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

Big data's era has come, the explosive growth of information makes more and more industries face the challenge of storing and analyzing this large amount of data. As an open source distributed parallel processing platform, Hadoop is becoming more and more popular because of its high expansion, high efficiency, high reliability and so on. This also led to the release of the commercial version of hadoop. Here, through Express DKhadoop, we will introduce the architecture of hadoop big data platform in detail.

At present, there are commercial distributions of hadoop in China, such as Huawei Cloud, in addition to Express DKhadoop. Although the publisher is different, but the platform architecture is similar, here I am familiar with the dkhadoop to introduce.

1. Daxuai Dkhadoop, which can be said to integrate all the components of the whole HADOOP ecosystem, has been deeply optimized and recompiled into a complete and higher-performance big data general computing platform, realizing the organic coordination of various components. Therefore, compared with the open source big data platform, DKH has a very high improvement in computing performance. This is also a personal feeling that dkhadoop is better than another commercial release I used before. it can be said that most domestic commercial distributions hadoop are secondary packaging, and what dkhadoop does well is to dare to develop on the basis of the original ecology.

2. Fast DKhadoop middleware technology simplifies the configuration of big data cluster into three nodes, which not only simplifies the management, operation and maintenance of the cluster, but also enhances the availability and stability of the cluster. Dkhadoop middleware integrates many components of apache, including support for file, SQL, log, message, crawler, stream data and heterogeneous data; integrates fast compression algorithm, and data synchronous distribution technology, realizes data import and reduction transfer at the same time, and has irreplaceable technical advantages for projects with real-time data requirements.

3. The commercial distribution of DKhadoop maintains the advantages of open source systems and is 100% compatible with open source systems. For those big data applications developed on the open source platform, they can run efficiently on dkhadoop without modification.

4. DKhadoop integrated development framework provides more than 20 classes commonly used in big data, search, natural language processing and artificial intelligence development, totaling more than 100 methods, which greatly improves the development efficiency. DK.HADOOP integrates NOSQL database and simplifies the programming between file system and non-relational database; DK.HADOOP improves the cluster synchronization system and makes the data processing of HADOOP more efficient.

5. The SQL version of DKhadoop also provides the integration of distributed MySQL. The traditional information system can be seamlessly realized for big data and distributed leapfrogging.

6. ES: the search system of Express DKhadoop is redeveloped on the open source ES system and supports completed full-text search. With the integration of effective support for Chinese search and support for fast data synchronization technology, DK.ES is one of the core components of DKH. Only with the integration of DKH with effective support for Chinese search and support for fast data synchronization technology, DK.ES is one of the core components of DKhadoop.

7. Chinese language processing component: fast Chinese language processing is the most widely used open source natural language processing package in China.

Simply introduce these, want to know more about it, you can search the query or download the dkhadoop learning version to understand. Here are the questions about the dkhadoop version:

DKH standard DKH- distributed SQL DK.HADOOP distribution

DKH Standard Edition has three different sub-versions: a stand-alone version for development and debugging; a learning version that supports three nodes; and a standard server version that supports more than 5 nodes.

There are two sub-versions of DKH- distributed SQL version: learning version and server version

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