Data Stream Processing

Research on distributed data stream clustering algorithm based on Hadoop mapreduce Cai Binlei Ningjiadong Zhu Shiwei Guo Qin with the continuous increase of data flow scale, the existing clustering algorithm based on grid has no effect on the clustering of data streams, can not find any shape clusters in real time, and can not delete the noise points in the data stream in time. This paper presents a distributed data stream clustering algorithm (Pgdc-stream) based on grid density in Hadoop platform environment, which facilitates the parallel clustering of data flow in MapReduce framework based on Hadoop.

In recent years, with the emergence of new forms of information, represented by social networking sites, location-based services, and the rapid development of cloud computing, mobile and IoT technologies, ubiquitous mobile, wireless sensors and other devices are generating data at all times, Hundreds of millions of users of Internet services are always generating data interaction, the big Data era has come. In the present, large data is hot, whether it is business or individuals are talking about or engaged in large data-related topics and business, we create large data is also surrounded by the big data age. Although the market prospect of big data makes people ...

Depending on the use scenario, large data processing is gradually evolving to two extremes-batch processing and streaming. The streaming processing pays more attention to the real-time analysis of the data, and represents the storm and S4 of the tools. 　　and batch processing is more focused on the long-term data mining, the typical tool is derived from the three major Google paper Hadoop. With the "bursting" of data, companies are racking their brains over large data processing, with the aim of being faster and more accurate. However, the recent new Open-source tool Summingbird has broken the rhythm of ...

Ufida UAP Data platform has the ability of large data processing and analysis, it mainly relies on unstructured data processing platform Udh (UAP distribute for Hadoop) to complete. 　　UDH includes Distributed file system, storage database, distributed analysis and computing framework for Distributed batch processing, real-time analysis query, stream processing and distributed batch processing based on memory, and distributed data mining. In today's big data, companies can not blindly follow, but should understand why big data is so hot, why pay attention to it. Its ...

To understand the concept of large data, first from the "Big", "big" refers to the scale of data, large data generally refers to the size of the 10TB (1TB=1024GB) data volume above. Large data is different from the massive data in the past, and its basic characteristics can be summed up with 4 V (Vol-ume, produced, and #118alue和Veloc-ity), that is, large volume, diversity, low value density and fast speed. Large data features first, the volume of data is huge. Jump from TB level to PB level. Second, the data types are numerous, as mentioned above ...

Large data areas of processing, my own contact time is not long, formal projects are still in development, by the large data processing attraction, so there is the idea of writing articles. Large data is presented in the form of database technologies such as Hadoop and "NO SQL", Mongo and Cassandra. Real-time analysis of data is now likely to be easier. Now the transformation of the cluster will be more and more reliable, can be completed within 20 minutes. Because we support it with a table? But these are just some of the newer, untapped advantages and ...

As we all know, Java in the processing of data is relatively large, loading into memory will inevitably lead to memory overflow, while in some http://www.aliyun.com/zixun/aggregation/14345.html "> 　　Data processing we have to deal with massive data, in doing data processing, our common means is decomposition, compression, parallel, temporary files and other methods; For example, we want to export data from a database, no matter what the database, to a file, usually Excel or ...

According to Gigaom, Facebook and Yahoo! released some details of their live multi-system real-time data flow management tools last week. In this one, Storm-YARN, announced by Yahoo! and open source, is based on YARN (Hadoop 2.0) and Storm, bringing a much tighter set of Storm and Hadoop clusters - borrowing Hadoop batch clusters even from Storm when needed Ability. Wormhole Integrated Surveillance System, Ability to Support Capacity Planning, Automated Repair, ...

When Hadoop enters the enterprise, it must face the problem of how to address and respond to the traditional and mature it information architecture. 　　In the industry, how to deal with the original structured data is a difficult problem for enterprises to enter large data field. When Hadoop enters the enterprise, it must face the problem of how to address and respond to the traditional and mature it information architecture. In the past, MapReduce was mainly used to solve unstructured data such as log file analysis, Internet click Stream, Internet index, machine learning, financial analysis, scientific simulation, image storage and matrix calculation. But ...

1 Hadoop pipeline improvement in the implementation of the Hadoop system, the output data of the map end is written to the local disk first, and the Jobtracker is notified when the native task is completed, and then the reduce end sends an HTTP request after receiving the Jobtracker notification. Pull back the output from the corresponding map end using the Copy method. 　　This can only wait for the map task to complete before the reduce task begins, and the execution of the map task and the reduce task is detached. Our improvement ...