Why Data Management With Mapreduce Is the Most Stable Solution Currently Available
Data management is a challenging area of the modern computing environment. The average computing system handles extremely large amounts of data. As the amounts of data on a computer network increase, managing them and executing data related tasks becomes more of a challenge. This is similar to how users of computers with large memory capacities need a search tool to find data within their storage. This has led the computing world to develop many data management architectures to address these issues. None of the situations that are currently available reaches close to the stability of MapReduce.
What is it?
MapReduce is a data management architecture that combines distributed file system architectures with multiple parallel processing. Distributed file system architectures are what enable users on one computer to access data that are stored in other computers that are connected to the network. Multiple parallel processing is the ability for separate computers on the same network to undertake different segments of the same task separately.
Under this architecture, a task that is being performed on large sets of data is broken down into smaller jobs that are assigned to the computers that are on the network. There is a master node that undertakes this process which is called mapping. The master node assigns the smaller jobs and monitors the slave nodes as they perform their tasks. Once the slave nodes are done with their jobs, they send their answers to the master node. The master node then takes these answers and combines them to provide an answer to the main task that was being addressed. This part of the process is called reduce. The user is able to access this architecture mainly through the use of Hadoop applications that are designed for this purpose.
Why is this system the most stable?
The MapReduce architecture is quite stable because of the way that the master node interacts with the slave nodes. It monitors their performance on a regular schedule to ensure that they are performing their tasks. If a slave node does not send its expected signal for longer than the usual period of time, the master node considers this node to have crashed and, therefore, reassigns the task to another node. As a result of this, the Hadoop applications [http://www.mapreduce.org/news-events.php] that are used on the consumer end are not inconvenienced by any instability in any of nodes that form the data system, thus ensuring a stable performance.
What is it?
MapReduce is a data management architecture that combines distributed file system architectures with multiple parallel processing. Distributed file system architectures are what enable users on one computer to access data that are stored in other computers that are connected to the network. Multiple parallel processing is the ability for separate computers on the same network to undertake different segments of the same task separately.
Under this architecture, a task that is being performed on large sets of data is broken down into smaller jobs that are assigned to the computers that are on the network. There is a master node that undertakes this process which is called mapping. The master node assigns the smaller jobs and monitors the slave nodes as they perform their tasks. Once the slave nodes are done with their jobs, they send their answers to the master node. The master node then takes these answers and combines them to provide an answer to the main task that was being addressed. This part of the process is called reduce. The user is able to access this architecture mainly through the use of Hadoop applications that are designed for this purpose.
Why is this system the most stable?
The MapReduce architecture is quite stable because of the way that the master node interacts with the slave nodes. It monitors their performance on a regular schedule to ensure that they are performing their tasks. If a slave node does not send its expected signal for longer than the usual period of time, the master node considers this node to have crashed and, therefore, reassigns the task to another node. As a result of this, the Hadoop applications [http://www.mapreduce.org/news-events.php] that are used on the consumer end are not inconvenienced by any instability in any of nodes that form the data system, thus ensuring a stable performance.
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