream Computing: Concepts, Challenges, and Tencent Cloud Solutions

As mobile and IoT data surge, real-time stream computing—especially Flink’s low-latency, high-throughput, exactly-once engine—addresses challenges of latency, accuracy, and usability, and Tencent Cloud’s managed Flink service provides elastic, secure, integrated pipelines for applications ranging from online status monitoring to fraud detection and smart transportation.

Apache StormCloud ServicesFlink

0 likes · 30 min read

Real-Time Stream Computing: Concepts, Challenges, and Tencent Cloud Solutions

Architecture Digest

Dec 28, 2017 · Big Data

Storm Component Relationships, Parallelism Calculation, and Nimbus Task Assignment

This article explains the relationships among Storm's core components, how parallelism is calculated from configuration parameters, and how Nimbus performs task assignment and load balancing across workers, supervisors, and executors in a distributed streaming topology.

Apache StormNimbusParallelism

0 likes · 11 min read

Storm Component Relationships, Parallelism Calculation, and Nimbus Task Assignment

Architect

Jul 14, 2016 · Backend Development

Using Multiple Streams and Groups in Apache Storm Topology

This article explains how to declare and emit multiple stream IDs in Apache Storm, demonstrates code examples for MultiStream and MultiGroup patterns, discusses common pitfalls, and shows how to abstract stream declarations and bolt configurations for more flexible and dynamic topologies.

Apache StormStream Processingbackend development

0 likes · 9 min read

Using Multiple Streams and Groups in Apache Storm Topology

Architect

Jul 14, 2016 · Big Data

Understanding Custom Stream IDs and Topology Building in Apache Storm

This article explains how to construct Apache Storm topologies with custom stream IDs, demonstrates the classic WordCountTopology example, and provides detailed Java code snippets illustrating spout and bolt configurations, stream declarations, and grouping strategies for real‑time stream processing.

Apache StormCustom Stream IDStream Processing

0 likes · 8 min read

Understanding Custom Stream IDs and Topology Building in Apache Storm

Architect

Mar 29, 2016 · Big Data

Understanding Apache Storm Architecture, Stream Groupings, and the Acker Mechanism

This article provides a comprehensive overview of Apache Storm’s architecture, including the roles of Nimbus, Supervisor, and ZooKeeper, explains various stream groupings, details the Acker mechanism, and describes task execution, parallelism calculation, and internal data flow within the Storm cluster.

Apache StormStream Processingbig data

0 likes · 19 min read

Understanding Apache Storm Architecture, Stream Groupings, and the Acker Mechanism

Qunar Tech Salon

Dec 15, 2015 · Big Data

Real-Time Computing with Apache Storm: Architecture, Code Samples, and Fault Tolerance

This article explains the principles of real-time computing, compares it with offline batch processing, and demonstrates a practical solution using Kafka for ingestion, Apache Storm for continuous computation, and various storage options, while also covering streaming concepts and Storm's high‑availability mechanisms.

Apache StormStream Processingbig data

0 likes · 8 min read

Real-Time Computing with Apache Storm: Architecture, Code Samples, and Fault Tolerance

Art of Distributed System Architecture Design

Sep 24, 2015 · Big Data

Comparative Overview of Apache Storm, Spark Streaming, and Samza for Real-Time Data Processing

This article introduces Apache Storm, Spark Streaming, and Samza, explains their architectures, common features, key differences such as delivery guarantees and state management, and provides guidance on selecting the most suitable framework for various real‑time big‑data use cases.

Apache StormReal-time ProcessingSamza

0 likes · 8 min read

Comparative Overview of Apache Storm, Spark Streaming, and Samza for Real-Time Data Processing

High Availability Architecture

May 15, 2015 · Big Data

Real-Time Computing at Dianping: Architecture, Use Cases, and Best Practices

During a detailed live session, senior Dianping engineer Wang Xinchun explains the company's real‑time computing platform built on Apache Storm, covering use cases such as dashboards, search and recommendation, system architecture, data ingestion tools like Blackhole and Puma, performance tuning, monitoring, and practical best‑practice recommendations.

Apache StormStream Processingbig data

0 likes · 21 min read

Real-Time Computing at Dianping: Architecture, Use Cases, and Best Practices

Qunar Tech Salon

Mar 16, 2015 · Big Data

Comparison of Apache Storm, Spark Streaming, and Samza for Real‑Time Data Processing

This article introduces Apache Storm, Spark Streaming, and Apache Samza, outlines their architectures, highlights commonalities and differences such as delivery guarantees and state management, and offers guidance on selecting the most suitable framework for various real‑time big‑data use cases.

Apache SamzaApache StormReal-time Processing

0 likes · 8 min read

Comparison of Apache Storm, Spark Streaming, and Samza for Real‑Time Data Processing