Author: Zen and the Art of Computer Programming
1 Introduction
With the development of new economic and social forms such as the Internet, mobile Internet, and the Internet of Things, massive amounts of data are constantly emerging. How to efficiently process massive data and conduct effective analysis has become one of the important issues facing the IT industry today. As for data processing frameworks, Apache Spark and Apache Flink are currently the most mainstream open source frameworks with rich data processing functions. Therefore, this article will compare Spark Streaming and Flink Stream SQL, start from the advantages and disadvantages of both, explain the differences between them, and look forward to their future development direction.
2. Basic concepts and terminology explanation
Apache Spark
Apache Spark is an open source big data cluster computing framework developed by AMPLab at the University of California, Berkeley. It provides many features such as high fault tolerance, ease of use, reliability, and high performance, and can be used for rapid iterative data processing. Spark is designed as a unified computing engine that can be used to support application scenarios such as batch processing, interactive querying, and machine learning. Spark has the following characteristics:
Parallel computing capability: Spark adopts a data-based parallel computing mechanism, which can cut complex tasks into multiple parallel threads and utilize all computing resources to achieve faster execution speed.
Ease of use: Spark provides API interfaces in multiple languages such as Python, Java, and Scala. Users can easily complete data processing through these interfaces.
Scalability: Spark supports dynamic resource allocation between clusters, allowing users to achieve elastic expansion and contraction of computing resources by adding or reducing nodes in the cluster.
HDFS support: Spark can use HDFS as a distributed file system and directly read or write data sets on HDFS.
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