I. Introduction to Flink
Nowadays, the application scenarios of big data technology have higher and higher requirements for real-time performance. As a new generation of big data stream processing framework, Flink is unique due to its excellent real-time performance, which has aroused great interest and concern in the industry in recent years. Flink provides millisecond latency, low latency, high throughput and correctness of data processing, rich time type and window computation, exact-once semantic support, state management, and CEP (complex event processing) support. Flink’s strength in real-time analytics has led more and more companies to migrate real-time projects to Flink, and its community is growing rapidly.
At present, Flink has become the focus of the real-time field of major companies, especially the domestic ali as a representative of a large factory, are fully invested, many companies for the Flink community contributed a lot of source code. Flink is now considered by many to be the direction and future of real-time processing of big data, and many companies are recruiting and stocking up on Flink.
Shang Xuetang carefully created the Flink theory and project practice course, put equal emphasis on Flink theory and e-commerce data analysis project practice, systematically combed and elaborated the basic theoretical knowledge of Flink, and practiced the specific project of e-commerce user behavior analysis with multiple indicators. It provides the best learning platform for engineers who are interested in increasing their experience in big data projects and expanding their knowledge of streaming processing framework.
Second, Flink architecture
Apache Flink is a framework and distributed processing engine for stateful computation of unbounded and bounded data streams. Flink is designed to run in all common clustered environments, performing computations at memory speed and on any scale. Here we explain important aspects of the Flink architecture.
Processing unbounded and bounded data Any type of data is generated as a stream of events. Credit card transactions, sensor measurements, machine logs or user interactions on websites or mobile apps, all of these data are generated as streams. Data can be treated as an unbounded or bounded stream.
An unbounded flow has a beginning but no defined end. They do not terminate and provide data at generation time. Unbounded flows must be processed continuously, that is, events must be processed immediately after ingestion. There is no way to wait for all the input data to arrive because the input is unbounded and will not complete at any point in time. Processing unbounded data typically requires ingesting events in a specific order, such as the order in which they occurred, so that result integrity can be inferred.
A bounded flow has a defined beginning and end. A bounded stream can be processed by ingesting all the data before performing any calculations. Processing bounded streams does not require ordered ingestion because the bounded data set can always be sorted. The processing of bounded streams is also called batch processing.
Apache Flink specializes in working with unbounded and bounded data sets. Precise control of time and state enables Flink’s runtime to run any type of application on an unbounded flow. Bounded flows are handled internally by algorithms and data structures designed specifically for fixed-size data sets, resulting in excellent performance.
Three, Flink learning courses
This course is mainly divided into two parts: Flink theoretical basis and flink-based e-commerce user behavior analysis project practice.
The first part is mainly the explanation of the basic theory of Flink, involving a variety of important concepts, principles and API usage, and there will be a lot of example code implementation;
The second part, with e-commerce as the business application scenario, Flink as the analysis framework, introduces the development of an e-commerce user behavior analysis project.
Through the close combination of theory and practice, students can have a full understanding and understanding of Flink, and have a deeper understanding of the application scenarios of Flink and streaming processing, as well as the business field of e-commerce analysis in the actual project practice. In addition, by learning the principle of flow processing and comparing with batch processing architecture, we can have a more comprehensive understanding of big data processing architecture, and lay a foundation for future development as an architect.
Who is suitable to learn Flink
- Programmers with some background in Java and Scala who want to understand the new direction of big data
- Developers with Java and Scala development experience, knowledge of big data, and want to increase project experience
- Job seekers who have a good foundation in big data and want to master Flink and streaming processing framework
5. Flink learning route
Theory + practice project integration learning, if you need Flink learning video, please click here: Shang Xuetang Big data Flink video
The learning course is divided into two parts: The first part: Flink theory foundation the second part: e-commerce user behavior analysis project