Shulou(Shulou.com)06/03 Report--

With the continuous increase of the data scale of the Internet, higher requirements are put forward for the file storage system, which requires a file storage system with larger capacity, better performance and higher security. like the traditional distributed file system, the HDFS distributed file system is also connected to the node through the computer network, but it also has the advantage over the traditional distributed file system.

1. Support for very large files

HDFS distributed file system has a large data set, which can store super-large data files at TB or PB level, and can provide high data transmission bandwidth and data access throughput. Accordingly, HDFS opens some necessary interfaces of POSIX to allow streaming access to the data of the file system.

two。 High fault tolerance

HDFS is aimed at hundreds of server clusters, each server stores part of the data of the file system. In the cluster environment, hardware failure is a common problem, which means that there is always a part of hardware can not work for various reasons. Therefore, error detection and fast and automatic recovery are the core architectural goals of HDFS. Therefore, HDFS has a high degree of fault tolerance.

3. High data throughput

HDFS uses a simple data consistency model of "write once, read multiple times". In HDFS, once a file is created, written, and closed, it generally does not need to be modified. Such a simple consistency model helps to improve throughput.

4. Streaming data access

The data processing scale of HDFS is relatively large, and applications need to access a large amount of data at one time. At the same time, these applications are generally batch processing, rather than user interactive processing, and applications can access data sets in the form of streams.

Hadoop has rapidly grown into the preferred big data analysis solution for unstructured data. HDFS distributed file system is one of the core components of Hadoop, which ensures the reliable storage of big data. When used with MapReduce, you can quickly and reliably analyze structured and complex big data, thus providing strong support for enterprises to make better decisions, promote revenue growth, improve services, and reduce costs!

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