What Is Stream Processing?

Stream processing is an important method for big data processing. Its main feature is that the data it processes is continuous and arrives in real time. Distributed stream processing is a fine-grained processing mode for dynamic data. Based on distributed memory, it processes continuous dynamic data. Its fast, efficient, and low-latency characteristics for data processing play an increasingly important role in big data processing.

Distributed stream processing refers to

In data stream processing systems, there are many factors that affect the rate of data stream processing. According to the characteristics of these influencing factors, they can be divided into the following two categories:

The logical task of the task itself. That is, for each piece of incoming data, the processing logic will execute different processing logic according to the different data content, so the data processing rate will be different. Such factors have nothing to do with streaming data processing systems, and

Distributed computing is also translated as decentralized computing. This research area mainly studies how distributed systems perform calculations. A distributed system is a system formed by a group of computers that communicate and communicate with each other through a network and coordinate their behavior. Components interact with each other to achieve a common goal. The engineering data that requires a lot of calculations is partitioned into small blocks, which are separately calculated by multiple computers. After uploading the calculation results, the results are unified and the science of data conclusions is unified. Examples of distributed systems come from different service-oriented architectures, massively multiplayer online games, and peer-to-peer network applications. Distributed computing is a new way of computing. The so-called distributed computing is to share information between two or more softwares, which can run on the same computer or on multiple computers connected through a network. Compared with other algorithms, distributed computing has the following advantages:

1. Rare resources can be shared.

2. Through distributed computing, the computing load can be balanced on multiple computers.

3. You can put the program on the computer most suitable for running it.

Among them, sharing scarce resources and balancing loads are one of the core ideas of computer distributed computing.