Hadoop Hdfs

Hadoop Hdfs Introduction:

Hadoop File System was developed using distributed file system design. It is run on commodity hardware. Unlike other distributed systems, HDFS is highly fault tolerant and designed using low-cost hardware. HDFS holds very large amount of data and provides easier access. To store such huge data, the files are stored across multiple machines. These files are stored in redundant fashion to rescue the system from possible data losses in case of failure. HDFS also makes applications available to parallel processing.

Overview Of Hadoop Hdfs Job Support:

Hadoop Distributed File System is an primary storage system used for the Hadoop.  It is an key tool for managing Big Data & supporting  the analytic applications in an scalable, cheap &  rapid way. Hadoop is usually used on the low-cost commodity machines, where server failures are fairly common.

To accommodate an high failure environment the file system is designed to distribute data throughout the  different servers in different server racks making the data highly available. Moreover, when the  HDFS takes in data it breaks it down into smaller blocks that get assigned to the different nodes in an cluster which allows for parallel processing, increasing the speed in which the data is managed.

Based on google file systems it provides redundant storage of massive amounts of data by using commodity hardware. The data is distributed across all nodes at the load time. It also provides efficient map reduce processing. The HDFS runs on the commodity hardware-it assumes high failure rates of the components. It works well with lots of large files. It builds around the idea of write once & ready many times – it means no random access. It follows that high throughput is more important than low latency

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