Author: Zen and the Art of Computer Programming
1 Introduction
Apache Flink is an open source distributed stream processing platform developed by the Apache Software Foundation (ASF) and released in September 2015. Apache Flink supports multiple programming languages such as Java, Scala, Python, etc., and provides rich API interfaces to facilitate users in data processing. Flink's system architecture mainly includes: JobManager, TaskManager, Task, Slot, ResourceManager, JobGraph, Plan, DataSet API, etc. Its core is a highly fault-tolerant distributed operating environment, which ensures that streaming data is processed correctly in the cluster through carefully designed task scheduling strategies and resource management mechanisms. After solving many key issues in real-time computing, Flink's development team has been committed to improving its architecture, improving overall performance, and achieving more flexible, efficient, and reliable stream processing capabilities.
As an open source distributed stream processing framework, Flink has achieved very successful results in the past few years. As the demand for cloud computing and large-scale data becomes more and more urgent, stream processing technology is becoming more and more important. As a stream processing platform, Flink, in order to meet the needs of real-time processing of massive data, has prompted its developers to make various attempts to explore how to perform fast and efficient real-time data processing in complex distributed operating environments. In this process, Flink provides a novel data stream-based processing model - Flink Stream Processing API, which allows developers to more easily define, debug, optimize and execute complex stream processing applications. In addition, it also supports the elasticity and fault tolerance of distributed computing, and can use Flink to streamline and incrementally process traditional Batch Processing, ultimately helping enterprises complete online analysis and machine learning work.
This article will share the lessons learned from Flink’s data processing revolution, as well as the latest progress of Flink’s Stream Processing API. We will first introduce the historical evolution of Flink, and then focus on Flink’s important position in the field of real-time computing.