Evolution of Stream Processing Systems Stream processing focuses on handling unbounded data streams in real time, delivering results with minimal latency. To understand Flink's role, we trace the evolution of data systems: Early Batch Era: Google's MapReduce (2003) and Apache Hadoop popularized larg...
Apache Spark and Apache Flink are both widely used big data processing frameworks, but they differ significantly in stream processing architectures. Spark uses Spark Streaming for micro-batch stream processing, while Flink is designed as a true stream processing engine with stream-batch unification...
Apache Storm is an open-source distributed computation system designed for processing real-time data streams. Often compared to Hadoop for batch processing, Storm excels in unbounded data scenarios where low latency is critical, such as real-time analytics, online machine learning, and continuous co...
Flink SQL Overview Table API and SQL represent the highest-level APIs within Flink. These two APIs are tightly integrated, with SQL operations being executed against Flink's Table abstraction. Consequently, they are often considered a unified layer. Flink provides a unified batch and stream processi...
Business Context and Challenges As the company expanded its overseas operations, an e-commerce platform was initially developed to serve international customers. Over time, this system accumulated significant user data. Later, with the launch of smart robot products, a mobile application was introdu...
Resource configuration is the foundational step in Flink performance tuning. Adequate resource allocation correlates directly with throughput capabilities. When submitting applications via YARN in per-job mode, resources are defined through command-line arguments or configuration files. Since Flink...