Killer Combo: Hadoop HDFS and MR
Apache Hadoop is a bundling of reliable shared storage Hadoop Distributed File System (HDFS) and speedy analysis schema MapReduce (MR) programming model. These first generation had paved the way for creditably longevity and also planted brilliantly its clever reflection in worldwide cluttered market. Theoretically, thousand CPU machine would cost a very large amount of money than 1,000 single-CPU. In economical and logical ways, Hadoop HDFS will able to tie at more reasonably price minicomputers in sync into one cost-effective cluster. Simplified programming scheme Hadoop MapReduce has provided the better approach for user to quick code in distributed system and test its efficiency in both automation and manual way across the network machines. Hence the constant nourishment was given to primitive Hadoop concept to combat with the rapid increase in nostalgia marketing.
Menace in novel Hadoop norm
Data is spanned over many hardware and software hubs. In Hadoop environmental setup, the chances of network complications are more. It is vital important to understand good deed and practical value in expanding business horizon. Establishing distributed filesystems become more conglomerate and laborious than normal disk filesystems.
Hadoop Distributed File System (HDFS) contain few to several thousands of server nodes. Big data set is chopped and distributed evenly across each node filesystem reserved areas. Each hardware machine has more probability of fault and failure over a period of time. Handling computer hardware and software components are complicated. Driver failure, node and network failure, disk failure occurs now and then. HDFS architectural design should able to automatically recovery from such failover and gracefully lead forward.
Processing data in distributed cluster environment always need insight deeper understanding to maximize technical knowledge base opening up the new doors for better and quick services. At the target of speeding up data transfer rate, some network associated problems were faced. Some are like hard to split and aggregation end result, synchronization and co-ordination problem, timing issues, deadlock, bounded bandwidth. Some other common struggle are resource sharing and effective way of CPU resource utilization across system, allowing correlated concurrent access and modification without consequence, maintaining transparency to project collective machines as single whole image, making layers of abstraction to hide the complexity and functional details, reliable service supporting anytime anywhere access to data, ensuring portability across different heterogeneous operating system and hardware, achieving high throughput available service, scaling up and down with data and load with requisite, provisioning fault tolerance, redundant and recovery services at all time. To address most of these disrupt, many Apache Hadoop subprojects were incorporated.