BestHub
Sign in
Tagged articles
23 articles
Page 1 of 1
Manbang Technology Team
Manbang Technology Team
Jan 10, 2019 · Backend Development

Mastering Apache Storm: Architecture, Components, and Real‑Time Processing Essentials

This article provides an in‑depth technical overview of Apache Storm, covering its core architecture, key components such as Nimbus, Supervisor, Worker, Executor, and Task, the role of ZooKeeper, high‑availability setup, API interfaces, code examples, grouping strategies, metrics, back‑pressure handling, and essential configuration parameters for building low‑latency stream processing topologies.

Apache StormBack-pressureBolt
0 likes · 12 min read
Mastering Apache Storm: Architecture, Components, and Real‑Time Processing Essentials
Tencent Cloud Developer
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 StormBig DataFlink
0 likes · 30 min read
Real-Time Stream Computing: Concepts, Challenges, and Tencent Cloud Solutions
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Apr 17, 2018 · Big Data

How a Big Data Platform Powers Real‑Time Facial Recognition for Billion‑Scale Face Libraries

This case study details how Beijing 恒远华信息技术有限公司 built a dynamic face‑capture and real‑time recognition solution on Huawei FusionInsight HD, leveraging deep‑learning algorithms, distributed storage, and stream processing to handle hundreds of millions of faces with high speed, efficiency, and security.

Apache StormHBaseHuawei FusionInsight
0 likes · 17 min read
How a Big Data Platform Powers Real‑Time Facial Recognition for Billion‑Scale Face Libraries
Meituan Technology Team
Meituan Technology Team
Dec 28, 2017 · Big Data

Design and Implementation of a Scalable Scenario Query System for Meituan

Meituan built a scalable scenario‑query platform that unifies traffic, activity and investment data by layering RPC services, a Storm‑driven pre‑computation tree stored in Redis/Tair, and a middle‑platform API with circuit‑breaker logic, cutting response times from seconds to under one second while dramatically reducing code coupling and simplifying future feature development.

Apache StormBig DataNoSQL
0 likes · 12 min read
Design and Implementation of a Scalable Scenario Query System for Meituan
Meituan Technology Team
Meituan Technology Team
Nov 16, 2017 · Big Data

Performance Comparison of Apache Flink and Apache Storm for Real-Time Stream Processing

The study benchmarks Apache Flink against Apache Storm on a shared cluster, showing Flink delivering three‑to‑five times higher throughput and roughly half the latency across simple, sleep‑induced, and windowed workloads, with modest throughput loss for exactly‑once semantics, leading to a recommendation of Flink for high‑performance, stateful real‑time stream processing.

Apache FlinkApache StormExactly-Once
0 likes · 19 min read
Performance Comparison of Apache Flink and Apache Storm for Real-Time Stream Processing
dbaplus Community
dbaplus Community
Oct 30, 2017 · Big Data

How to Build a Real‑Time Spam Monitoring System with Apache Storm

This article walks through the design, deployment, and code implementation of a real‑time spam detection pipeline using Apache Storm, comparing it with Hadoop, detailing cluster setup, topology components, data flow, and how to package and run the solution on a distributed Storm cluster.

Apache StormBig DataHibernate
0 likes · 13 min read
How to Build a Real‑Time Spam Monitoring System with Apache Storm
dbaplus Community
dbaplus Community
Jul 10, 2017 · Big Data

Master Apache Storm: Real‑Time Stream Processing from Basics to Word‑Count and Call‑Log Examples

This tutorial explains Apache Storm’s core principles, architecture, and development workflow, covering its relationship with Hadoop, key concepts such as spouts, bolts, tuples, and topologies, and provides step‑by‑step code examples for a word‑count program and a call‑log analysis application.

Apache StormBig DataReal-time Processing
0 likes · 14 min read
Master Apache Storm: Real‑Time Stream Processing from Basics to Word‑Count and Call‑Log Examples
Java High-Performance Architecture
Java High-Performance Architecture
Jun 29, 2017 · Big Data

Master Apache Storm: Core Concepts, Real‑Time Word Count & Call Log Analytics

This tutorial introduces Apache Storm’s fundamental principles and development workflow, providing a PDF guide and source code for two practical examples—real‑time word‑count and call‑record aggregation—while covering its definition, use cases, relationship with Hadoop, core concepts, cluster architecture, and step‑by‑step usage.

Apache StormBig DataReal-time Processing
0 likes · 1 min read
Master Apache Storm: Core Concepts, Real‑Time Word Count & Call Log Analytics
Architect
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 StormBig DataCustom Stream ID
0 likes · 8 min read
Understanding Custom Stream IDs and Topology Building in Apache Storm
Architect
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 StormDistributed Systemsjava
0 likes · 9 min read
Using Multiple Streams and Groups in Apache Storm Topology
Architect
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 StormBig DataReal-time analytics
0 likes · 19 min read
Understanding Apache Storm Architecture, Stream Groupings, and the Acker Mechanism
Qunar Tech Salon
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 StormKafkaReal‑Time Computing
0 likes · 8 min read
Real-Time Computing with Apache Storm: Architecture, Code Samples, and Fault Tolerance
21CTO
21CTO
Nov 23, 2015 · Big Data

How Dianping Scales Real‑Time Analytics with Apache Storm

This article explains how Dianping built a millisecond‑level real‑time computation platform using Apache Storm, covering use cases, system architecture, core Storm concepts, performance tuning, best practices, and a detailed Q&A on their production deployment.

Apache StormBig DataReal-time analytics
0 likes · 23 min read
How Dianping Scales Real‑Time Analytics with Apache Storm
21CTO
21CTO
Sep 24, 2015 · Big Data

Comparing Apache Storm, Spark, and Samza: Which Real‑Time Stream Processor Fits Your Needs?

Apache Storm, Spark Streaming, and Samza are three open‑source, low‑latency, scalable distributed systems for real‑time data processing; this article outlines their architectures, key concepts, differences in data handling, state management, delivery guarantees, and typical use‑cases to help you choose the right framework.

Apache SamzaApache StormBig Data
0 likes · 7 min read
Comparing Apache Storm, Spark, and Samza: Which Real‑Time Stream Processor Fits Your Needs?
Art of Distributed System Architecture Design
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 StormBig DataComparison
0 likes · 8 min read
Comparative Overview of Apache Storm, Spark Streaming, and Samza for Real-Time Data Processing
Art of Distributed System Architecture Design
Art of Distributed System Architecture Design
Jun 19, 2015 · Big Data

Storm vs Spark: Which Real‑Time Analytics Platform Wins for Your Business?

The article compares Apache Storm and Apache Spark, examining their origins, architecture, language support, integration capabilities, and performance characteristics, and offers guidance on selecting the right platform for real‑time business intelligence based on specific workload and infrastructure needs.

Apache SparkApache StormBig Data
0 likes · 11 min read
Storm vs Spark: Which Real‑Time Analytics Platform Wins for Your Business?
High Availability Architecture
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 StormBig DataReal‑Time Computing
0 likes · 21 min read
Real-Time Computing at Dianping: Architecture, Use Cases, and Best Practices