Shulou(Shulou.com)06/01 Report--

In this issue, Xiaobian will bring you about how to carry out in-depth analysis of Flink principle and architecture. The article is rich in content and analyzed and described from a professional perspective. After reading this article, I hope you can gain something.

Nowadays, more and more enterprises have high requirements for real-time data. Take e-commerce as an example, Ali will erect an electronic screen on Double 11 to display Taobao data in real time, such as turnover, number of visitors, order volume, turnover volume, etc. Behind this large electronic screen is the real-time processing technology that uses what we call data. Of course, real-time calculation is not completely real-time, it must have a delay, but this delay is very short. What is real-time data processing? My personal understanding of real-time data processing is:

This process line down, processing data speed in seconds or even milliseconds.

At this stage, real-time processing is mainly using Flink technology!

Ali, Tencent, Baidu, ByteDance, Didi, Huawei and many other Internet companies have taken Flink as an important driving point for future technology. In the next 3 ~ 5 years, Flink will definitely develop into the mainstream data processing framework within the enterprise and become the "stepping stone" for developers to enter the big factory.

At present, a Flink R & D engineer with 3 to 5 years of experience generally earns around 30K, and if you are a core developer in the company's big data real-time computing field, the salary will be even higher. But what would you do if you encountered these three Flink questions in a big factory interview? How does Flink implement Exactly-once semantics? Flink classification of time types and their respective implementation principles? How does Flink handle data disorder and latency? Obviously, it was impossible to answer without in-depth study and practical application. The key to learning Flink real-time processing technology lies in whether it can be exposed to the real data environment and whether there are ready-made architectures and cases that can be learned. Architecture design, capacity planning, and performance tuning in big data should be considered comprehensively with specific business requirements, so it is difficult to obtain this part of experience, which cannot be achieved by self-study alone. The above is how to carry out Flink principle and architecture in-depth analysis shared by Xiaobian for everyone. If there are similar doubts, please refer to the above analysis for understanding. If you want to know more about it, please pay attention to the industry information channel.

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