Parallelization Vs Distributed Cluster Computing
Bringing together ideas and past experience as a guide, the parallel computing programming model was designed to scale up from single computing CPU to millenary of CPUs. Parallelization imports multiple processors or CPU’s in a one machine to perform calculations or simulations with many processors. Such supercomputers are designed to perform parallel computation where these system do not essentially have shared memory.
Example: Consider standard disk capacity as 1 terabytes and approximately 100MB/s of transfer speed. Then the time taken for reading whole disk would be 3 hours. There are some limiting factors like network bandwidth at peak and normal time, restriction to the number of processor chips based on application. Due to those factors, escalation in processing time may or may not be beneficial at all time. Based on thought-provoking and interesting research, employing multiple processors to solve problem came into picture. These issue was unfolded by fragmenting job into small pieces of sub task and assigned to each one of processor. Let they are 100 drives holding major share of data each. By accomplishing the task parallel, same 1 terabytes of data was read in approximately 2 minutes. Such commendable effort have sharpen and broaden our understanding in data transfer. Computation power in parallel processing have proven to the better choice than sequential processing.
Parallel computing have washed out the traditional system to fulfil need of common infrastructure. It has scaled down comparatively stress and misery of time-consuming from the most frustratingly and volatile environment. Parallel computing despite have drawbacks like portability, design complexity, scalability, parallel slowdown, race conditions, difficult to handle data/resource dependencies etc. By continuous learning, they have leveraged technology to build community for refactoring and burying the hurdles in current parallelism model. Meanwhile, distributed computing gave birth to a verdant future beyond imagination, bursting forth into unexpected all-purpose usable architect.
Parallelization -> Many processors or CPU’s in a single machine
Distributed computing -> Many computers linked through a network
Distributed computing originated from the business world. In the distributed system, same data are accessed and modified by many process and applications at the same time. Some of characteristic like concurrency, consistency, reliability, scalability, availability and fault tolerance need to be taken care by systems involved in distributed computing work area. As the data growth increase every now and then, the chances of getting a single physical machine saturated with maximum allowed storage capacity. Need for partitioning large set of data across multiple individual nodes in the connected network have been elevated greatly. Such type of file system managing data storage crosswise arrangement of system in network is generally known as a Distributed File System (DFS). IO channels constraint becoming barrier in development prosperity still today. DFS break out battle conflict in repository by logically associating group of multiple small machines to execute together and behave as a single massive machine. They were also designed to achieve parallel computing for faster results. Recent distributed computing technology like Hadoop, Hbase, Spark etc. are skillful in handling all types of network and node failure. Gracefully managing single node fault and moving ahead towards completion of task. Big no was shown for many instances of single point of failure.
Distributed computing have faced some of difficulties while handling enormous quantity of different variety of data. Threats are encountered for storing data persistently, node/network and disk failure, network bottleneck delay and few other associated problems. Chances of failover increases highly on managing many pieces of hardware together. Somehow data after processing need to be integrated. Data read from one disk may want to be coupled with its related data present in other disk. It is laborious task to combine final data after analysis.
To the Rescue
At the forefront of current boom in the trade of data infrastructure services, Apache Hadoop wins the crown. Apache Hadoop framework is implemented for running applications on large cluster connecting various commodity hardware. There is a common way to avoid the data loss. Using replication concept, the redundant copies of the same data are preserved by the system. In case of the failure, these another additional copy can be made available for handling breakdown without any consequences. Such implementation was done in Hadoop major component called Hadoop Distributed File System (HDFS). All storage troublesome were managed with HDFS. Network associated problems was resolved using Apache Hadoop another main programming model known as MapReduce (MR). Moving arithmetic and processing within close range of data. Moreover, data shifting is economical rather than moving data. MapReduce is an all-powerful tool designed for better and rooted examination of huge data sets. Hive is most broadly utilized data access technology. Hadoop and Hive have been a solid bed rock in transformation world. New data processing high tech mechanism are unearthed to bring up the enhancement in data operation and asset security. Though various big data industrial science has step up rapidly, Hadoop and Hive continued to be base nucleus in the data processing toolkit. Portability across composite software and hardware domain, have promoted Hadoop as popularly adopted technology among other set of applications.