Pora (Personal Offline Realtime Analyze) is a high‑throughput, low‑latency platform that captures user behavior in real time, enabling Alibaba’s search engine to deliver personalized results, support online learning, and run 24/7 with massive data volumes.
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.
This article explains how IBM Research leverages Spark to capture and analyze network traffic of microservice‑based applications in an OpenStack cloud, providing real‑time transaction tracing and batch latency statistics to reveal service dependencies and performance bottlenecks.
To monitor hundreds of web apps on UAE’s PaaS platform in near‑real time, we combined Storm with lightweight log transport, a memcached‑based fqueue, and adaptive cardinality counting to efficiently compute PV, UV, response times, and custom metrics while handling cross‑cluster log aggregation.
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.
The article explains how Storm complements batch‑oriented Hadoop by providing fault‑tolerant, low‑latency stream processing for massive, continuously generated data such as Twitter’s tweet firehose, and demonstrates its architecture, key features, and a simple MapReduce‑style code example.
