Ganglia Configuration

Back-end development work related to big data for more than a year, with the development of the Hadoop community, and constantly trying new things, this article focuses on the next Ambari, the new http://www.aliyun.com/zixun/aggregation/ 　　14417.html ">apache project, designed to facilitate rapid configuration and deployment of Hadoop ecosystem-related components of the environment, and provide maintenance and monitoring capabilities. As a novice, I ...

Preface Having been in contact with Hadoop for two years, I encountered a lot of problems during that time, including both classic NameNode and JobTracker memory overflow problems, as well as HDFS small file storage issues, both task scheduling and MapReduce performance issues. Some problems are Hadoop's own shortcomings (short board), while others are not used properly. In the process of solving the problem, sometimes need to turn the source code, and sometimes to colleagues, friends, encounter ...

Flume-based Log collection system (i) architecture and Design Issues Guide: 1. Flume-ng and scribe contrast, flume-ng advantage in where? 2. What questions should be considered in architecture design? 3.Agent crash how to solve? Does 4.Collector crash affect? What are the 5.flume-ng reliability (reliability) measures? The log collection system in the United States is responsible for the collection of all business logs from the United States Regiment and to the Hadoop platform respectively ...

Several articles in the series cover the deployment of Hadoop, distributed storage and computing systems, and Hadoop clusters, the Zookeeper cluster, and HBase distributed deployments. When the number of Hadoop clusters reaches 1000+, the cluster's own information will increase dramatically. Apache developed an open source data collection and analysis system, Chhuwa, to process Hadoop cluster data. Chukwa has several very attractive features: it has a clear architecture and is easy to deploy; it has a wide range of data types to be collected and is scalable; and ...

Currently, the Hadoop distribution has an open source version of Apache and a Hortonworks distribution (HDP Hadoop), MapR Hadoop, and so on. All of these distributions are based on Apache Hadoop.

Dell 25th announced the launch of the Dell Hadoop solution based on the Intel Hadoop release, Dell PowerEdge Cloud Server and network architecture to further strengthen its next generation computing solution to provide customers with one-stop large data solutions. Dell Hadoop solution provides customers with optimized hardware and software configuration recommendations, simple and fast implementation of deployment services, and overall professional service support to ensure high availability and stability of the enterprise Hadoop environment. The collaboration between Dell and Intel has pushed the development of the big data age. Close to Customer soft ...

The most important reason to choose Hadoop is that three points: 1, can solve the problem, 2, low cost, 3, mature ecological circle. 　　One, Hadoop helps us solve what problems both domestic and foreign large companies have an insatiable thirst for data, and will do everything they can to collect all the data, because the asymmetry of information is constantly being made available, and a great deal of information can be obtained through data analysis. The source of the data is very much, the data format is more and more complex, over time data ...

In today's technology world, big Data is a popular it buzzword. To mitigate the complexity of processing large amounts of data, Apache developed a reliable, scalable, distributed computing framework for hadoop--. Hadoop is especially good for large data processing tasks, and it can leverage its distributed file systems, reliably and cheaply, to replicate data blocks to nodes in the cluster, enabling data to be processed on the local machine. Anoop Kumar explains the techniques needed to handle large data using Hadoop in 10 ways. For from HD ...

In today's technology world, big Data is a popular it buzzword. To mitigate the complexity of processing large amounts of data, Apache developed a reliable, scalable, distributed computing framework for hadoop--. Hadoop is especially good for large data processing tasks, and it can leverage its distributed file systems, reliably and cheaply, to replicate data blocks to nodes in the cluster, enabling data to be processed on the local machine. 　　Anoop Kumar explains the techniques needed to handle large data using Hadoop in 10 ways. For the ...