Author: Zen and the Art of Computer Programming
With the continuous development of new network technologies such as the Internet, mobile Internet, and the Internet of Things, enterprises are increasingly dependent on the processing of massive data, and fields such as big data analysis, decision support, and risk control all require massive data processing capabilities. How to process massive data efficiently and quickly, improve processing efficiency, and reduce costs is one of the key technologies for processing large-scale and complex data sets. In terms of big data platform architecture, Apache Hadoop has become the de facto "king", but the parallel computing model of Hadoop MapReduce is too low-level and cannot meet the needs of complex and changeable real-time analysis scenarios; Spark has become even more popular, but Spark is not suitable for analysis tasks. It takes up too many resources, is slow and error-prone; Apache Storm and Samza based on the stream processing framework also have excellent real-time computing characteristics, but they are all batch processing frameworks and can only be used for offline computing or some simple real-time computing. Task. Therefore, in view of the characteristics and limitations of various current big data platforms, coupled with the vigorous development of the open source community in recent years, Apache Flink based on the stream processing framework came into being. What is Flink? It is an open source distributed stream processing framework with features such as high throughput (Throughput), low latency (Latency), Exactly Once and Fault-Tolerance (fault tolerance), and can be used to perform high throughput on real-time and offline data. , low latency, accurate once calculation and analysis. Its key innovations are:
Data processing model and programming interface: Flink provides a wealth of data processing models, including DataStream API, DataSet API, Table API, SQL, etc., supports Java/Scala/Python/R language programming, and also provides corresponding IDE plug-in support to facilitate development ;
Pipeline architecture: Flink adopts a pipeline architecture to divide the data flow into multiple stages for parallel processing, achieving a relatively